Standardized model for process chain assessment of cost, time and environmental impact

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Facoltà di Ingegneria

Laurea Magistrale in Ingegneria Gestionale

Tesi di laurea

Standardized model for process chain assessment of

cost, time and environmental impact

CANDIDATO

RELATORE

Erica Campolongo Prof. Gino Dini

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Abstract

Thesis topic: Development of a Standard Process Chain Model

Author: Erica Campolongo

Supervisor: Dipl.-Ing. Stefan Jacob

This research presents the existing methods utilized for the description of process chains, and point out their limit, that is the lack of standardization. The main problem related to the absence of generic models is the time wasted in the creation of a tailored process chain description for every new product introduced into the market. The aim of the work is the introduction of a generic model that can be used for mapping any manufacturing system. Another innovative element of this work is the importance given to the assessment not only of cost and time, but also of energy consumption and en-vironmental impact related to the manufacturing activities. Those two aspects, gener-ally not included in the existing models, have been introduced into the chain description because of the growing importance recently given to the reduction of the environmental impact of production systems. The developed model enables the evaluation of different solutions for the production of the same item, so as the user can choose the best op-tion, according to his priorities (minimize the energy consumpop-tion, the machining time, the quality, etc.). The chain is described as a sum of modules, which contain the de-scription of a specific manufacturing process (milling, turning, etc.).

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Statement

I hereby certify that I have produced the present draft without any external help and I have not used any other materials a part from the ones mentioned in the references.

Hannover, 28.02.2018

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List of Figures

Figure 2- 1: Possible layout of MS ... 4

Figure 2- 2:Material and energy input and output ... 6

Figure 2- 3: General method for the formulation of energy KPIs ... 7

Figure 2- 4: Local benchmarking methodology ... 10

Figure 2- 5: Ishikawa process chain representation ... 17

Figure 2- 6: IDEF0 process representation ... 17

Figure 2- 7: Two dimensional approach for process chain modeling ... 18

Figure 2- 8: Interdependency matrix [HOAN17]... 20

Figure 2- 9: Neuron input and output ... 22

Figure 2- 10: Process Interfaces representation [DENK11] ... 25

Figure 3- 1: Conventional and additive manufacturing... 35

Figure 3- 2: Examples of turning operations [SCHR00/414] ... 37

Figure 3- 3 Examples of turning operations [SCHR00/524] ... 38

Figure 3- 4: AM-From a chain to a single process ... 50

Figure 3- 5:Power bed fusion technology [SCHM17] ... 54

Figure 4- 1:Process chain model structure ... 64

Figure 4- 2: Data sources ... 65

Figure 4- 3: Process behavior description ... 66

Figure 4- 4: Example of a model of chain with two processes ... 67

Figure 4- 5: User interface... 70

Figure 4- 6: Data entering-Tree structure ... 72

Figure 4- 7: Brain-Process Model Interaction ... 72

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Figure 5- 2: Manufacturing cost and time for one product ... 81

Figure 5- 3: Energy consumption analysis. ... 82

Figure 5- 4: Trend of cost and SEC for different cutting speed ... 84

Figure 5- 5: Trend of cost and energy consumption for different layer thickness ... 87

Figure 5- 6: Trend or cost and energy consumption for different scan speed ... 88

Figure 5- 7: Trend of cost and energy consumption for different wall thickness ... 90

Figure 6- 1: Cycle time trend related to volume variation ... 97

Figure 6- 2: Cost trend related to volume variation ... 97

Figure 6- 3: AM technologies vs Injection moulding ... 98

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List of Tables

Table 2- 1: Classification of the models in literature ... 29

Table 3- 1: AM-Advantages and disadvantages ... 52

Table 4- 1: Example of a DB for machining tools ... 69

Table 4- 2: Example of a correlation matrix for milling ... 75

Table 4- 3: Correlation matrix for AM and CM processes ... 76

Table 5- 1: Overall chain performances... 81

Table 5- 2:Cutting speed sensitivity analysis ... 83

Table 5- 3:Feed rate sensitivity analysis ... 84

Table 5- 4: Layer thickness sensitivity analysis ... 86

Table 5- 5: Scan speed sensitivity analysis ... 88

Table 5- 6: Sensitivity analysis for wall thickness ... 89

Table 6- 1: Description the chains object of the analysis ... 93

Table 6- 2: Volume variation in the final product ... 94

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List of Abbreviation

Symbols Description Unit

AI Artificial Intelligence

AM Additive Manufacturing

ANN Artificial Neural Network

CM Conventional Manufacturing

CMM Capacity Maturity Models CNC Computer Numerical Control

DB Database

DoE Design of Experiments

DSM Design Structure Matrix

EBM Electron Beam Melting

GWP Global Warming Potential KPI Key Performance Indicator

LBM Laser Beam Melting

LCA Life Cycle Assessment

LEI Lean Energy Indicator

MRR Material Removal Rate

MS Manufacturing System

SEC Specific Energy Consumption

SVM Support Vector Machine

TBS Technical Building Service

UI User Interface

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1 Introduction

Manufacturing accounts for over 30% of global CO2 emissions and energy

consump-tion. However, there is a lack of awareness of manufacturing companies for energy consumption in machining operations. Most of the indicators developed and utilized in the plants to monitor the production are related to cost and time. Nowadays, manufac-turing sustainability is more and more encouraged by government regulations and in-centives. This led to the growing of the number of researches about energy indicators and monitoring energy tools.

This master thesis work introduces a method for the description of manufacturing sys-tem that includes the assessment of the energy consumption along with the cost re-lated to the emissions into the atmosphere. The aim of the work is giving a tool to the production managers that allows understanding how the process parameters choices influence the performances of the production chains. In other words, the developed tool can be used to evaluate the monetary and energy performances of different chain for manufacturing the same item. Moreover, once that the user has selected the chain that best suit his goals, the tool can be used for making some comparison between the performance of the same chain but with different process parameters, such as cutting speed, scan speed, etc.

