Technical Paper
Multi-objective optimization and innovization-based knowledge discovery of sustainable machining process

https://doi.org/10.1016/j.jmsy.2022.04.013Get rights and content

Abstract

Nowadays, establishing sustainable machining processes is getting a widespread interest in many industries. Moreover, the last decade has seen a rapid rise in using knowledge-embedded optimization techniques to optimal determining of cutting conditions, and accordingly achieving the required sustainability targets. However, there is still a need to establish an approach which can fully analyse the optimized results, offering recommended settings to accommodate any desired levels of the sustainable machining responses. Such approach should be also flexible to switch between different desired objectives with extremely minimum efforts to accommodate the various requirements of the sustainable machining system. In this context, the current study offers a novel knowledge discovery approach to optimize the sustainable machining processes. In addition, a case study is conducted in order to validate the proposed approach. Genetic Programming (GP) and Non-dominated Sorting Genetic Algorithm (NSGA-II) were utilized for modelling and optimization purposes, respectively. In addition, the optimal cutting conditions were clustered into seven clusters, offering five different desirability levels to minimize the surface roughness, specific energy, and unit volume machining time. These obtained results showed that the decision maker can easily use any of the discovered knowledge based on the optimal solutions in their determined clusters. The proposed approach is promisingly applicable on similar engineering applications as a novel direction resulted by collaboration between machine learning (ML) and multi-objective optimization (MOO).

Introduction

There is an increasing demand these days to accommodate the sustainability requirements within a wide range of industrial related activities. These requirements are complementary with the sustainability Triple Bottom Line (TBL) of the environmental, social, and economical aspects. It should be also stated that all relevant levels in the manufacturing industry (i.e., product, process, and system levels) must receive huge efforts in order to achieve the desired sustainability requirements [1]. This can be achieved by adopting green technologies such as 6 R concept (i.e., reduce, reuse, recover, redesign, remanufacture, recycle) at the product level. For the process level, it is promoted to enhance the process planning to optimize the process significant parameters which lead to reduce the environmental impacts, occupational hazards, and the power consumption. Concerning the system level, which is more comprehensive and integrated level, the entire life cycle stages of premanufacturing, manufacturing, use, and post use should be considered to properly implement the sustainability principles. The sustainability principles should be implemented simultaneously within those three levels to accurately achieve the main goals of the sustainability concept in the manufacturing industry. Manufacturing processes, including the machining operations are considered one of the main sources for energy consumption and carbon emissions footprint [2]. In addition, according to the high contribution of the machining operations in the manufacturing sector and its environmental impacts, machining operations are targeted to involve the sustainability principles [1]. Consequently, reducing the machining energy consumption and its environmental burdens is recently considered one of the promising approaches to accommodate the sustainability requirements in the entire manufacturing system [3].

To satisfy the sustainability requirements in the machining field, a lot of efforts have been devoted for developing new machining strategies that minimize the negative environmental impacts of the machining processes. These strategies are presented in the sustainable machining ring such as dry cutting, cooling/lubrication methods using the minimum amounts of cutting fluids, and cryogenic cooling [4].

On the other hand, many attempts have been focused on assessing the sustainability levels of the machining processes through developing sustainability metrics tailored for the machining processes [5], [6]. Besides, the Life Cycle Assessment (LCA) becomes a common technique to evaluate the environmental impacts associated with the machining processes as presented in the open literature [7], [8]. These assessment techniques considered a single index to elect the best cutting condition, enhancing the level of sustainability performance. This may lead to inaccurate selection of the optimized settings as these approaches in general sticks only with a single solution instead of set of trade-off solutions; which can offer more options for expert decision makers to consider other crucial design and sustainable aspects properly; this would be achievable when we consider multi-objective design which obtains Pareto-front (PF) solutions set, as the trade-off solutions.

