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

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.

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Mechanical properties optimization of the steering column based on multi-objective optimization design

Advances in Mechanical Engineering ◽

10.1177/1687814016682941 ◽

2016 ◽

Vol 8 (12) ◽

pp. 168781401668294 ◽

Cited By ~ 1

Author(s):

Si Chen ◽

Zhaohui Wang ◽

Mi Lv

Keyword(s):

Mechanical Properties ◽

Process Parameters ◽

Optimization Design ◽

Strain Difference ◽

Optimization Method ◽

Equivalent Strain ◽

Multi Objective Optimization ◽

Optimal Process ◽

Multi Objective ◽

Initial Results

The mechanical properties of the steering column have a significant influence on the comfort and stability of a vehicle. In order for the mechanical properties to be improved, the rotary swaging process of the steering column is studied in this article. The process parameters, including axial feed rate, hammerhead speed, and hammerhead radial reduction, are systematically analyzed and optimized based on a multi-objective optimization design. The response surface methodology and the genetic algorithm are employed for optimal process parameters to be obtained. The maximum damage value, the maximum forming load, and the equivalent strain difference obtained with the optimal process parameters are, respectively, decreased by 30.09%, 7.44%, and 57.29% compared to the initial results. The comparative results present that the quality of the steering column is improved. The torque experiments and fatigue experiments are conducted with the optimal steering column. The maximum torque is measured to be 260 NM, and the service life is measured to be 2 weeks (40 NM, 2500 times), which are, respectively, increased by 8.3% and 8.69% compared to the initial results. The above results display that the mechanical properties of the steering column are optimized to verify the feasibility of the multi-objective optimization method.

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Multi-Objective Optimization of Microturning Process Parameters Using Particle Swarm Technique

Volume 2: Systems; Micro and Nano Technologies; Sustainable Manufacturing ◽

10.1115/msec2013-1011 ◽

2013 ◽

Author(s):

Nithin Tom Mathew ◽

Kanthababu Mani

Keyword(s):

Tool Wear ◽

Process Parameters ◽

Removal Rate ◽

Computational Cost ◽

Particle Swarm ◽

Depth Of Cut ◽

Multi Objective Optimization ◽

Multi Objective ◽

Confirmation Test ◽

First Time

In this work, for the first time an attempt has been made to carry out multi-objective optimization for tool based microturning process parameters using particle swarm optimization (PSO) technique. The input microturning process parameters considered are speed, feed and depth of cut. The output parameters considered are material removal rate (MRR), surface roughness (Ra) and tool wear (TW). The significant parameters are identified individually using ANOVA and main effect plots. However, it is observed that the main goal of the manufacturers is to produce high quality products in shorter interval of time. In order to meet the above objective, multi-objective optimization is carried out to achieve simultaneously higher MRR, low Ra and low TW using PSO. From the PSO analysis, it is observed that the combination of microturning parameters such as speed (18.25 m/min), feed (9.31 μm/rev) and depth of cut (14.61 μm) results in high MRR, low Ra and low tool wear. The PSO analysis indicates that it is a promising optimization algorithm due to its simplicity, low computational cost and good performance. A confirmation test was carried out to validate the predicted results.

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Multi-Objective Optimization of Process Parameters of Longitudinal Axial Threshing Cylinder for Frozen Corn Using RSM and NSGA-II

Applied Sciences ◽

10.3390/app10051646 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1646 ◽

Cited By ~ 1

Author(s):

Jun Fu ◽

Haikuo Yuan ◽

Depeng Zhang ◽

Zhi Chen ◽

Luquan Ren

Keyword(s):

