Optimization of the Cutting Parameters Affecting the Turning of AISI 52100 Bearing Steel Using the Box-Behnken Experimental Design Method

Abstract

In this study, we aimed to optimize the cutting parameters that affect the minimum temperature and power consumption in the turning of AISI 52100 bearing steel. For this, the Box-Behnken experimental design method, which was used for the lowest number of experiments in the experimental systems created using the response surface method (RSM), was used. The cutting parameters affecting the turning of the AISI 52100 bearing steel were determined as the cutting speed, depth of cut, and feed rate based on a literature research. The temperature and power consumption values were obtained via analyses according to the experimental design method determined by the finite element analysis (FEM) method. The results obtained were analyzed in Design Expert 13 software. According to the analysis results, the parameter values were determined for the minimum temperature and power consumption. The temperature and power consumption variables were affected by all three parameters, namely the cutting speed, depth of cut, and feed rate. For the minimum temperature and power consumption, a cutting speed of 162.427 m/min, depth of cut of 1.395 mm, and feed rate of 0.247 mm/rev, as well as the feed rate parameters, affected both the temperature and power consumption the most. In addition, it was determined that the cutting speed parameter had the least effect on both the temperature and power consumption variables. In addition, validation experiments were carried out in a real experimental environment with optimum values for the cutting parameters. The results showed that the output values obtained within the limits of the study with the obtained equation were quite close (3.3% error for temperature, 6.6% error for power consumption) to the real experimental outputs.

Investigation of the Effects of Machining Parameters on Surface Roughness, Cutting Temperature and Cutting Force in Turning AISI 52100 Bearing Steel

“…Yıldız ve ark. [14] AISI 52100 rulman çeliğinin tornalanmasında minimum sıcaklık ve güç tüketimine etki eden kesme parametrelerinin optimize edilmesi amacıyla bir analiz gerçekleştirmişlerdir. Bunun için yanıt yüzey yöntemi kullanılarak oluşturulan deneysel sistemlerde en düşük deney sayısı için kullanılan Box-Behnken deneysel tasarım yöntemi kullanılmıştır.…”

Evaluation of steel turning by means of probability-based multi-objective optimization with appropriate numbers of attributes

“…Turning is a typical machining process. The number of turning machines is about 30% of all cutting machines in a cutting workshop (Thien Van et al, 2021;Hegde et al, 2022;Yıldız et al, 2023, Nguyen & Vo Thi, 2022.…”

“…The surface roughness of machining, cutting forces, vibrations, and the material removal rate (MRR) are usually used as the assessed attributes of quality evaluation for the overall machining process. In order to ensure the minimum value of surface roughness, Taguchi design and the Response Surface Method (RSM) were frequently used to conduct the optimization of cutting parameters such as cutting velocity, feed rate, and cutting depth in the turning process for various materials with minimizing surface roughness, or minimizing cutting forces, or maximizing the MRR, solely (Thien Van et al, 2021;Hegde et al, 2022;Yıldız et al, 2023, Nguyen & Vo Thi, 2022Irzaev et al, 2021).…”

“…However, until now, an appropriate solution for a concurrent optimization of minimizing surface roughness, minimizing cutting forces and vibrations, and maximizing the MRR in turning processes has not been achieved yet (Thien Van et al, 2021;Hegde et al, 2022;Yıldız et al, 2023, Nguyen & Vo Thi, 2022Irzaev et al, 2021).…”

“…Probabilitybased multiobjective optimization (PMOO) is a newly developed approach to conduct the concurrent optimization problem of multiple attributes (objectives). A new idea of preferable probability and its assessment have been put forward (Zheng et al, 2024).…”