Abstract

In this article, new research on the multi-objective optimization of the process parameters applied to enhance the efficiency in the shoe-type centerless grinding operation for the inner ring raceway of the ball bearing made from SUJ2 alloy steel is presented. The four important input parameters for this process, which included the normal feed rate of fine grinding (Snf), the speed of the workpiece (Vw), the cutting depth of fine grinding (af), and the number of ground parts (Np), were investigated. The aim of the study was to find the most appropriate value set of process parameters in order to, simultaneously minimize the grindstone wear (Gw), maximize the material removal rate (MRR) and the total number of ground parts in a grinding cycle (N’p), while guaranteeing other technology requirements such as surface roughness Ra ≤ 0.5 (µm), oval level Op ≤ 3 (µm), etc. In order to solve the problem, based on the experimental data, in which the grindstone wear was measured online by a measuring system consisting of two pneumatic probes, the optimization of the target functions of Gw, N’p, and MRR and mathematical models that express the dependencies of outcome parameters Gw, Ra, Op, MRR, etc. on the process parameters were determined. Therefore, a global optimal solution of such a discrete and nonlinear multi-objective optimization problem was solved by using a genetic algorithm, presenting the most appropriate process parameters as follows: Snf = 15.38 (µm/s), Vw = 6.00 (m/min), af = 11.76 (µm), and Np = 20 (parts/cycle). In addition, the impact of the four process parameters (Snf, Vw, af, Np) on the wear of the grinding wheel (Gw), the oval level of parts (Op), and the surface roughness of parts (Ra) was evaluated. The discovered technology mode has been applied to the real machining process for the inner ring raceway of the 6208_ball bearing made from SUJ2 alloy steel, and the outcome showed a much better result in comparison with default setting modes, while still ensuring the technology requirements. The difference between the predicted values and the real values of the parameters Gw, Ra, Op, and MRR were controlled within 5% of the ranges.

Highlights

  • Bearings have long been widely utilized in various industries, and requirements for precision bearings have been increasing significantly in the continual pursuit of perfect quality and high-performance products [1]

  • The difference between the predicted values and the real values of the parameters grindstone wear (Gw), roughness of parts (Ra), Op, and material removal rate (MRR) were controlled within 5% of the ranges

  • The aim of the experimental study was to determine the optimal set of process parameters in the shoe-type centerless grinding operation for the inner ring raceway surface, which allowed the smallest grindstone wear, the largest material removal rate, and the largest total number of ground parts in one grinding cycle, while the precision of ground parts was ensured

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Summary

Introduction

Bearings have long been widely utilized in various industries, and requirements for precision bearings have been increasing significantly in the continual pursuit of perfect quality and high-performance products [1]. To determine the optimum process parameters (Snf , Vw , af , Np ) to minimize the grindstone wear (Gw ) and to maximize the material removal rate (MRR) and the total number of ground parts in a grinding cycle (N’p), while ensuring the other technology requirements are still guaranteed such as: the surface roughness Ra ≤ 0.5 (μm), the oval level Op ≤ 3 (μm) in the STCG operation for SUJ2 alloy steel. To determine the influences of the process parameters on the wear of the grinding wheel (Gw ), the oval level of the parts (Op ), and the surface roughness of the parts (Ra) in the STCG operation for SUJ2 alloy steel As noted above, this type of extensive study in the STCG operation for the inner ring raceway surface of the 6208_ball bearing made from SUJ2 alloy steel has not been published before.

Test Specimen and Grinding Equipment
The schematic diagram and realistic image of
SJ400 roughness
Images and diagram for the structural of theprinciples measurementofequipment
The Determination of Process
Design of Experiments and Experimental
Establishing Mathematical Models for Main Output Parameters over
Establishing Constraints
Establishing the Objective Function
Findings
Conclusions
Full Text
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