scholarly journals Multi-Objective Optimization of Process Parameters to Enhance Efficiency in the Shoe-Type Centerless Grinding Operation for Internal Raceway of Ball Bearings

Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 893
Author(s):  
Nguyen Anh Tuan
Keyword(s):  
Surface Roughness ◽  
Alloy Steel ◽  
Process Parameters ◽  
Machining Process ◽  
Multi Objective Optimization ◽  
The Real ◽  
Multi Objective ◽  
Fine Grinding ◽  
Centerless Grinding ◽  
The Impact

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.

scholarly journals Multi-Objective Optimization for Grinding Parameters of 20CrMnTiH Gear with Ceramic Microcrystalline Corundum

Materials ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1352 ◽  
Author(s):  
Shengyong Zhang ◽  
Genbao Zhang ◽  
Yan Ran ◽  
Zhichao Wang ◽  
Wen Wang
Keyword(s):  
Surface Roughness ◽  
Practical Significance ◽  
Grinding Wheel ◽  
Linear Regression Method ◽  
Grinding Process ◽  
Low Carbon ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Grinding Parameters ◽  
The Impact

(1) The alloy material 20CrMnTiH is widely used in gear manufacturing, but difficult to process, and its quantity (efficiency) and quality (surface quality) are generally negative correlation indicators. As a difficult but realistic problem, it is of important practical significance to explore how to efficiently grind high-precision low-carbon alloy gear workpieces. (2) Firstly, the pixel method was applied to analyze the grinding principles and explore the grinding parameters—the grinding wheel speed and grinding wheel frame moving speed—as well as the feed rate, which impacts the grinding indicators. Secondly, based on the ceramic microcrystalline corundum grinding wheel and the 20CrMnTiH gear workpiece, controlled experiments with 28 groups of grinding parameters were conducted. Moreover, the impact curves of the grinding parameters on the grinding indicators—the grinding efficiency, grinding wheel life, and surface roughness—were obtained by the multiple linear regression method. Finally, the multi-objective optimization method was used to comprehensively optimize the grinding process. (3) Compared with the traditional grinding process, under optimized grinding parameters, the 20CrMnTiH gear workpieces have a lower surface roughness and a longer grinding wheel life, and require a shorter time to achieve grinding accuracy. (4) The grinding experiments showed that the grinding parameters are linearly related to the grinding indicators. The optimization results show that the precision, efficiency, and economy of the 20CrMnTiH gear grinding process have been improved via the comprehensive optimization of the grinding parameters.

Optimization of Machining Process Parameters for Minimizing the Waste Stream Response through Multi-Objective Optimization

2019 ◽  
Author(s):  
Sundar Singh Sivam Sundarlingam Paramasivam ◽  
Ganesh Babu Loganathan ◽  
Krishnaswamy Saravanan ◽  
Durai Kumaran ◽  
Raj Rajendran ◽  
...  
Keyword(s):  
Process Parameters ◽  
Machining Process ◽  
Waste Stream ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Stream Response

High-pressure jet-assisted turning of AISI 304: Experimental and multi-objective optimization approach

2017 ◽  
Vol 232 (6) ◽  
pp. 734-750 ◽  
Author(s):  
Sayed E Mirmohammadsadeghi ◽  
H Amirabadi
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
High Pressure ◽  
Cutting Force ◽  
Process Parameters ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Aisi 304 ◽  
Multi Objective ◽  
304 Austenitic Stainless Steel

High-pressure jet-assisted turning is an effective method to decrease the cutting force and surface roughness. Efficiency of this process is related to application of proper jet pressure proportional to other process parameters. In this research, experiments were conducted for high-pressure jet-assisted turning in finishing AISI 304 austenitic stainless steel, based on response surface method. Against the expectations, the maximum jet pressure could not lead to the most efficient results, which means that applying high-pressure jet-assisted turning without considering optimal process parameters will diminish the improving effects of high-pressure jet assistance. For this purpose, two artificial neural networks were trained by genetic algorithm to model the surface roughness and cutting force based on the process parameters. Ultimately, nondominated sorting genetic algorithm was implemented for multi-objective optimization of process. Results demonstrated that the employed method provides an effective approach that indicates optimized range of process parameters.

Multi-objective optimization of dimensional accuracy, surface roughness and hardness of hybrid investment cast components

2017 ◽  
Vol 23 (5) ◽  
pp. 845-857 ◽  
Author(s):  
Parlad Kumar Garg ◽  
Rupinder Singh ◽  
IPS Ahuja
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Surface Finish ◽  
Dimensional Accuracy ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Fused Deposition ◽  
Taguchi Design Of Experiments ◽  
Investment Cast

Purpose The purpose of this paper is to optimize the process parameters to obtain the best dimensional accuracy, surface finish and hardness of the castings produced by using fused deposition modeling (FDM)-based patterns in investment casting (IC). Design/methodology/approach In this paper, hip implants have been prepared by using plastic patterns in IC process. Taguchi design of experiments has been used to study the effect of six different input process parameters on the dimensional deviation, surface roughness and hardness of the implants. Analysis of variance has been used to find the effect of each input factor on the output. Multi-objective optimization has been done to find the combined best values of output. Findings The results proved that the FDM patterns can be used successfully in IC. A wax coating on the FDM patterns improves the surface finish and dimensional accuracy. The improved dimensional accuracy, surface finish and hardness have been achieved simultaneously through multi-objective optimization. Research limitations/implications A thin layer of wax is used on the plastic patterns. The effect of thickness of the layer has not been considered. Further research is needed to study the effect of the thickness of the wax layer. Practical implications The results obtained by the study would be helpful in making decisions regarding machining and/or coating on the parts produced by this process. Originality/value In this paper, multi-objective optimization of dimensional accuracy, surface roughness and hardness of hybrid investment cast components has been performed.