The second chapter of this work provides a general understanding of the energy con-sumption of machine tools. In particular, the investigation aims at finding which com-ponents of the equipment absorb the most energy and how energy waste can be avoided with the use of energy KPIs. The chapter also includes a review of the methods more adopted in literature for the process chain mapping and for build indicators for measuring the process energy consumption. In the third chapter, a description of the main CM and AM processes is presented. In particular, every process is described in a very general way with the use of equations for the assessment of energy, cost, and time. Each process represents an independent module of the chain. In the fourth chap-ter, the structure of the developed model is introduced, along with its main components and their interactions. In the fifth chapter, the model is tested for the assessment of the machining time, the cost, the energy consumption and the pollution effects. The object selected for the assessment phase has a simple shape, in order to simply the model evaluation. Moreover, in this chapter, apart from the evaluation, it has been executed

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another analysis. In fact, the model is applied to study the influence of the process parameters on the overall performances. In order to do that, a sensitivity analysis is executed for the process parameters that can be controlled and changed by the users. The aim of this further analysis is understanding on which process parameters the user should focus for improving the overall chain performances. Finally, in the last chapter, it is possible to find a comparison between two chains, one with only CM processes and one with both CM and AM processes, for the production of the same item. This comparison is executed more than one time, for different size of the final product. The aim of this analysis is understanding how much the size of the item influences the user choice regarding the more suitable process chain for a given product.

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2 Literature review

Description of Manufacturing System

A manufacturing system (MS) consists of a set of workstations where operations are executed through equipment and human resources with the aim of manufacturing a final product (output) from raw materials and parts (input). In literature, the main pa-rameters used for the characterization of a MS are the type of operations, the number of workstations, the flexibility and the level of automation.

It is possible to classify MSs on the base of the productive process. There exist three different MSs, that is flow-shop, job shop and group technology.

 Flow-shop: raw materials and parts move from one station to another, following a standard and predetermined sequence. In each station is executed a process, such as milling, turning, grinding, etc. An example of this kind of system is the assembly chain, where at each station corresponds an assembly operation. The productive cycle is the same for all the products of the batch and does not change from one batch to another one (fix routing).

 Job-shop: like in the flow-shop, the operations needed to transform the input into the output are predetermined and are the same for the items that belongs to the same batch. However, the process sequence change for the different batches (variable routing).

 Group technology: the realization of the products that belong to the same family takes place in a dedicated area of the plant, where there is the necessary equip-ment (variable routing). The products of the same family are similar but not iden-tical.

In the figure 2-1, each process is represented inside a rectangular shape. The arrows that connect the processes are the symbol of the technologies interfaces, which are the parameters that are the results of a process and have influence on the next one [GROO11].

In the modern MSs, most of the operations are accomplished by machines according to a part program. It is possible to make the following distinction between the different levels of automation:

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 Manually operated machine: a human operator controls and supervises the op-erations;

 Semi-automated machine: the human execute a portion of the controlling phase and the remaining is executed by the machine program control;

 Fully automated machine: the machine can operate without the presence of a human operator during a period longer than one work cycle.

Workstation 1 Workstation 2 Workstation 3 Workstation 4

Workstation a Workstation c Workstation e

Workstation d

Workstation b Workstation f

input output

Figure 2- 1: Possible layout of MS

The flexibility refers to the number of different products that a MS can manufactures. In particular, the outputs of a MS can be all identical (rigid MS), or different and manu-factured in different batches after a set-up (semi-flexible), or different but manumanu-factured without the needs of stopping the production for setting the equipment (flexible MS) [GROO08].

There exist many way to manufacture a product. The phase in which managers take decisions about how to transform raw materials in final products is called process plan-ning. Process planning consists in the selection of manufacturing processes and the determination of their sequences to transform a designed product in a physical one. It includes activities such as selection of machining operations, machine tools, cutting

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tools, calculation of cost and production time. It is possible to plan the production ac-cording to different targets: cost, energy use, time, quality, etc. Energy targets are more and more frequently included in this decision.

For many products the manufacturing processes are quite complex. Consequently, it is necessary a simplification of those systems, in order to make an accurate analysis. Process chain models help reduce the complexity of the MSs through a decomposition in smaller elements (processes, sub-processes and activities) and a representation of their interactions [GROO08].

Energy consumption of machine tools

2.1.1 General understanding (KPI, CO2)

Recently, great consideration is given to ecological aspects of manufacturing systems, due to the growing awareness of the huge impact that production systems have on global sustainability. Pursuing efficiency in existing manufacturing processes can re-duce that impact.

Manufacturing accounts for one third of global CO2 emissions and energy consump-tion. UK Government reported that global energy demand will be more than 50% higher in 2030 than in 2006, and the related greenhouse gas emissions will be approximately 55% higher [DEPA07]. The industrial community needs to tackle this problem, studying both the manufacturing systems and the raw material production. There exists many means to reduce the energy and material demand in manufacturing, such as, longer-lasting products; modularization and remanufacturing; component re-use; designing products with less material consumption [ALLW11].

The pursuing of energy efficiency is not more a company choice but is an answer to the increasing scarcity of resources and the pressure from international environmental regulation, such as the European 2020 Strategy targets [MAY15].

From an environmental point of view, it is possible to map the input and the output of a process, as shown in the figure 2-2.

Even if in the last few years industrial people understood the role that manufacturing system plays in the context of climate change and resource depletion, no methods are implemented in the production plants to measure and monitoring the energy use.

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Therefore, there is low awareness for energy consumption in manufacturing pro-cesses. Most of the researches about the reduction of the impact of production sys-tems were only conducted in an academic environment, and few of them are used to measuring/predicting/reducing the energy consumption in manufacturing processes [OWOD16].

Process

Raw material Energy Parts Final product Heat Waste

Figure 2- 2:Material and energy input and output

E.W.T. Ngay et al. designed an energy and utility resource management maturity model that allows the assessment of the maturity level of energy and utility manage-ment, and guides the organizations to improve energy management. The assessment activities provide feedback for the continuous improvement. That research is based on the capacity maturity models (CMM), which describes organization maturity through different maturity levels (from “initial” to “optimizing”) on the bases of key process area performance. The assessment framework introduced in the paper can be used by any organization that currently does not have any energy management practices, in order to achieve the higher level of maturity. That level is characterized by the presence of environmental performance improvement objectives, monitoring systems for the iden-tification of process variation, and practices for the resolution of identified defects that negatively influence the energy performance.

