This study introduces a comprehensive framework that integrates Multi-objective optimisation with Multiple-Criteria Decision Making (MCDM) techniques to enhance decision-making in sustainable machining processes. Through a systematic literature review, the framework is developed, incorporating sustainability indicators, experimental validation, and multi-objective optimisation algorithms to identify the optimal machining conditions. To address the challenge of subjective judgements and the multiple solutions generated by multi-objective optimisation, MCDM techniques are employed to select the best solution. By considering economic viability, environmental impact reduction, and social commitment, the proposed framework overcomes the limitations of traditional optimisation approaches. It provides an objective and holistic approach to decision-making, promoting sustainability in machining processes. The framework’s effectiveness and applicability are demonstrated through a case study, validating its potential for practical implementation and informed decision-making
Table 14. Sustainability assessment of steel through normalised responses. Steel DOC 0.8 1.5 1.5 1.3 1 1.5 1.1 0.9 1.4 1.2 1.4 Feed 0.3 0.3 0.5 0.3 0.3 0.4 0.3 0.3 0.3 0.3 0.5 Vb 0.000 1.000 0.879 0.714 0.286 0.939 0.429 0.143 0.857 0.571 0.838 T 0.384 0.000 0.430 0.531 0.274 0.507 0.648 0.388 0.327 0.242 1.000 I 0.368 0.000 1.000 0.105 0.263 0.500 0.210 0.316 0.053 0.158 0.910 V 0.699 1.000 0.000 0.914 0.785 0.500 0.828 0.742 0.957 0.871 0.050 A 0.000 0.349 1.000 0.249 0.100 0.674 0.149 0.050 0.299 0.199 0.973 ESA 1.299 2.000 2.139 2.055 1.499 2.246 1.859 1.435 2.065 1.747 2.798 EnSA 0.368 0.000 1.000 0.105 0.263 0.500 0.210 0.316 0.053 0.158 0.910 By normalising the dataset, the responses are transformed into a normalised dataset, which is presented below. This normalisation process enables a comprehensive analysis and comparison of the responses to support the MCDM techniques application and subsequent sustainability assessment within the framework.
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(cid:0) max yij (cid:0) max yij (cid:0) yij (cid:0) (cid:0) min yij smaller (cid:0) xij the (cid:0) better xij (cid:0) (cid:0) yij (cid:0) min yij (cid:0) max yij (cid:0) min yij larger (cid:0) the (cid:0) better
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The normalised values obtained from Table 12 are used for calculating the sustainability metrics. In this analysis, the weight of each response is assigned a value of 1, indicating equal importance. The sustainability metrics are calculated based on the following considerations: Economic Sustainability (ESA): Economic sustainability is evaluated by considering the variables Vb (flank wear), T (machining time), I (spindle motor AC current consumption), and V (spindle vibration). Reducing these parameters leads to cost reduction and improved product quality, thereby enhancing economic sustainability. SSA 0.00 1.00 0.03 0.03 0.95 0.01 0.87 0.00 0.47 0.45 0.50 0.49 0.96 0.98 0.49 0.46 0.01 SSA 0.000 0.349 1.000 0.249 0.100 0.674 0.149 0.050 0.299 0.199 0.973 (4) (5)
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Environmental Sustainability (EnSA): Environmental sustainability is assessed by focusing on the variable I (spindle motor AC current consumption). By reducing the current consumption, energy usage is minimised, resulting in lower greenhouse gas emissions and improved environmental sustainability. Social Sustainability (SSA): Social well-being is linked to the variable A (spindle acoustic emission), which represents noise levels. By reducing acoustic emissions and noise levels, the working environment becomes more conducive, benefiting the well-being and comfort of workers. This social sustainability. contributes enhanced to
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