Process Parameters ◽

Feed Rate ◽

Loss Rate ◽

Optimization Method ◽

Operational Performance ◽

Multi Objective Optimization ◽

Optimal Process ◽

Nsga Ii ◽

Multi Objective ◽

Damage Rate

Corn was frozen at harvest time in high-latitude areas, when corn kernel is wetter and more easily broken. When frozen corn was threshed and separated by the longitudinal axial threshing cylinder of a combine harvester, it caused a significantly high kernel damage rate and loss rate. The process parameters of threshing cylinder were optimized using RSM (response surface method) and NSGA-II (Non-Dominated Sorted Genetic Algorithm-II). The drum speed (Ds), feed rate (Fr) and concave clearance (Cc) were determined as the optimized process parameters. The loss rate (Lr) and damage rate (Dr) were indicators of operational performance. The RSM was used to establish a mathematical model between process parameters and indicators. With an elite strategy, NSGA-II was used for multi-objective optimization to obtain the optimal operational performance of the threshing cylinder. Overall, when the drum speed was selected as 384.1 rpm, the feed rate as 8.6 kg/s and the concave clearance as 40.5 mm, according to the requirement of corn harvest, the best operational performance of the longitudinal axial threshing cylinder on frozen corn was obtained. The Lr was 1.98% and the Dr was 3.49%. This result indicated that the applicability of the optimal process parameters and the optimization method of combining NSGA-II and RSM was effective for determining the optimal process parameters. This will provide an optimization method for synchronously reducing the loss rate and damage rate of grain harvesters.

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

Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering ◽

10.1177/0954408917738488 ◽

2017 ◽

Vol 232 (6) ◽

pp. 734-750 ◽

Cited By ~ 1

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.

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

Rapid Prototyping Journal ◽

10.1108/rpj-10-2015-0149 ◽

2017 ◽

Vol 23 (5) ◽

pp. 845-857 ◽

Cited By ~ 5

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.

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

Metals ◽

10.3390/met11060893 ◽

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.

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Weighted Average-Based Multi-Objective Optimization of Tube Spinning Process using Non-Traditional Optimization Techniques

International Journal of Swarm Intelligence Research ◽

10.4018/ijsir.2013070103 ◽

2013 ◽

Vol 4 (3) ◽

pp. 42-57 ◽

Cited By ~ 6

Author(s):

Pandu R Vundavilli ◽

J. Phani Kumar ◽

M.B. Parappagoudar

Keyword(s):

Surface Roughness ◽

Process Parameters ◽

Treatment Time ◽

Weighted Average ◽

Optimization Techniques ◽

Multi Objective Optimization ◽

Input Process ◽

Tube Spinning ◽

Multi Objective ◽

Spinning Process

Tube spinning is an effective process for producing long and thin walled tubes. It is important to note that the quality of parts produced in tube spinning process, namely internal surface roughness, external surface roughness, change in diameter and change in thickness depends on the right combination of input process parameters, such as mandrel rotational speed, feed rate of rollers, percentage of thickness reduction, initial thickness, solution treatment time and ageing treatment time. As the 2024 aluminum tube spinning process contains four objectives, it is very difficult to achieve a set of optimal combination of input process parameters that produce best quality product. This paper presents a weighted average-based multi-objective optimization of tube spinning process using non-traditional optimization techniques, namely genetic algorithm, particle swarm optimization and differential evolution. Multiple regression equations developed between the control factors and responses have been considered for optimization.

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Machining Process Parameters Optimization Based on Grid Optimization Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.562-564.2021 ◽

2012 ◽

Vol 562-564 ◽

pp. 2021-2025 ◽

Cited By ~ 1

Author(s):

Chao Deng ◽

Yao Xiong ◽

Yuan Hang Wang ◽

Jun Wu

Keyword(s):

Process Parameters ◽

Optimization Algorithm ◽

Optimization Method ◽

Parameters Optimization ◽

Machining Process ◽

Cnc Machine Tools ◽

Multi Objective Optimization ◽

Cnc Machine ◽

Multi Objective ◽

Grid Optimization

Machining process parameters directly affect the machining quality and efficiency of heavy-duty CNC machine tools, selecting correctly machining process parameters can improve the machine’s machining performance effectively. This paper presents a machining process parameters optimization method based on grid optimization algorithm for heavy-duty CNC machine tools. In this method, a multi-objective optimization model will be established, which considers not only the linear constrains of machining process parameters, such as machining time and machining cost, but also the non-linear constrain, such as chatter in machining process. Grid optimization algorithm will be adapted to search the optimal combination of machining process parameters from the multi-objective optimization model. In the end, this paper will present an example to verify superiority of the multi-objective optimization method by comparing with single-objective optimization method.

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