Optimization of aluminum alloy by CO<sub>2</sub> laser cutting using genetic algorithm to achieve surface quality

Senthilkumar, V.; Nagadeepan, Anbazhagan; Raj, L. Hubert Tony; Sabarish, P.; Stonier, Albert Alexander

doi:10.1088/1757-899x/1055/1/012123

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Necip Fazil and co-researchers have used artificial neural networks (ANNs) among other methods, achieving a high precision rate in predicting the ultimate tensile strength of welded joints [10]. SenthilKumar V et al have proposed a genetic algorithm for optimizing laser cutting parameters, demonstrating a meticulous evaluation of the effects on the quality of cuts [11].…”

Section: Introductionmentioning

confidence: 99%

Research on Predicting Welding Deformation in Automated Laser Welding Processes with an Enhanced DEWOA-BP Algorithm

Zhang,

Hu,

et al. 2024

Machines

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Welding stands as a critical focus for the intelligent and digital transformation of the machinery industry, with automated laser welding playing a pivotal role in the sector’s technological advancement. The management of welding deformation in such operations is fundamental, relying on advanced analysis and prediction methods. The endeavor to accurately analyze welding deformation in practical applications is compounded by the interplay of numerous variables, a pronounced coupling effect among these factors, and a reliance on expert intuition. Thus, effective deformation control in automated laser welding operations necessitates the gathering of pre-test laser welding data to develop a predictive approach that accurately reflects real-world conditions and is characterized by improved reliability and stability. To address the technological evolution in automated laser welding, a predictive model based on neural network technology is proposed to map the intricate relationship between process variables and the resulting deformation. At the heart of this approach is the formulation of a predictive model utilizing a back-propagation neural network (BP), with an emphasis on four essential welding parameters: speed, peak power, duty cycle, and defocusing amount. The model’s predictive accuracy is then honed through the application of the whale optimization algorithm (WOA) and the differential evolutionary (DE) algorithm. Finally, extensive testing in an automated laser welding experimental setup is conducted to validate the accuracy and reliability of the proposed prediction model. It is demonstrated through these experiments that the deformation prediction model, enhanced by the DEWOA-BP neural network, accurately forecasts the relationship between laser welding parameters and the induced deformation, maintaining a prediction error margin of ±0.1mm. The model is employed to fulfill the requirements for a pre-welding quality evaluation, thereby facilitating a more calculated and informed approach to welding operations. This method of intelligent prediction is not only crucial for the intelligent transformation of laser welding but also holds significant implications for traditional machining technologies such as milling, grinding, and spraying. It offers innovative ideas and methods that are pivotal for the industrial revolution and technological advancement of the traditional machining industry.

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Section: Introductionmentioning

confidence: 99%

Research on Predicting Welding Deformation in Automated Laser Welding Processes with an Enhanced DEWOA-BP Algorithm

Zhang,

Hu,

et al. 2024

Machines

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A comprehensive review of studying the influence of laser cutting parameters on surface and kerf quality of metals

Alsaadawy,

Dewidar,

Said

et al. 2023

Int J Adv Manuf Technol

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Sheet metals such as titanium alloys, steel alloys, and aluminum alloys are significant materials due to their importance among everyday life products as well as high-strength applications in aircraft, ships, automobiles, construction, military, and marine purposes. Recently, laser cutting is one of the best and fastest non-conventional methods to cut sheet metals, so it is necessary to understand how laser cutting parameters affect cutting quality. A comprehensive review was presented to investigate how laser cutting parameters affect the cut surface and kerf quality and which parameters affect cutting quality the most. An overview of the advantages of laser cutting when compared to other methods of machining was presented. In addition, a description of the laser cutting method and the different sources of laser were presented with clearing the range of thicknesses of the cut material for each source and their advantages. Also, a description of the properties and the applications of the studied materials were discussed. The performance parameters ofcutting were illustrated in detail by graphs and equations. The research analysis and discussion were discussed in such organized details by tables and graphs which show the full classification of the studied papers. It was found that the best conditions to obtain low surface roughness, small HAZ width, small kerf width, and small kerf angle are using low laser power, high cutting speed, medium gas pressure, high standoff distance, medium pulse frequency, medium pulse width, small nozzle diameter, small thickness, and nitrogen as an assist gas.

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Optimization of aluminum alloy by CO₂ laser cutting using genetic algorithm to achieve surface quality

Cited by 2 publications

References 17 publications

Research on Predicting Welding Deformation in Automated Laser Welding Processes with an Enhanced DEWOA-BP Algorithm

Research on Predicting Welding Deformation in Automated Laser Welding Processes with an Enhanced DEWOA-BP Algorithm

A comprehensive review of studying the influence of laser cutting parameters on surface and kerf quality of metals

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Optimization of aluminum alloy by CO2 laser cutting using genetic algorithm to achieve surface quality

Cited by 2 publications

References 17 publications

Research on Predicting Welding Deformation in Automated Laser Welding Processes with an Enhanced DEWOA-BP Algorithm

Research on Predicting Welding Deformation in Automated Laser Welding Processes with an Enhanced DEWOA-BP Algorithm

A comprehensive review of studying the influence of laser cutting parameters on surface and kerf quality of metals

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Optimization of aluminum alloy by CO₂ laser cutting using genetic algorithm to achieve surface quality