Applications of that framework showed an improvement in the energy consumption per product. Moreover, energy and utility management practices help the managers in the phase of production planning, reducing their workload and enhancing their efficiency. An enabling factor for the application of that methodology is the capability of collecting energy data [NGAY13].

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In order to be aware of the energy consumption of a plant or a single machine, energy KPI are needed. Conventionally, every plant measures and monitors indicators related to cost, lead-time and product quality. From a green point of view, measurement sys-tems should integrate energy KPI, in order to set energy targets and to assess energy improvements. Nowadays, factories are able to provide data only about the energy consumption per year or per unit manufactured. Nevertheless, these aggregated data cannot be used to reduce energy waste in manufacturing and to understand the causes of energy inefficiency. G. May et al. developed a general method that allows compa-nies to build firm-tailored energy related KPIs. As shown in the figure 2-3, the first step of the methodology is the description of both the production system (layout, level of automatization, etc.) and the single resources (CNC machine, conveyor, etc.). Then, for each machine the power requirement at each stages (cycling, set-up, idle, etc.) is calculated, along with the variables that affect the energy consumption. It is interesting the analysis of the energy consumption of the single machine compared to the whole energy consumption. It is also possible studying the energy use in each stage of the machine and comparing it with the total energy demand per product. A critical activity is detecting the relation between the effect (energy waste) and the manufacture varia-ble that are the causes of it. For that purpose, the author proposes the use of an Ishi-kawa diagram. After the preliminary analysis, several indicators are introduced in the paper so as it is possible to have a framework of the actual efficiency of a plant and of its machines. Their combination results in an aggregated indicator, called in the study “Lean Energy Indicator”, which is obtained as the ratio of the energy used to produce valuable output and the total energy consumption. The paper provides also guidelines to implement and use those indicators [MAY15].

Figure 2- 3: General method for the formulation of energy KPIs

M. Benedetti et al. introduced another methodology for the development, the imple-mentation and the use of Energy KPI. In this case, the research is valid in an electrical

Description of production system and resources Power requirement of each machine in each stage Calculation of energy KPI Aggregation of the energy KPI in

the Lean Energy Indicator (LEI)

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consumption context, but with some adjustments, it can be used for other energy re-sources. The analysis dedicates more attention to the control system rather than to the development of energy KPI. The measurement and control system is based on the definition of responsibility centers, areas whose performances are under the responsi-bility of one single person and whose costs are independent from other areas. The Energy KPI used in the study are extracted by the International Standard ISO 50006:2014. The key concept of this approach is the definition of the baseline for each indicator, in order to detect as soon as possible every deviation in the value of the KPI over the time. In the paper three different performance control tool are proposed: the performance control matrix, percent variation control chart (for strategic level) and CuSum chart (suitable for all the hierarchical levels). Those instruments allow the iden-tification of changes in energy performance, so as it is possible to act to bring the system back to the standard operational condition [BENE16].

Even the international standard ISO 50001 prescribes the necessity of the data gath-ering, the determination of energy performance indicator, the evaluation of energy per-formance and other activities. The growing of the companies interest in energy man-agement is proved by the fact that the number of certifications for ISO 50001 (a global standard for energy management developed by the International Organization for Standardization in 2011) grew to roughly 12 000 in 2015, 85% of which were in Europe. Another interesting data is about the economic impact of the energy management. Companies that implement ISO 50001 or similar standards can achieve annual energy and financial savings of over 10% [PUBL17].

Energy consumption in the manufacturing line can be analyses also through a local benchmarking. Hoda A. ElMaraghy et al. proposed a six steps method for local bench-marking, which allows the identification of the main contributors to energy consump-tion, so as the energy management efforts can be oriented to them. In other words, benchmarking generates targets and provides an estimation of the potential improve-ment in energy consumption of different manufacturing plants. That methodology is valid only for discrete product industries and it can be applied within individual plants considering the existing working condition and operating factors (equipment technol-ogy and age, plant temperature, process plans, cutting conditions, etc.). Moreover, the author studies only the CNC machines, not all the equipment of the production line,

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due to the fact that the CNC machine are the most energy consuming equipment in a manufacturing line [ELMA16].

As shown in the figure 2-4, the first step of the proposed methodology is the data col-lection, which is a very important prerequisite in energy management. Energy meas-urement devices collect energy data over a predetermined time frame. Those data are related to the equipment states (cycling, blocked, under repair, etc.). The author un-derlines the fact that the average energy consumption of the CNC machines during the different states are similar to each other, and this is something that should be changed. In the second step, the equipment cycles are identified. A machine cycle is composed by all the cycling and no-cycling activities (set-up, under repair, blocked, starved, etc.) executed by a machine to manufacture a single product. After the detection of the equipment cycle, in the third step the time cycle and the minimum, maximum and av-erage consumption are calculated for each machine. Those data are the input to the step four, where the Energy Use Local Benchmarking is calculated for each machine per product realized. This data represent the ideal minimum energy consumption for each machine. The fifth and sixth steps consist of the calculation of average energy consumption per product for each machine and of the waste energy; and of the calcu-lation of the Energy Efficiency as the ratio of average energy consumption and total energy used by the machine [ELMA16].

In order to be aware of energy consumption, researches have focused not only the definition and measurement of energy KPIs, but also in the development of prediction models. O. Owodunni investigated the energy consumption during machining using three phase power meters. The result of the research is a model that can predict the Specific Energy Consumption (SEC) from cutting parameters. SEC is the energy re-quired by the machine tool for removing 1 cm3_{of material. The benefits of this quantity}

is that it enables the comparison between different machining processes. For milling operation the author find out that SEC decreases with increase in material processing rate. That result has been confirmed also through experiments for turning and AM [OWOD16].

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Figure 2- 4: Local benchmarking methodology

S. Kara and W. Li proposed another energy consumption prediction model for milling and turning operations. It is an empirical model and it is the result of experiments exe-cuted in eight different milling and turning machines. The experiments are set with the use of DoE methods. Like in the model above, SEC is used to express the energy consumption. The main outcome of the study is that higher MRR (Material Removal Rate) results less energy consumption of removing same volume of material. That brings also to an increase in productivity because less time is needed to machining products [KARA11].