Thus, many researchers [9], [10] have utilized the multi-objective optimization application in the machining processes. This was aimed to simultaneously optimize more than one objective of the machining characteristics. In addition, multi-objective optimization produces various optimal solutions which show different performance through the studied objectives. Gray-Taguchi approach was utilized for multi-objective optimization when cutting aerospace-grade titanium alloy [11]. In this study, the Gray-Taguchi approach was utilized to optimize the values of cutting speed, depth of cut, feed, and approach angle to minimize the nose tool wear, flank tool wear, and metal removal rate in dry environment (i.e., green machining). For example in the non-conventional machining processes, the Multi Objective Particle Swarm Optimization (MOPSO) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were utilized to optimize the parameters of the electro-discharge machining (EDM) which include pulse on time, duty cycle, discharge current, and gap voltage when cutting mild steel [12]. Besides, few research papers have connected the utilization of multi-objective optimization with the sustainability assessment of the machining processes [13]. It should be stated that the output of the multi-objective optimization needs deeper understanding rather than electing one of the solutions in the obtained pareto-front. Thus, the concept of “innovization” started to be applied to analyse the optimal solutions to gain further understanding and knowledge about the optimized settings and solutions. The analysis of the optimal solutions is conducted through relating the optimal solutions to their process parameters (i.e., objective space vs. decision space) which can lead to insights for similar problems. In fact, the innovization is a post-processing phase for discovering hidden design related rules, knowledge, and patterns among decision variables and objective values (i.e., variable-variable, objective-objective, variable-objective, and objective-variable formulas/rules); which starts after optimization phase. Accordingly, its outcomes can highly improve our insight understanding about the design related aspects [14], [15]. Despite the importance of this analysing process, there are few papers included this concept in the literature. Similar analysis has conducted in [16] in order to generate trade-off solutions and extract rules which are associated with the design variables of the turning process. This study offered the recommend ranges for the tool geometry and machining parameters which achieved lower cutting temperature and wear depth as well as higher metal removal rate. In addition, the decision map has been generated in [17] to ease the selection of the optimal cutting conditions when machining using nano-cutting fluids.

Many attempts have been performed in the open literature to optimize the sustainable machining performance; however, to the best of our knowledge, no approach is existing to compare different machining strategies and fully analyse the pareto-front solutions offered by the multi-objective optimization techniques. Accordingly, there is still need to introduce an approach which is able to fill this gap through connecting the sustainability principles of the machining processes, application of multi-objective optimization, and the knowledge discovery from the optimized solutions. In addition, this approach which can analyse these solutions will provide the decision maker with different optimized ranges/recommendations to fit any desired target(s) (i.e., the desirability levels). In other words, this approach can offer recommended settings to accommodate any desired levels of the considered objectives and it is also flexible to switch between different desired levels with extremely minimum efforts, without re-running of the optimizer.

Section 2 in this paper presents the main steps of the proposed approach. A case study to prove the effectiveness and practicality of the proposed approach is provided in Section 3. Finally, the conclusion is presented in Section 4.

Section snippets

The proposed approach

Fig. 1 shows a flowchart of the proposed approach. This approach includes ten consecutive steps. These steps are summarized as the follows:

  • The first step includes the selection of different machining strategies whose performance needs to be analysed such as machining using various cooling techniques, machining using various cutting tools, and machining using various assisted techniques. Besides, the studied process parameters of these strategies should be identified in this step.

  • The selection

Validation

The experimental data which were obtained in [20] are used in this paper to validate the proposed approach. Various end milling experiments were conducted in [20] on AISI 1045. The aim of this paper was to investigate the effect of using two different solid lubricants which were graphite and molybdenum disulphide on cutting forces and surface roughness. Besides, these two lubrication strategies have been compared with wet machining. In overall, 81 end milling experiments were performed at

Conclusions and future work

In this study, a new knowledge discovery approach has been presented to analyze the optimized solutions of the sustainable machining processes. The approach can provide the decision maker with different optimized ranges/recommendations to fit any desired target(s). In order to assess the effectiveness and the practicality of this approach, the consecutive steps of the proposed approach have been implemented on a machining case study, where different machining strategies, process parameters, and

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC).

References (21)

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© 2022 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Scientific Committee of the NAMRI/SME.

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