Multi-objective optimization of wire electrical discharge machining of 20MnCr5 alloy steel

2020 ◽  
Vol 17 (3) ◽  
pp. 325-333
Author(s):  
Naresh Kumar ◽  
Khushdeep Goyal
Keyword(s):  
Surface Roughness ◽  
Alloy Steel ◽  
Process Parameters ◽  
Electric Discharge ◽  
Research Work ◽  
Machining Process ◽  
Content Type ◽  
Taguchi’S Technique ◽  
Pulse Off Time ◽  
Off Time

Purpose Wire electric discharge machining (WEDM) is a non-conventional machining process, which is used to provide difficult and intricate shapes. The purpose of this research work is to apply Taguchi’s technique to optimize the process parameters in WEDM. Alloy steel 20MnCr5 has been selected as base material for experimentation. The effects of the input process parameters such as wire type, pulse-on time, pulse-off time, peak current, wire feed rate and servo voltage have been calculated on the material removal rate (MRR) and surface roughness (Ra) in WEDM operation. Design/methodology/approach In the research work, Taguchi's technique is applied to optimize the process parameters in WEDM. Findings ANOVA indicated that pulse-off time was the most significant factor for the MRR, and servo voltage was the most significant factor for surface roughness (SR). As a part of the project, 20MnCr5 was machined in wire electric discharge machine, and the optimal control parameters were found to get higher MRR and better SR using Taguchi’s technique. Originality/value To the best of authors’ knowledge, after reviewing the literature, materials including alloys of metals such as 16MnCr5 and 20MnCr5 have not been investigated so far, and research regarding machining of these materials is limited. Therefore, 20MnCr5 material has been selected for this research work to generate WEDM data.

scholarly journals Optimization of process parameters for surface roughness and tool wear in milling TC17 alloy using Taguchi with grey relational analysis

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199653
Author(s):  
Zhe Wang ◽  
Lei Li
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Process Parameters ◽  
Critical Role ◽  
Optimization Method ◽  
Ratio Method ◽  
Multi Objective Optimization ◽  
Processing Efficiency ◽  
Multi Objective ◽  
Process Guidance

To improve machining quality and processing efficiency, the Taguchi analysis method is employed to design the milling tests of titanium alloy TC17. According to results based on the signal-to-noise ratio method, the cutting depth plays a critical role in improving the surface roughness and tool wear. The grey correlation analysis is a multi-objective optimization method that can help to acquire process parameters combination of the optimal surface roughness and the optimal tool wear. Finally, the correctness of multi-objective optimization results is verified through comparison experiments. The research results can provide process guidance and data reference for the actual production processing.

Multi-Objective Optimization of Abrasive Waterjet Machining Process Parameters Using Particle Swarm Technique

2014 ◽  
Vol 1 (2) ◽  
pp. 62-79
Author(s):  
V. Murugabalaji ◽  
M. Kanthababu ◽  
J. Jegaraj ◽  
S. Saikumar
Keyword(s):  
Response Surface ◽  
Process Parameters ◽  
Particle Swarm ◽  
Mesh Size ◽  
Machining Process ◽  
Abrasive Waterjet ◽  
Depth Of Cut ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Abrasive Waterjet Machining

Multi-objective optimization is carried out for the first time to optimize abrasive water jet machining (AWJM) process parameters for graphite. Experiments are carried out by Response Surface Methodology (RSM) using Box-Behnken method. The input process parameters considered are pressure (P), traverse rate (TR) and mesh size (MS). Results are analyzed using Analysis of Variance (ANOVA) and response surface considering individually output parameters such as depth of cut (DOC) and surface roughness (Ra). ANOVA and response surface analyses indicated that similar combinations of AWJM process parameters such as high pressure (176 MPa), medium mesh size (# 100) and low traverse rate (1000 mm/min) resulted in higher depth of cut as well as lower Ra. Therefore, in order to verify the above combinations and to improve productivity, multi-objective optimization is carried out using Particle Swarm Optimization (PSO) to achieve higher depth of cut and low Ra together. From the PSO analysis, it is observed that pressure of 154 MPa, traverse rate of 1877 mm/min and mesh size of # 100 result in high depth of cut and low Ra together. The result obtained from the PSO is compared with that of ANOVA. The outcome of this study will be useful to the manufacturing engineers for selecting appropriate input AWJM process parameters for machining graphite, which has various applications such as aerospace, defence, etc.

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