Mono and multi-objective optimisation in hard machining of DIN 100Cr6 with wiper ceramic insert using Taguchi, M-PCA, MOORA and DF methods


Salim C., UYSAL A., Mohamed Athmane Y., Sebti B., Hadjira B., Eddine C. S.

Engineering Research Express, vol.8, no.13, 2026 (ESCI, Scopus)

  • Publication Type: Article / Article
  • Volume: 8 Issue: 13
  • Publication Date: 2026
  • Doi Number: 10.1088/2631-8695/ae7f6e
  • Journal Name: Engineering Research Express
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus
  • Keywords: ANOVA, DIN 100Cr6, hard turning, modelling, optimisation, wiper cutting tool
  • Yıldız Technical University Affiliated: Yes

Abstract

This study aims to analyse the effect of cutting conditions on the dry turning of DIN 100Cr6 bearing steel, using a wiper-geometry ceramic (Al (Formula presented) (Formula presented) O (Formula presented) (Formula presented) TiC) cutting tool (reference CC650WG). A reduced Taguchi (Formula presented) (Formula presented) experimental design was exploited to study the impact of cutting conditions, namely cutting speed, feed rate, and depth of cut, on the resulting responses such as surface roughness, tangential cutting force, material removal rate, and cutting power, through statistical analysis based on analysis of variance. The analysis of experiments results by response surface methodology led to the development of regression models for predicting the output parameters (Formula presented) (Formula presented) and (Formula presented) (Formula presented)). On the other hand, four optimisation methods mono-objective using the method of Taguchi and multi-objective by exploiting the desirability function method, modified principal component analysis (M-WPCA), and multi-objective optimisation (MOO) by ratio analysis were implemented to identify the optimal combination that simultaneously minimises (Formula presented) (Formula presented)), (Formula presented) (Formula presented)), and (Formula presented) (Formula presented)) and maximises (MRR). The results show that the desirability function method yielded minimal roughness (Formula presented) (Formula presented)). In contrast, the M-WPCA method resulted in minimal (Formula presented) (Formula presented)) and (Formula presented) (Formula presented)), but at the cost of the lowest (MRR). Finally, the MOO by ratio analysis method distinguished itself with an intermediate (MRR) value.