Multi-Objective Optimization Design of 6-UPS Parallel Mechanism Based on Taguchi Method and Entropy-Weighted Gray Relational Analysis

Song, Hao; Chen, Xiaoliang; Zhang, Shuai; Xu, Liyou

doi:10.3390/app12125836

Cited by 5 publications

(1 citation statement)

References 54 publications

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“…The computation of the grey relational grade, which serves as the foundation for the comprehensive assessment of multiple performance features, is the next stage of the grey relational analysis. Researchers typically use a weight to subjectively emphasize or de-emphasize a goal that lacks a sound mathematical foundation or they use an equal weight to decide the grey relational grade of numerous responses [33]. Assuming equal effects from the performance variables Ra, MRR, VB, and CT in this case study, each feature had an equal relative weight or relevance.…”

Section: Grey Relational Grade and Rank Determinationmentioning

confidence: 99%

Optimization of Multiple Performance Characteristics for CNC Turning of Inconel 718 Using Taguchi–Grey Relational Approach and Analysis of Variance

Zhujani,

Abdullahu,

Todorov

et al. 2024

Metals

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The optimization of machining processes is a deciding factor when increasing productivity and ensuring product quality. The response characteristics, such as surface roughness, material removal rate, tool wear, and cutting time, of the finish turning process have been simultaneously optimized. We used the Taguchi-based design of experiments L9(34) in this study to test and find the best values for process parameters like cutting speed, feed rate, depth of cut, and nose radius. The Taguchi-based multi-objective grey relational approach (GRA) method was used to address the turning problem of Inconel 718 alloy to increase productivity, i.e., by simultaneously minimizing surface roughness, tool wear, and machining time. GRA and the S/N ratio derived from the Taguchi approach were utilized to combine many response characteristics into a single response. The grey relational grade (GRG) produces results such as estimations of the optimal level of input parameters and their proportional significance to specific quality characteristics. By employing ANOVA, the significance of parameters with respect to individual responsibility and the overall quality characteristics of the cutting process were ascertained. The single-objective optimization yielded the following results: minimal surface roughness of 0.167 µm, tool wear of 44.65 µm, minimum cutting time of 19.72 s, and maximum material speed of 4550 mm3/min. While simultaneously optimizing the Inconel 718 superalloy at a cutting speed of 100 m/min, depth of cut of 0.4 mm, feed rate of 0.051 mm/rev, and tool nose radius of 0.4 mm, the results of the multi-objective optimization showed that all investigated response characteristics reached their optimal values (minimum/maximum). To validate the results, confirmatory experiments with the most favorable outcomes were conducted and yielded a high degree of concurrence.

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Section: Grey Relational Grade and Rank Determinationmentioning

confidence: 99%

Optimization of Multiple Performance Characteristics for CNC Turning of Inconel 718 Using Taguchi–Grey Relational Approach and Analysis of Variance

Zhujani,

Abdullahu,

Todorov

et al. 2024

Metals

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Combined forecasting approach for product quality based on support vector regression and gray forecasting model

Lian

Liu

et al. 2023

Advanced Engineering Informatics

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Application of machine learning and grey Taguchi technique for the development and optimization of a natural fiber hybrid reinforced polymer composite for aircraft body manufacture

Esangbedo,

Samuel

2024

Oxford Open Materials Science

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The rapid expansion of the air transport industry raises significant sustainability concerns due to its substantial carbon emissions and contribution to global climate change. These emissions are closely linked to fuel consumption, which in turn is influenced by the weight of materials used in aircraft systems. This study extensively applied machine learning tools for the optimization of natural fiber-reinforced composite material production parameters for aircraft body application. The Taguchi optimization technique was used to study the effect of sisal fibers, glass fibers, fiber length, and NaOH treatment concentration on the performance of the materials. Multi-objective optimization methods like the grey relational system and genetic algorithm (using the MATLAB programming interface) were employed to obtain the best combination of the studied factors for low fuel consumption (low carbon emission) and high-reliability structural applications of aircraft. The models developed from regressional analysis had high accuracy of prediction, with R-Square values all > 80%. Optimization of the grey relational model of the developed composite using the genetic algorithm showed the best process parameter to achieve low weight material for aircraft application to be 40% sisal, 5% glass fiber at 35mm fiber length, and 5% NaOH concentration with grey relational analysis at the highest possible level, which is unity.

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Multi-Objective Optimization Design of 6-UPS Parallel Mechanism Based on Taguchi Method and Entropy-Weighted Gray Relational Analysis

Cited by 5 publications

References 54 publications

Optimization of Multiple Performance Characteristics for CNC Turning of Inconel 718 Using Taguchi–Grey Relational Approach and Analysis of Variance

Optimization of Multiple Performance Characteristics for CNC Turning of Inconel 718 Using Taguchi–Grey Relational Approach and Analysis of Variance

Combined forecasting approach for product quality based on support vector regression and gray forecasting model

Application of machine learning and grey Taguchi technique for the development and optimization of a natural fiber hybrid reinforced polymer composite for aircraft body manufacture

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