Study of the surface roughness and optimization of machining parameters during laser-assisted fast tool servo machining of glass-ceramic

Fan, Mingxu; Zhou, Xiaoqin; Chen, Shunfa; Jiang, Shan; Song, Jun-Feng

doi:10.1088/2051-672x/acd5ec

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…The average disagreement for the output response of the experimental results in each parameter context is described in the OA matrix [18]. Analysis of the response was then performed using the Signal to Noise Ratio (SNR).…”

Section: Figure 1: Illustration Of the Milling Process In Cnc Machiningmentioning

confidence: 99%

Design Parameters Optimization in CNC Machining Based on Taguchi, ANOVA, and Screening Method

2023

JMechE

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Future manufacturing requires a process to achieve high productivity with high-quality products. Appropriate and optimum machining parameters during machining operation are essential to enhance the surface quality (Ra). This study investigated a design for machining parameter optimization to reduce time and cost with minimum experiments using the Taguchi method via the screening method. In this study, the machining parameters were the cut speed (vc), fed speed (vf), cut depth (doc), cut width (woc), and flute (z). The response analysed process was done on Aluminium alloy AA6061 via a high-speed computer numerical control machine (HSM) employing an end mill cutter in dry cutting. Analysis of Variance (ANOVA) of parameters combination applied during the process was used to analyse the results from data obtained and via the screening. Based on the result of ANOVA indicated that vf showed a greater F-value which meant vf had statistical significance for the terms and model. It was concluded that through the confirmation test, the optimal machining parameters vc at 220 rpm, vf at 150 mm/m, doc of 0.5 mm, woc at 4 mm, and z at 4 flutes, with Ra was around 0.122 - 0.127 which was achieved the requirement of standard for industries in polishing.

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Section: Figure 1: Illustration Of the Milling Process In Cnc Machiningmentioning

confidence: 99%

Design Parameters Optimization in CNC Machining Based on Taguchi, ANOVA, and Screening Method

2023

JMechE

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Study on tool wear and optimization of machining parameters in laser-assisted fast tool servo machining of glass-ceramic

Fan,

Zhou,

Song

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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Glass-ceramic is difficult to be ultra precision machined due to its high hardness and brittleness. Laser-assisted fast tool servo machining (LAFTSM) of glass-ceramic optical free-form surface was carried out with tool wear as the characteristic value to study the machining quality of glass-ceramic. Orthogonal experiments on LAFTSM were conducted using the Taguchi method (TM). The range of tool wear reduction obtained by comparing laser-assisted machining (LAM) with fast tool servo (FTS) machining is 48.83%–64.12%. The order of contribution of each machining parameter obtained through variance analysis to the reduction of tool wear is: spindle speed > laser power > feed rate > piezoelectric frequency. The optimal combination of machining parameters that can minimize tool wear obtained through signal-to-noise ratio (S/N) analysis is: spindle speed 55 rpm, feed rate 0.01 mm/rev, piezoelectric frequency 8 Hz, laser power 75 W. Artificial neural network (ANN) and genetic algorithm (GA) were used to fit and optimize the machining parameters and experimental results in TM orthogonal experiments. The fitting values of ANN are highly consistent with the orthogonal experimental results. The optimal combination of machining parameters obtained after GA optimization analysis is: spindle speed 50 rpm, feed rate 0.015 mm/rev, piezoelectric frequency 4 Hz, laser power 75 W. Experiments were conducted using the optimal combination of machining parameters of TM and ANN, the results showed that ANN performs better than TM in predicting minimum tool wear and optimizing machining parameters. This study provides a reference for LAFTSM and the research methods of tool wear.

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Optimization Algorithms and Their Applications and Prospects in Manufacturing Engineering

Song,

Wang,

Hao

2024

Materials

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In modern manufacturing, optimization algorithms have become a key tool for improving the efficiency and quality of machining technology. As computing technology advances and artificial intelligence evolves, these algorithms are assuming an increasingly vital role in the parameter optimization of machining processes. Currently, the development of the response surface method, genetic algorithm, Taguchi method, and particle swarm optimization algorithm is relatively mature, and their applications in process parameter optimization are quite extensive. They are increasingly used as optimization objectives for surface roughness, subsurface damage, cutting forces, and mechanical properties, both for machining and special machining. This article provides a systematic review of the application and developmental trends of optimization algorithms within the realm of practical engineering production. It delves into the classification, definition, and current state of research concerning process parameter optimization algorithms in engineering manufacturing processes, both domestically and internationally. Furthermore, it offers a detailed exploration of the specific applications of these optimization algorithms in real-world scenarios. The evolution of optimization algorithms is geared towards bolstering the competitiveness of the future manufacturing industry and fostering the advancement of manufacturing technology towards greater efficiency, sustainability, and customization.

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Study of the surface roughness and optimization of machining parameters during laser-assisted fast tool servo machining of glass-ceramic

Cited by 3 publications

References 40 publications

Design Parameters Optimization in CNC Machining Based on Taguchi, ANOVA, and Screening Method

Design Parameters Optimization in CNC Machining Based on Taguchi, ANOVA, and Screening Method

Study on tool wear and optimization of machining parameters in laser-assisted fast tool servo machining of glass-ceramic

Optimization Algorithms and Their Applications and Prospects in Manufacturing Engineering

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