Another subject of studying is the relation between the improvement of the plant energy efficiency and the costs of this change. This aspect have received until now less atten-tion. Decreasing the environmental impact results in an increase of the costs. However, a case study in the German automotive industry showed that by optimizing the energy mix, the CO2 emissions of the supply chain could be reduced by 30% at almost zero variable cost increase [TOGN15]. The cost of the change is one of the barriers to the

STEP 1: Processing raw data and identification of the

typical cycles

STEP 2:

Calculation of the cycle time and machine

states

STEP 3: Calculation of the average, min and max

energy consumption

STEP 4: Calculation of the Energy Use Local Benchmarking

STEP 5: Calculation of the

energy average consumption per product and energy

waste

STEP 6: Calculation of the Energy Efficiency

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green production. The literature ranks the barriers to the environmental efficiency into three classes: economical, institutional and organizational [MAY15].

A part from the quantification of energy efficiency, it is important to increase the aware-ness in energy consumption inside a company in all the hierarchical levels. This is a necessary step for breaking down the organizational barrier. In order to achieve this objective, H. Hopf and E. Müller proposed the use of Energy Cards, which make visible the energy consumption for all the workers, even if for the one that are not expert in it. According to the authors, the two main obstacle to energy efficiency are the lack of information about the energy consumption of each machine in the different stages, and the poor communication about energy consumption inside the plant. In fact, it is crucial that everybody has access to equipment energy data so as they can act during the execution of their tasks in case of energy KPI deviation from the standard value. This is coherent with the Plan-Do-Check-Act methodology, which is at the base of all the management system, included the energy management system [HOPF15].

2.1.2 Energy consumption in cutting operations and AM

Machining is the most energy intensive production activity, and CNC machines are the ones that require the major quantity of energy. Machining impacts on the environment in three different ways: consume of energy, production of waste material, and release of pollutants. Energy consumption in cutting operation depends on the workpiece and tool material and on the interaction between them. Energy is necessary not only for the cutting activity. In fact, there are many other activities that consume energy, such as cleaning operation, tool preparation, fluid deposition, control systems, etc. In other words, a CNC machine consumes energy also when no cutting operation are exe-cuted. As a matter of fact, the material removal process absorbs around the 25% of the machine total energy consumption. This is one of the aspects that should be im-proved in order to save energy.

S.T.Newman et al. made some experiments to assess the energy consumption of in-terchangeable machining processes for finish and semi-finish cutting of aluminium. The result of those experiments is a considerably difference in energy consumption for two interchangeable cutting processes. This proves that it is possible to improve the

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energy use in cutting processes, acting on the machining parameters. Another evi-dence from the experiments is that specific energy in semi-finish is smaller than the one calculated for finish cutting [NEWM12].

During an energy use analysis for a CNC machine, it is possible to choose to conduct the study looking at the whole machine or looking at its components separately. In order to assess the energy use for a cutting operation independently from the machine tool, only the energy used for the cutting process has to be included in the analysis, which depends only on workpiece properties and process conditions . Setting the cut-ting process parameters with the aim of minimizing the energy consumption allows the achievement of notable energy savings. In order to find the optimal parameters, it is necessary the formulation and solution of an optimization problem [ZHAO17].

A part from process parameters optimization, G.Y. Zhao et al. list different approach to the reduction of the energy use, such as acceleration-deceleration approach, task-scheduling approach, optimization of machine tool's energy components, and improve-ment of peripheral equipimprove-ment. M. Mori et al developed a new accelerator/decelerator control system to reduce the power absorption. Moreover, they formulated a model for the power consumption of a machine tool during normal operations, including position-ing, spindle acceleration, tool change, machinposition-ing, returning the spindle to the tool ex-change position after machining, and stopping the spindle. Other energy savings come from the shortening of the cycle time and the shutting down of unnecessary devices during non-machining stages, such as set-up and starving stages. However, if the ob-jective is the energy efficiency at the machine level, it is important to take into account not only the spindle system, which is the most energy intensive system in a CNC ma-chine, but also others. Besides the optimal setting of the cutting parameters, G.Y. Zhao et al. suggest to shorten the pre-cutting and post-cutting operations in order to achieve better energy performances [MORI11].

CNC machine are very complicated and consists of several components, such as cool-ing system, control system, lubrication system, hydraulic system and others. Each component absorbs energy, but in different way. In fact, the spindle and the servomotor systems need of energy depend on the cutting resistance, and therefore it is variable. On the contrary, the energy demand of control, hydraulic and lubrication systems are constant. W. Li et al. classified energy required by CNC machine into five categories:

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servo drives, control system, hydraulic system, cooling lubrication system, and auxil-iary system. Between them, hydraulic and lubrication systems consume the most of the total energy. Consequently, the study suggests focusing on hydraulic, lubrication and cutting systems when the aim is the reduction of energy use [LI11].

Scheduling the job taking into account environmental impacts and optimizing the pro-cess parameters are only some of the means that bring factories toward the eco-pro-duction. In the mentioned approaches, the goal is to improve energy performance with-out any changes in the production system (same machines, same products, and same machining process). However, there exist other possibilities that allow improvement but imply new investments. Machine tools manufactures until now projected and real-ized equipment that satisfy cost, quality and time requirements. They did not take into account any environmental impact that machines have on the atmosphere and they did not introduce into the machines measurement devices that help the data collection process. It is important to think about the need of achieve energy efficiency also for the processes that come before the manufacturing, such as the realization of manufactur-ing machines.

Another mean to improve the actual level of energy efficiency is the use of new tech-nologies. In literature, many studies assess the possibility of the use of additive man-ufacturing for industrial applications as a mean to improve material efficiency. This technology was initially used only to manufacture prototypes, but it was extended also to the production of whole products in the last years. The main advantage of that tech-nology is the decrease of wastes. In fact, with AM the raw material needed for the production is considerably lower, due to a low buy-to-fly-ratio (ratio between the raw material used for a component and the weight of the component itself) and to the pos-sibility of recycling the non-melted powder. Moreover, there are saving related to the use of fluid, which is utilized in great quantities in machining operations. B. Berman looks at AM as a disruptive technology that will lead to a new industrial revolution, because it gives the possibility of produce small quantity of customized goods at rela-tively low costs. Moreover, AM is also a good solution when the geometrical complexity of the item is high. On the other hand, even if it is true that additive process allows the reduction of material waste, it also true that the manufacturing of metal powder needs a great amount of energy [BERM12].

