Résumé: The present paper focuses on the determination of the optimum cutting conditions leading to minimum surface roughness as well as cutting force, cutting power and maximum productivity, in the case of the turning of the Polyoxymethylene polymer POM C using cemented carbide cutting tool. The optimization is based on the response surface methodology, RSM, (desirability function approach). Furthermore, the analysis of variance (ANOVA) is exploited to establish the statistical significance of the cutting parameters on different technological ones studied. The results revealed that the surface roughness is strongly influenced by feed rate with a large contribution, followed by cutting depth, whereas, the cutting speed has no influence. Regarding cutting force, it is found that depth of cut and feed rate are the most significant terms. The RSM allowed the optimization of the cutting conditions for minimal surface roughness, cutting force, cutting power and maximal material removal rate.

Résumé: The present work concerns an experimental study dealing with cutting parameters’ effects on the surface roughness, cutting force, cutting power, and productivity during turning of the polyoxymethylene (POM C) polymer. For that, a cutting tool made of cemented carbide was used. The work is divided into three steps. The first one deals with unifactorial tests, where the evolution of the machining parameters (roughness criteria, cutting force components, and cutting power) is investigated by varying cutting speed, feed rater, and depth of cut. The second part concerns the modeling of the output parameters: arithmetic roughness, cutting force, cutting speed, and material removal rate by using the results of a full factorial design (L27). The second step concerns the adoption of the two modeling techniques, which are the response surface methodology (RSM) and the artificial neural network (ANN). The obtained results related to two both techniques are compared in order to discern the most efficient one. The last step of the present research work concerns the multi-objective optimization using the desirability function (DF). The optimization was carried out according to three approaches, which are the “quality optimization,” “productivity optimization,” and the combination between the quality and productivity.

Résumé: For better surface quality and production efficiency in mechanical manufacturing, operators need to make a good choice of cutting parameters such as depth of cut, cutting speed, feed rate and also the cooling condition that affects not only product quality, but also respects the ecological aspect and preserves environment. In the current work, an experimental study has been carried out in order to evaluate the influence of cutting parameters on surface roughness, when turning of X210CR12 steel using multilayer-coated carbide insert with various nose radius. The ANOVA analysis has been performed to determine the effect of cutting conditions on Ra. The results have been analyzed using S/N ratios, mean effect graphs, and 3D response surface plots. The results indicate that the cutting insert nose radius and the feed rate are the mainly affecting factors on surface roughness. Confirmatory experiments have been established after Taguchi’s optimization. It has been found that the MQL is an interesting way to minimize lubricant quantity, protect operator health and environment with keeping better machining quality.

Résumé: This paper examines the effect of the material and machining parameters on cutting force, and surface roughness when turning polyoxymethylene POM C using a carbide tool. Turning experiments were conducted based on the Taguchi orthogonal matrix L16, taking into account cutting speed, cutting feed and cutting depth as input process parameters. Multi-objectif optimization was achieved through DFA, GRA, and TOPSIS methods. Results proved that both GRA and TOPSIS techniques provided a similar optimal parametric condition, namely a Vc = 500 m/min, a f = 0.08 mm/rev and a ap = 1 mm. On the other hand, the DFA method gives the following optimal parameters: Vc = 345.04 m/min, f = 0.09 mm/rev and ap = 1 mm. Experimental approach illustrates the feasibility and effectiveness of these proposed methods GRA and TOPSIS for multi-objectif optimization by contributing to the DFA method.