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<p>Heat-treated hardened tool steels are significantly utilised in die and mould manufacturing, automotive, aerospace, and precision engineering industries due to their excellent hardness, wear resistance, and dimensional stability. However, machining these materials remains a significant manufacturing challenge because of high cutting forces, elevated cutting temperatures, rapid tool wear, and deterioration of surface integrity. To address these challenges, this study suggests an integrated statistical and multi-criteria decision-making (MCDM) method for the multi-response optimisation of CNC turning parameters of heat-treated MDC-K tool steel (~ 58 HRC). The cutting speed (175–275&#xa0;m/min), feed rate (0.1–0.3&#xa0;mm/rev), and approach angle (70–90°) were chosen as control factors, whereas cutting force (<i>N</i>), surface roughness (<i>Ra</i>), and tool–chip contact length (<i>L</i>) was selected as response variables. The Box–Behnken design of the response surface methodology (RSM) was used for modelling the process, and the analysis of variance (ANOVA) was used to assess the statistical significance of the factors. To solve the multi-response optimisation problem, an integrated Multiple Criteria Ranking by Alternative Trace (MCRAT) and Ranking the Alternatives by Perimeter Similarity (RAPS) approach was employed; this allowed the objective weighting of the outputs and a powerful ranking of the alternatives of the machining processes. It can be concluded from the results that the surface roughness and cutting force are most sensitive to feed rate, and cutting speed shows significant control over other characteristics of the interaction between the tool and chip. The best machining conditions were determined to be 225&#xa0;m/min, 0.1&#xa0;mm/rev and 80° cutting angles, which give minimum cutting force and better surface quality. The validation of the proposed framework using the agreement between the RSM-based desirability optimisation and the MCDM ranking showed the reliability of the proposed framework. This is an integrated approach that presents a systematic and efficient approach for optimising machining parameters for hard-to-machine materials, which may be implemented in high-tech manufacturing processes.</p>

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A synergistic RSM–MEREC–MCRAT–RAPS approach for multi-response optimisation of CNC turning of hardened tool steel

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  • 出版模式：

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  • 发表日期：

    2026年8月2日

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  • Adooru L. N. Arunkumar,
  • Krishnadas Narayanan Nampoothiri,
  • Sunil Kumar

摘要

Heat-treated hardened tool steels are significantly utilised in die and mould manufacturing, automotive, aerospace, and precision engineering industries due to their excellent hardness, wear resistance, and dimensional stability. However, machining these materials remains a significant manufacturing challenge because of high cutting forces, elevated cutting temperatures, rapid tool wear, and deterioration of surface integrity. To address these challenges, this study suggests an integrated statistical and multi-criteria decision-making (MCDM) method for the multi-response optimisation of CNC turning parameters of heat-treated MDC-K tool steel (~ 58 HRC). The cutting speed (175–275 m/min), feed rate (0.1–0.3 mm/rev), and approach angle (70–90°) were chosen as control factors, whereas cutting force (N), surface roughness (Ra), and tool–chip contact length (L) was selected as response variables. The Box–Behnken design of the response surface methodology (RSM) was used for modelling the process, and the analysis of variance (ANOVA) was used to assess the statistical significance of the factors. To solve the multi-response optimisation problem, an integrated Multiple Criteria Ranking by Alternative Trace (MCRAT) and Ranking the Alternatives by Perimeter Similarity (RAPS) approach was employed; this allowed the objective weighting of the outputs and a powerful ranking of the alternatives of the machining processes. It can be concluded from the results that the surface roughness and cutting force are most sensitive to feed rate, and cutting speed shows significant control over other characteristics of the interaction between the tool and chip. The best machining conditions were determined to be 225 m/min, 0.1 mm/rev and 80° cutting angles, which give minimum cutting force and better surface quality. The validation of the proposed framework using the agreement between the RSM-based desirability optimisation and the MCDM ranking showed the reliability of the proposed framework. This is an integrated approach that presents a systematic and efficient approach for optimising machining parameters for hard-to-machine materials, which may be implemented in high-tech manufacturing processes.