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In the context of green production, an assessment of the environmental impact of AM it is needed, along with a comparative analysis with the traditional manufacturing tech-niques. After those analyses, it is possible to understand the potential of AM as a new paradigm for eco-productive systems. H. Paris et al. investigate the trade-off between additive and subtractive process using LCA methodology and appropriate eco-criteria for the comparison of their environmental impacts [PARI16]. Both the production tech-niques are used for the production of a titanium aerospace turbine. The results prove that EBM (Electron Beam Melting) is better than traditional machining when the com-plexity of the geometry is high and requires strong material removal with subtractive technique. In particular, what makes EBM more sustainable is not the energy con-sumption but the lower material waste. Those results make the AM technique very interesting for the aerospace sector, where the buy-to-fly-ratio is 10 to 1, that means that only 10 percent of the original material remains in the final part [KOBR06].

P. C. Priarone and G. Ingarao conduct another comparative study that investigates two manufacturing approaches: the use of AM plus finishing operation and the only use of machining technologies [PRIA17]. The authors proposed a graphic method for the se-lection of the environmental approaches based on primary energy demand and CO2 emissions, which are both represented in two different three-dimensional planes. For the points in the plane, the two different approaches are equivalent in term of CO2 emissions and energy demand. As in other several studies, the LCA method is used to take into account the product interaction with the environment. The difference be-tween this research and the one mentioned above is that here it is considered also the possibility of weight reduction through the topological optimization. In fact, one of the benefits of additive processes is the possibility of redesign the products in order to reduce their weight, while ensuring the same functionalities. That aspect is of consid-erably importance in the field of aerospace, in particular during the use phase. Yunlong Tang et al. conducted a study that demonstrated the benefits of topological optimiza-tion. In particular, they confronted CNC and binder-jetting process for the manufactur-ing of an engine bracket. The design of the item realized by AM is obtained through a design optimization method introduced in the paper [YUNL16].

In comparing two manufacturing technologies, quality surface has to be taken into ac-count, aside from costs and energy use. The main drawback of AM is the poor quality surface. Consequently, finishing machining operation are often needed. However, AM

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represents a notable instrument to improve energy and material efficiency in manufac-turing. As mentioned before, many studies are conducted to provide methodologies that guide in the choice of different manufacturing techniques. In order to do a com-parison it is necessary a model for the representation of production chain, so as it is possible an analysis of time, cost and energy related to each process.

Modeling a process chain

2.1.3 Reason to model a process chain

Process chain models are the input to the production planning because they allow the analysis of each process of the chain and of the dependencies between them. Under-standing the behavior of the processes and how they influence each other is the first step for the optimization of a manufacturing chain. Optimizing means setting the values of process parameters so as it is possible to achieve one or more objectives (single or multi-objectives problem). In doing that, some constrains should be taken into account because the optimal solution must be feasible. For example, a production plan is fea-sible if it does not exceed the capacity of the machines. Moreover, after the planning stage, models are useful to monitor and control the manufacturing process. In fact, once that the production is scheduled, it is important monitoring the production line though KPIs, in order to relieve any deviation from the implemented plan.

A model is useful not only when the goal is the improvement of the performances of an existing process chain, but also when we have to manufacture a new product. In fact, there exist many different ways to manufacture an item and it is important to figure out which one is the best to reach predetermined objectives (mitigation of the risk, minimization of cost and/or time and/or resources, etc.). Consequently, each alterna-tive is analyzed developing a model, and then through simulations, which allow a dy-namical analysis of the process chain.

Furthermore, a model is a very useful instrument for an easier and more effective com-munication inside a company. Even if top managers or workers have not knowledge about a manufacturing process, with a representation of it, it is simpler to make them understand how the production cycle works. This is truer when the processes are com-plex.

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In addition, a MS model is the input for the automation of processes. As a matter of fact, to shift from manual execution to automatic execution it is essential a model of the processes, in order to study and analyze them. In the same way, the analysis of a process chain is useful to take decisions about system reconfiguration.

In this study, a model for manufacture process chains is developed with the aim of introduce a method for optimizing a chain, taking into account the manufacturing envi-ronmental impact as well as the costs. The model wants to provide a flexible represen-tation of process chains. In particular, the research focuses on the standardization of the process interfaces.

In order to achieve the goal, the literature is used to consider both existing process chain models and the developed approaches for the green optimization of manufac-turing processes.

2.1.4 Existing process chain models

In order to improve energy efficiency of MSs, models are necessary to evaluate the actual energy consumption, to identify the most critical resources and to define action plans. The most common way to represent a process chain and its interfaces is through a series of blocks connected by narrows. Each block is a process and the narrows are the symbol for the input and the output of a process. For the execution of a process, it is necessary the presence in the workstation of a machine, a human operator, input material and a procedure with indications about how to operate in the station (Ishikawa- 4M). In a chain, the output of a process is the input for the next one, so there are interdependencies between them (see figure 2-5).

There exist different way to model a process chain. A distinction between them can be done according to the approach used (mathematical method, simulation method, ma-chine learning, analytical method, etc.), the level of detail, the flexibility and also the understandability (or comprehension). In the following section different approach to process chain modeled are illustrated and compared in a final table. One of the most known approach to process representation is IDEF0 (Integration Definition for Function Modeling). It prescribes a top-down approach, from the plant, to the processes, the sub-processes and the activities. It allows the simplification of the MSs analysis.

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Figure 2- 5: Ishikawa process chain representation

IDEF0 is a graphic modeling language for the representation of processes and activi-ties, their interactions and system data and information flows. A “box and arrow” nota-tion is used to map the interfaces, the resources and the constraints of every process (see figure 2-6).

