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Optimizing Green Machining Processes Using MCDM Methods in q-rung Orthopair Fuzzy Environment
https://orcid.org/http://orcid.org/0000-0002-9624-5656
With the goal of satisfying environmental perspectives in the era of Industry 4.0, optimal energy consumption has become the most important quandary in manufacturing environment, which involves several machining processes aimed at achieving higher productivity, no-defect policy, and cost and time efficient product delivery. Need of adopting green machining techniques has now become evident to contribute towards environmental footprints. It is clearly comprehensible that strategic adaptation of eco-friendly raw materials or machining processes for energy saving may become extravagantly expensive in terms of initial investment. Nevertheless, optimizing the input parameters of the existing processes from the direction of energy consumption may be a simpler solution. In this paper, mathematical models addressing uncertainties in machining environments are demonstrated for two green machining processes, e.g. green turning and green milling. These processes are simultaneously optimized using three of the well-known multi-criteria decision-making (MCDM) techniques, namely, COmbinative Distance-based ASsessment (CODAS), Combined Compromise Solution (CoCoSo), and Mixed Aggregation by COmprehensive Normalization Technique (MACONT) in a q-rung orthopair fuzzy (q-ROF) setting contemplating inexplicabilities associated with the decision-making task. The experimental results for the green turning process identify that a combination of depth of cut = 2.0 mm, feed rate = 0.25 mm/rev and cutting speed = 400 m/min provides the optimal performance while striking a balance among total energy consumption, material removal rate, total specific energy and surface roughness in all the three q-ROF-MCDM methods. For the green milling experiments, q-ROF-CoCoSo and q-ROF-CODAS suggest a setting of spindle speed = 1200 rpm, feed rate = 800 mm/min, milling depth = 0.2 mm and milling width = 50 mm to achieve the best performance with respect to surface roughness, specific energy consumption, tool life, processing time and processing energy consumption, whereas, q-ROF-MACONT identifies the ideal setting as spindle speed = 1000 rpm, feed rate = 700 mm/min, milling depth = 0.3 mm and milling width = 40 mm for the same process. Finally, statistical analyses are conducted verifying consistency and reliability of the proposed q-ROF-MCDM methods, and demonstrating their effectiveness in optimizing the said machining processes.
Optimizing Green Machining Processes Using MCDM Methods in q-rung Orthopair Fuzzy Environment
https://orcid.org/http://orcid.org/0000-0002-9624-5656
With the goal of satisfying environmental perspectives in the era of Industry 4.0, optimal energy consumption has become the most important quandary in manufacturing environment, which involves several machining processes aimed at achieving higher productivity, no-defect policy, and cost and time efficient product delivery. Need of adopting green machining techniques has now become evident to contribute towards environmental footprints. It is clearly comprehensible that strategic adaptation of eco-friendly raw materials or machining processes for energy saving may become extravagantly expensive in terms of initial investment. Nevertheless, optimizing the input parameters of the existing processes from the direction of energy consumption may be a simpler solution. In this paper, mathematical models addressing uncertainties in machining environments are demonstrated for two green machining processes, e.g. green turning and green milling. These processes are simultaneously optimized using three of the well-known multi-criteria decision-making (MCDM) techniques, namely, COmbinative Distance-based ASsessment (CODAS), Combined Compromise Solution (CoCoSo), and Mixed Aggregation by COmprehensive Normalization Technique (MACONT) in a q-rung orthopair fuzzy (q-ROF) setting contemplating inexplicabilities associated with the decision-making task. The experimental results for the green turning process identify that a combination of depth of cut = 2.0 mm, feed rate = 0.25 mm/rev and cutting speed = 400 m/min provides the optimal performance while striking a balance among total energy consumption, material removal rate, total specific energy and surface roughness in all the three q-ROF-MCDM methods. For the green milling experiments, q-ROF-CoCoSo and q-ROF-CODAS suggest a setting of spindle speed = 1200 rpm, feed rate = 800 mm/min, milling depth = 0.2 mm and milling width = 50 mm to achieve the best performance with respect to surface roughness, specific energy consumption, tool life, processing time and processing energy consumption, whereas, q-ROF-MACONT identifies the ideal setting as spindle speed = 1000 rpm, feed rate = 700 mm/min, milling depth = 0.3 mm and milling width = 40 mm for the same process. Finally, statistical analyses are conducted verifying consistency and reliability of the proposed q-ROF-MCDM methods, and demonstrating their effectiveness in optimizing the said machining processes.
Optimizing Green Machining Processes Using MCDM Methods in q-rung Orthopair Fuzzy Environment
https://orcid.org/http://orcid.org/0000-0002-9624-5656
With the goal of satisfying environmental perspectives in the era of Industry 4.0, optimal energy consumption has become the most important quandary in manufacturing environment, which involves several machining processes aimed at achieving higher productivity, no-defect policy, and cost and time efficient product delivery. Need of adopting green machining techniques has now become evident to contribute towards environmental footprints. It is clearly comprehensible that strategic adaptation of eco-friendly raw materials or machining processes for energy saving may become extravagantly expensive in terms of initial investment. Nevertheless, optimizing the input parameters of the existing processes from the direction of energy consumption may be a simpler solution. In this paper, mathematical models addressing uncertainties in machining environments are demonstrated for two green machining processes, e.g. green turning and green milling. These processes are simultaneously optimized using three of the well-known multi-criteria decision-making (MCDM) techniques, namely, COmbinative Distance-based ASsessment (CODAS), Combined Compromise Solution (CoCoSo), and Mixed Aggregation by COmprehensive Normalization Technique (MACONT) in a q-rung orthopair fuzzy (q-ROF) setting contemplating inexplicabilities associated with the decision-making task. The experimental results for the green turning process identify that a combination of depth of cut = 2.0 mm, feed rate = 0.25 mm/rev and cutting speed = 400 m/min provides the optimal performance while striking a balance among total energy consumption, material removal rate, total specific energy and surface roughness in all the three q-ROF-MCDM methods. For the green milling experiments, q-ROF-CoCoSo and q-ROF-CODAS suggest a setting of spindle speed = 1200 rpm, feed rate = 800 mm/min, milling depth = 0.2 mm and milling width = 50 mm to achieve the best performance with respect to surface roughness, specific energy consumption, tool life, processing time and processing energy consumption, whereas, q-ROF-MACONT identifies the ideal setting as spindle speed = 1000 rpm, feed rate = 700 mm/min, milling depth = 0.3 mm and milling width = 40 mm for the same process. Finally, statistical analyses are conducted verifying consistency and reliability of the proposed q-ROF-MCDM methods, and demonstrating their effectiveness in optimizing the said machining processes.
Optimizing Green Machining Processes Using MCDM Methods in q-rung Orthopair Fuzzy Environment
J. Inst. Eng. India Ser. C
Chowdhury, Samriddhya Ray (author) / Chatterjee, Srinjoy (author) / Chakraborty, Shankar (author)
Journal of The Institution of Engineers (India): Series C ; 105 ; 1545-1569
2024-12-01
25 pages
Article (Journal)
Electronic Resource
English
Optimizing Green Machining Processes Using MCDM Methods in q-rung Orthopair Fuzzy Environment
| Springer Verlag | 2024
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