Response surface methodology-based optimization of parameters and cutting quality of 22MnB5 plates by UV picosecond laser layered cutting

Bian, Hairong; Wang, Zhao; Zhang, Hongmei; Bu, Xingyu; Lu, Haifei; Luo, Kaiyu; Lu, Jinzhong

doi:10.1016/j.optlastec.2024.111197

Optics & Laser Technology

2024

DOI: 10.1016/j.optlastec.2024.111197

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Response surface methodology-based optimization of parameters and cutting quality of 22MnB5 plates by UV picosecond laser layered cutting

Hairong Bian,

Zhao Wang,

Hongmei Zhang

et al.

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Cited by 3 publications

References 35 publications

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Recent research and advances in Injection-Seeded lasers across Wavelengths: 1, 1.6, and 2 µm

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Investigation of excellence in Nd-YAG laser cutting of Al6061-T6 thin sheet using GRA coupled with PCA

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Cutting flying materials with minimum deviations and metallurgical damages is always preferred for airframe work in aircraft industries. In the present scenario, the laser cutting process has become the preferred choice for fine cutting of sheet materials with close tolerances. This paper reports the investigation of excellence in pulsed laser cutting of Al-alloy (Al6061-T6) sheet. Five quality characteristics for quantifying the dimensional accuracy (i.e., kerf width and deviation at the top and bottom side) and metallurgical damage (i.e., heat-affected zone) have been considered for the proposed work. These characteristics depend on the lamp current, laser parameters (pulse width and frequency), and relative motion between the sheetmetal and laser source. The experiments have been performed using the design of experiments technique. Further, the results of laser cutting have been optimized by using grey relational analysis (GRA) coupled with principal component analysis (PCA). The application of GRA-PCA is found capable of optimizing all five process responses at a time. The unevenness within the kerf is reduced by 22% and 32% at the top and bottom sides, respectively.

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An Advanced Approach for Predicting Workpiece Surface Roughness Using Finite Element Method and Image Processing Techniques

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Li,

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et al. 2024

Machines

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Workpiece surface quality is a critical metric for assessing machining quality. However, due to the complex coupling characteristics of cutting factors, accurately predicting surface roughness remains challenging. Typically, roughness is measured post-machining using specialized instruments, which delays feedback and hampers timely problem detection, ultimately resulting in cutting resource wastage. To address this issue, this paper introduces a predictive model for workpiece surface roughness based on the finite element (FE) method and advanced image processing techniques. Initially, an orthogonal turning experiment was designed, and an FE cutting model was constructed to assess the distribution of cutting forces and temperatures under varying cutting parameters. Image processing methods (including mesh calibration, edge extraction, and contour fitting) were then applied to extract surface characteristics from the FE simulation outputs, yielding preliminary estimates of surface roughness. By employing range and regression analyses methods, this study quantitatively evaluates the interdependencies among cutting parameters, forces, temperatures, and roughness, subsequently formulating a multivariate regression model to predict surface roughness. Finally, a turning experiment under actual working conditions was conducted, confirming the model’s capacity to predict the trend with an accuracy of 85.07%. Thus, the proposed model provides a precise predictive tool for surface roughness, offering valuable guidance for optimizing machining parameters and supporting proactive control in the turning process, ultimately enhancing machining efficiency and quality.

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Response surface methodology-based optimization of parameters and cutting quality of 22MnB5 plates by UV picosecond laser layered cutting

Cited by 3 publications

References 35 publications

Recent research and advances in Injection-Seeded lasers across Wavelengths: 1, 1.6, and 2 µm

Recent research and advances in Injection-Seeded lasers across Wavelengths: 1, 1.6, and 2 µm

Investigation of excellence in Nd-YAG laser cutting of Al6061-T6 thin sheet using GRA coupled with PCA

An Advanced Approach for Predicting Workpiece Surface Roughness Using Finite Element Method and Image Processing Techniques

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