Figure 2- 6: IDEF0 process representation

Mary Kathryn Thompson et al. introduce a new two-dimensional graphical approach to model a chain. Instead of represent all the processes in a single line, they suggest the utilization of two lines, each one contains the processes necessary for the production of one artifact. The authors aim at a model that considers the role of AM technologies in process chains. In fact, AM can be used to realize tools (Rapid Tooling), patterns or other artifacts that are necessary for the production of a product. The model allows the

Process A

Input

Output/

input

Process B

Output

Process

re so u rce s input output co n st ra in ts

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analysis of how those artifacts are manufactured and when they enter in the main pro-cess chain. The output of a propro-cess can be either a work-in-progress, which needs to be further processed in the next process of the chain, or a finish product, which does not need to be processed afterwards. As shown in the figure 2-7, that finish product can be or the product required by the customer (process chain 2) or an artifact neces-sary in another process chain (process chain 1). In the model proposed in the paper, a vertical narrow indicates the passage of the artifact from one chain to another. The advantages of this representation is that it is possible to determinate when each artifact is needed and how they influence the overall manufacturing cycle. In fact, when two chains are compared, it is interesting to know how many artifacts are needed and in which processes, because they add complexity to the model. The drawback of that model is the specificity. In fact, the representation is valid only for a particular pro-cesses chain. Consequently, if something changes in the propro-cesses, the model is not valid anymore [THOM16].

Figure 2- 7: Two dimensional approach for process chain modeling

Leigh Smith and Peter Ball introduced a graphical method for mapping process, based on the IDEF0 approach. The aim of the study is the creation of a tool for tracing the material, energy and waste flows within MSs. That tool is particularly useful for the improvement of manufacturing resource efficiency. Those flows are mapped using IDEF0 modeling language in a bottom-up approach, from the single process to the whole system. The proposed method consists of nine steps:

1) Manufacturing system study;

Workstation 1 Workstation 2

Workstation 1

input output

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2) Elaboration of a first draft of process map using IDEF0;

3) Brainstorming for the identification of the material, energy and waste flows 4) Process map approval;

5) Data gathering for each flow; 6) Creation of a quantitative model;

7) Analysis of the MS using the model to identify targets for the improvement ac-tions;

8) Developing of an action plan for improve system efficiency; 9) Implement the plan

This model is very specific for the plant, because the starting point is the analysis of the manufacturing system with the help of people involved in the process manage-ment and maintenance. In fact, the brainstorming used for build the model involves the human resources that know accurately the plant and the processes. However, the methodology allows the identification of all the critical resources and it is a valid decision support tool for the management [SMIT12].

H.Wiemer et al proposed a method for the optimization of manufacturing chains that uses statistics. The first step of the methodology is the graphical representation of the process chain with material, machines and tools used in each operations as well as the parameters necessary for the behavioral analysis. Databases are created for the collection of the process data. Then, Design of Experiments (DoE) methods are applied to calculate the programs of experiments from the process description. In the second step, experiments are executed and the collected data are logged. In the third step, analytical algorithms determine the interaction between processes. Those algo-rithms use concepts such as correlation, level of sensitivity and process statistics [WIEM17].

DoE is “a procedure to plan and define the conditions for performing controlled ex-perimental trials”. According to the method, the first thing to do is the determination of the experiment aim and the quantitative method for measuring the output as well as the number of factors used in the experiment. We can explain the output using two or more factors. In the case of two-level factorial design, every input variable can have two value, high level and low level. For example, the quality surface of a forged part depends on the temperature of the material during the process and the pressure applied. If we change the parameters, it is interesting look at the system response.

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The method proposes the execution of sequential series of experiments. The first series is called “screening design”, and it is about the identification of the meaningful factors. The second is called “fully factorial design” and it concerns the modeling of the effects (how the system reacts at the input changing). The last series consists in the execution of experiments to validate the outcomes. Through experiments, we can understand which parameters have more influence on the output and which can be ignored. DoE are useful for this study because it allows the determination of which interfaces are important for the model and which are not. This help simplify the model and allows the analysis of the impact that changes in process parameter have in the whole system as well as in the other processes of the chain [GARU17].

Mapping process interface is the main requirement for the design of an adaptive MS able to react to environmental changes. In fact, the identification and analysis of the interdependencies within a MS allows the evaluation of the impact that modifications in resources and processes have on the system. If we want to change the value of a parameter of a particular resource, it is opportune an analysis of the impact of that change in the system as well as in the single processes. As shown in the figure 2-8, Xuan-Luu Hoang et al. studied the relations between processes and resources using an interdependency matrix.

Figure 2- 8: Interdependency matrix [HOAN17]

The elements of that matrix are product, process and resource parameters. The aim is the detection of interdependencies between processes as well as between processes

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and resources. In fact, modifications in process parameter influence the process re-sources requirement. For example, if we change the feed rate of a milling operation, the processing time will change. The concept of correlation is used to express those interdependencies. Moreover, in the matrix are included also the product parameters. That is because of the fact that, before changing something in a process, it is neces-sary take into consideration constrains related to the product requirements. For exam-ple, a change in cutting speed has influence in the product quality surface. Looking at the interdependency matrix, it is possible to understand all the interactions within the system. This is the main benefit of the model. However, it has the drawback of taking long time to be built and of being valid only for a particular chain and a determined product [HOAN17].

Another interesting study that uses statistics is the one conducted by Najibesadat Sa-dati et al. The authors propose an integrated approach for detecting the control param-eters and facilitating the process paramparam-eters design with the use of Response Surface Methodology (RSM). It is a method for modeling the performance of a system from data collected through structured experiments, which are disruptive (to conduct them it is necessary the interruption of the manufacturing activities) and vert costly. For this reason, the authors propose the application of that methodology with historical data, already available. The paper draws a distinction between all the possible parameters that can be used model a chain. In particular, they discriminate between control pa-rameters and noise (or uncontrollable) papa-rameters. The aim of the study is the creation of a model that allows the setting the value of control parameters in order to optimize the target performance and minimize the effect of the noise parameters. The procedure consists of four steps:

1) List of the potential set of parameters (control and noise); 2) Selection of possible set of control parameters;

3) Build a dual response surface model (one for the mean value and one for the variance) in the form:

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where Y is the performance target, g(x) includes only the control parameters (x), and f(x,z) express the effect of the noise parameters (z).

4) Check if the model meet the specifications. Otherwise, the steps 2 and 3 are iterate.

The second step makes use of variable filter algorithms as well as expert knowledge from staff and experts in the specific process. After the creation of the model in step 3, it is possible the calculation of the expressions for mean and variance. The aim is the minimization of the variance, with the constrain that the mean value should be equal to the target performance value. Solving this optimization problem, it is possible set the control parameter values for the achievement of the process target perfor-mance [SADA17].

