Analysis of the optimisation of parameters for the laser machining of timber

Deng, Chyn-Shu; Hsia, Kuo-Lun

doi:10.1007/s00170-016-9341-6

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…Analysis of variance (ANOVA) serves as a robust statistical tool for discerning the significance of various effects. Combining the S/N ratio method with ANOVA has emerged as an innovative approach in materials machining optimization research [37,[40][41][42]. Subsequent sections of this paper will extend the ANOVA analysis to gauge the impact of different machining variables on cutting force.…”

Section: Multivariate Analysismentioning

confidence: 99%

“…Likewise, k g2 , k g3 , and k g4 are calculated similarly. CT denotes the correction number, computed as detailed in references [42,43].…”

Section: Multivariate Analysismentioning

confidence: 99%

“…Subsequent calculations and results are outlined in Table7. Equations for k g1 follow the procedure described in references[42,43]: k g1 = 932.99 + 1071.53 + 1047.71 + 1027.78 = 4080.01…”

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confidence: 99%

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Finite Element Modeling and Optimization Analysis of Cutting Force in Powder Metallurgy Green Compacts

Yang,

Zhang,

Wang

et al. 2023

Processes

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Powder metallurgy (PM) is a manufacturing technique that employs metal powder as the raw material, which is then molded and sintered to produce various products. PM green compacts are inherently weak, rendering them prone to damage during machining due to cutting forces, which also affect the quality of the machined surface. To study the impact of different machining variables on cutting force, a finite element simulation (FEM) was employed, focusing on cutting thickness, cutting speed, tool rake angle, and rounded edge radius. The results indicated that cutting thickness had a highly significant impact on cutting force, while the rounded-edge radius and cutting speed were also significant factors. The tool rake angle was found to have minimal effects. The optimal parameters for minimizing cutting force were identified: a cutting thickness of 0.20 mm, a cutting speed of 120 m/min, a tool rake angle of 0°, and a rounded-edge radius of 40 μm, which reduced the cutting force to 887.95 N.

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Section: Multivariate Analysismentioning

confidence: 99%

“…Likewise, k g2 , k g3 , and k g4 are calculated similarly. CT denotes the correction number, computed as detailed in references [42,43].…”

Section: Multivariate Analysismentioning

confidence: 99%

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Finite Element Modeling and Optimization Analysis of Cutting Force in Powder Metallurgy Green Compacts

Yang,

Zhang,

Wang

et al. 2023

Processes

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Optimization of user experience in mobile application design by using a fuzzy analytic-network-process-based Taguchi method

Zhu

2019

Applied Soft Computing

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Simulation and Algorithmic Optimization of the Cutting Process for the Green Machining of PM Green Compacts

Zhang,

Yang,

Zeng

et al. 2024

Materials

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Powder metallurgy (PM) technology is extensively employed in the manufacturing sector, yet its processing presents numerous challenges. To alleviate these difficulties, green machining of PM green compacts has emerged as an effective approach. The aim of this research is to explore the deformation features of green compacts and assess the impact of various machining parameters on the force of cutting. The cutting variables for compacts of PM green were modeled, and the cutting process was analyzed using Abaqus (2022) software. Subsequently, the orthogonal test ANOVA method was utilized to evaluate the significance of each parameter for the cutting force. Optimization of the machining parameters was then achieved through a genetic algorithm for neural network optimization. The investigation revealed that PM green compacts, which are brittle, undergo a plastic deformation stage during cutting and deviate from the traditional model for brittle materials. The findings indicate that cutting thickness exerts the most substantial influence on the cutting force, whereas the speed of cutting, the tool rake angle, and the radius of the rounded edge exert minimal influence. The optimal parameter combination for the cutting of PM green compacts was determined via a genetic algorithm for neural network optimization, yielding a cutting force of 174.998 N at a cutting thickness of 0.15 mm, a cutting speed of 20 m/min, a tool rake angle of 10°, and a radius of the rounded edge of 25 μm, with a discrepancy of 4.05% from the actual measurement.

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Analysis of the optimisation of parameters for the laser machining of timber

Cited by 4 publications

References 10 publications

Finite Element Modeling and Optimization Analysis of Cutting Force in Powder Metallurgy Green Compacts

Finite Element Modeling and Optimization Analysis of Cutting Force in Powder Metallurgy Green Compacts

Optimization of user experience in mobile application design by using a fuzzy analytic-network-process-based Taguchi method

Simulation and Algorithmic Optimization of the Cutting Process for the Green Machining of PM Green Compacts

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