In a dynamic context, artificial neural network (ANN) based approach are particularly useful for the modeling and the optimization of MSs. ANN is a computational model inspired by the architecture of human brain. Those networks consist of many pro-cessing elements or nodes (analogous to human neurons), which have many weighted inputs called connections. The figure 2-9 shows the structure of a neuron.

Figure 2- 9: Neuron input and output

All the input are summed in a net input that enters in a transfer function, which returns an output through the output connection of the artificial neuron. The output of the neu-ron j is:

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𝑌𝑗 = 𝑓(∑ 𝑤𝑗,𝑖× 𝑥𝑗,𝑖)

𝑖𝜖𝑖𝑛𝑝𝑢𝑡𝑠 (2.2)

Generally, the processing elements are grouped in a series of layers, from input layer to output layer. The input layer receives information from outside the network, the out-put layer transmits to the outside, and the intermediate levels receive and transmit information within the network. The neuron is activated only if the sum of the input exceeds the neuron threshold. The processing elements of the network learn from a set of examples. In fact, a neuron can change its weight as a response to the error between the output target and the output returned by the transfer function. The error is expressed as a function of the input weights. A part from changing the weights, the process element can change the threshold or create/eliminate connection links. The learning process is an iterative process. After every iteration, the error become smaller [BASH00].

For the modeling purpose, ANN can model the relation between input and output if the network is trained with input-output data from experiments or from observations. That is particularly useful when does not exist any theoretical model for a problem. Moreo-ver, ANN can optimize an objective function subject to a set of constrains, or can be used for forecasting, if the network is trained with time series representing a given phenomenon. In forecasting, artificial intelligence (AI) is used to identify the relation between a technology property (quality surface, roughness, etc.) and process param-eters. We are talking about what is called machine learning. The use of this technology is possible thanks to the more and more frequent integration of sensory components into manufacturing equipment. That guarantee the availability of a considerably amount of process data that can be transformed in machine knowledge. The most common machine learning technique is learning from example (or inductive learning). A learning program that generalizes the example and build general rules. Afterwards, those rules can be applied to new contexts. In this research, machine learning algo-rithms can be used to find the relation between energy consumption and process pa-rameters. In that case, the data will come from the practical experience on the shop floor [BASH00].

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As far as optimization problem in concerned, artificial intelligence is used in context characterized by a considerable variability, where it is necessary a tool for modeling and optimize a process chain in a dynamic environment. For instance, contexts char-acterized by the rapid introduction of new products in the market. In that case, it is necessary to find a way to redesign the MS, and consequently to optimize it again. Eike Permin et al. introduce the concept of self-optimization as a solution to the need of fast configuration and optimization of MSs. The author developed a supporting tool that provides information about the current system status and assistance to improve continuously the operating point. The idea is using AI in order to make a system adapt-ing to the changes of the environment, without external intervention. At the base of the concept of self-optimization is the combination of cybernetic and deterministic models, in order to build systems that can change their internal state according to the changes of external conditions. In particular, self-optimization uses embedded expert knowledge and direct process information. According to the MS level of automation, self- optimization can support the human operator illustrating different way to act or can operate autonomously deciding everything without the human approval. In other word, if a system is working in the optimal point but something in the environment changes, the system can find again the new optimal point. The authors suggest its application in the assembly lines, which requires the capability of fast reaction to un-expected market changes [PERM15].

Another self-optimizing approach is proposed by B. Denkena et al. The authors sug-gest the use the manufacturing data, gained by equipment sensory components, for the description of the effects of machining parameters on the cutting performances. The methodology prescribes the use of Support Vector Machine (SVM) as machine learning technique, which is an alternative to ANN. In the paper, SVM generates a multidimensional model for the prediction of the shape error in a milling process. That model is used for implementing a self-optimization cutting process. The initial process parameters are set on the base of the boundary conditions generated in the previous modeling phase. Those parameters are optimized iteratively and, after every iteration, they are adjusted. In the study, the instrument for storing and analyze the data is Virtual Planner, and the machining equipment is connected to a computer with this application on it. That approach can be adapted for different MSs and it allows the determination

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of the relation between process parameters and performances. Those relations are modeled and used for the optimization of the milling process [DENK16].

Another common approach to process chain models is characterized by the use of simulations. As a matter of fact, manufacturing system is the largest application domain for simulation modeling. B.Denkena et al. proposed a method, called DTI method (De-signing Technological Interfaces), for process planning that allows the design and the optimization of the technological interfaces. In a simple model only the necessary in-teraction are mapped, and the aim of the study is the minimization of the number in interfaces in a process chain model. The methodology point at finding the technology dependencies needed for a good model. It is composed of four stages: process anal-ysis, interface modeling, optimization and physical implementation [DENK06].

Figure 2- 10: Process Interfaces representation [DENK11]

The process chain is represented with a Sadt-diagram, which allows the distinction between economic and technological variables (see figure 2-10). To applicate the method, it is necessary a process database with all the information about the equip-ment, the process and the work piece. The database is an input to the simulation en-vironment. Simulations help find the technology dependencies between multiple man-ufacturing processes. The benefits of simulation approach is that it is possible captur-ing the dynamics of the manufacturcaptur-ing process and the cause-effect relation. Once we

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have the process interfaces, which are represented by equations, it is possible to op-timize the chain using those equations as target functions of the multi-criteria optimi-zation problem. The same author points out that, in literature, process chain optimiza-tion models focus on a local optimizaoptimiza-tion of the single process and do not consider a holistic view of the chain. Considering the process interdependencies leads to a better operation of the overall chain because the parameters trade-off are taken into consid-eration. The paper presents the benefits and the differences between the two ap-proaches, as long as the definition of technology interfaces and holistic chain optimi-zation. When the interaction between processes are neglected, we find the optimum value for each machine of the chain. In this case, at each machine corresponds a different optimization problem. That method is easily applicable, but it does not repre-sent the global optimum [DENK11].

Christoph Herrmann and Sebastian Thiede proposed another simulation approach to mapping production system with all the interdependencies between the processes. In particular, they took into consideration all the energy inputs and outputs in the produc-tion chain as well as the energy consumpproduc-tion due to the technical building services (TBS). The latter aspect is less significant for this study, whose aim is the modelling of only the manufacturing processes. The authors underline the relation between the en-ergy needed in machining processes and TBS enen-ergy consumption. In fact, TBS is responsible for providing all the resources for the plant, included the ones required by the manufacturing level, such as electricity, compressed air, steam or cooling water. The aim of that methodology is having an integrated overview of the MS, considering simultaneously cost, quality, time and energy targets. For this purpose, an integrated model is built, which includes both ecological and economical aspects and all the in-terdependencies of technical equipment. In the model, machine energy consumption is classified in fixed and variable demand. The first one is related to control units, pump and coolers. The second regards tool handling and positioning and cutting operations. The fixed energy consumption is not directly value adding, but it makes possible the execution of the machining activities. However, it represents sometimes a considerable part of machines energy consumption. Analyzing machines energy profile, it is possible to detect the critical component and concentrate on them the efforts for achieving a better energy use. The authors propose a statistical model, whose aim is to be as detailed as necessary and not as detailed as possible like in the other statistical mod-els. In fact, in the simulation does not reflect the behavior of all the machines, but it is

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used a modular process design. In particular, the model describes only the basic and relevant behavior of each specific process. Therefore, the obtained model is high ap-plicable and it takes less modeling efforts. Combining different process modules, it is possible to obtain a process chain. The simulation computes load curves for equip-ment energy consumption at which can be added energy contract information, so as it is possible an evaluation of energy costs [HERR09].

In order to integrate ecological and economic analysis of the MS, C. Herrmann et al. developed a hybrid model that considers both continuous (material, parts, etc.) and discrete flows (energy, emissions, heat, etc.). The integration is obtained using Unified Modeling Language (UML) for real time. In the paper, the production line appears as a series of machines, buffers and quality gates. Those objects are described using “active object”, that allows the definition of interfaces and the characterization of the equipment through parametrization. The different states of the machines are described by “statecharts”, and the non-linear relationships by “lookup tables” [HERM07].

Hyun Woo Jeon et al. proposed a model for energy assessment at product, plant and industry level. In order to assess the plant energy requirement, the authors modeled the energy consumption of the single machine using the following expression:

Energy = ∑𝑁_𝑖=1𝑃𝑜𝑤𝑒𝑟(𝑖)𝑥 𝑇𝑖𝑚𝑒 (𝑖) (2.3)

where i is the index of machine state.

In order to use this expression, it is necessary the identification of the machine states and power and time needed in each state. This information can be taken from the process plan. The processing power in cutting operation can be modeled as a function of MRR. Consequently, data about the raw material, the final product, the process op-erations and the equipment are needed. After the construction of the model, the plant energy consumption is simulated, using various production parameters. In the study, DoE allows the identification of significant factors in energy consumption. After that, a regression model is developed to estimate both total energy consumption and energy spent per product [JEON15].

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The table below contains the process chain models reviewed in this study. They are compared according to the type of approach used by the authors, the specificity of the models and their abstraction level. The column “Specific” indicates if the model is valid only for a specific MS (in this case we put a ✓), or if it can be easily adapted also for others process chains (empty cell). Some of the studies focus in the description of the whole system (Holistic view), taking into account the relations between processes. On the contrary, other model analyze the influence of all the process parameters on the performance, but they do it for one process at a time. That allows the local optimization of the single process, and it usually do not coincides with the global optimum. The distinction between those approaches is made using the column “Holistic”. The table includes also an evaluation of the models suitability for the representation of additive manufacturing and cutting processes. In general, specific process for machining op-erations are not easily applicable in AM process mapping. On the contrary, generic process chain models suit AM.

MODELS APPROACH HOLISTIC SPECIFIC OBIECTIVE AM CP Mary

Kathryn Thompson

et al.

Graphical ✓ ✓ Map the

manu-facturing process of the artifact used in a MS ✓ Leigh Smith and Peter Ball Graphical and expert knowledge ✓ ✓ Map physical flows in a MS ✓ ✓ H.Wiemer et al.

Statistic ✓ Detect process

interactions

✓

Xuan-Luu Hoang et

al.

Statistic ✓ ✓ Detect process,

product and re-sources interac-tions ✓ ✓ Najibesada t Sadati et al. Statistic and expert knowledge Detecting control parameters and design process parameters ✓ ✓

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2.1.5 Existing approaches to sustainable process chain optimization

Once that companies measured their actual performances and built monitoring sys-tems to measure their progresses in the pursuing of energy efficiency, the next step is the preparation of an action plan to improve the energy and resources usage.

In order to minimize the impacts that manufacturing systems have in the atmosphere, it is possible to act in the selection of energy supply system and/or in the scheduling of the production plan and/or in optimizing process parameters.

L. Feng et al. studied the actual use of purchased energy from utility companies and renewable energy in manufacturing plants and proposed an approach for the optimi-zation of energy supply system. It is a multi-objective optimioptimi-zation problem, whose ob-jects are the minimization of the energy purchased by utilities companies, the energy cost and the emissions to the atmosphere. The main renewable energy sources are solar and wind energy. The problem of renewable energy use is their instability, due to the variability of weather and climatic conditions. In the research, the emissions due to the energy use are expressed through the emission index (kilogram per megawatt

E. Permin et al. Artificial intel-ligence Build a system capable of auto-regulation ✓ ✓ B. Denkena et al Artificial intel-ligence

Build process ca-pables of

auto-regulation

✓ ✓

B.Denkena et al.

Simulation ✓ ✓ Detect the most

important pro-cess interaction ✓ Christoph Herrmann and Sebas-tian Thiede

Simulation ✓ Integrate

ecologi-cal and economi-cal aspects

✓

Hyun Woo Jeon et al.

Simulation ✓ Assess the total

energy consump-tion

✓

Table 2- 1: Classification of the models in literature

Standardized model for process chain assessment of cost, time and environmental impact

Facoltà di Ingegneria

Laurea Magistrale in Ingegneria Gestionale

Tesi di laurea

Standardized model for process chain assessment of

cost, time and environmental impact

CANDIDATO

RELATORE

Abstract

Table of Contents

List of Figures

List of Tables

List of Abbreviation

1

Introduction

2

Literature review

Process

Process A

Input

Output/

input

Process B

Output

Process