Data-driven multi-objective optimization of laser welding parameters of 6061-T6 aluminum alloy

Wu, Jianzhao; Zhang, Shuaikun; Sun, Jiahao; Zhang, Chaoyong

doi:10.1088/1742-6596/1885/4/042007

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…It is clear that the main obstacle for achieving full a priori multi-objective optimization capability in AM is the availability of accurate and reliable models for the optimization function. It is also possible that one can carry out multi-objective optimization studies in welding [193] or non-metal AM [194] and subsequently implement similar methodologies into metal AM.…”

Section: Overview Of Optimization Algorithmsmentioning

confidence: 99%

Process parameter optimization of metal additive manufacturing: a review and outlook

Chia¹,

Wu²,

Wang³

et al. 2022

J Mater Inf

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The selection of appropriate process parameters is crucial in metal additive manufacturing (AM) as it directly influences the defect formation and microstructure of the printed part. Over the past decade, research efforts have been devoted to identifying "optimal" processing regimes for different materials to achieve defect-free manufacturing, which mostly involve costly trial-and-error experiments and computationally expensive mechanistic simulations. Hence, it is apropos to critically review the methods used to achieve the optimal process parameters in AM. This work seeks to provide a structured analysis of current methodologies and discuss systematic approaches toward general optimization work in AM and the process parameter optimization of new AM alloys. A brief review of process-induced defects due to process parameter selection is given and the current methods for identifying "optimal processing windows" are summarized. Research works are analyzed under a standard optimization framework, including the design of experiments and characterization, modelling and optimization algorithms. The research gaps that preclude multi-objective optimization in AM are identified and future directions toward optimization work in AM are discussed. With growing capabilities in AM, we should reconsider what the definition of the "optimal processing region".

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Section: Overview Of Optimization Algorithmsmentioning

confidence: 99%

Process parameter optimization of metal additive manufacturing: a review and outlook

Chia¹,

Wu²,

Wang³

et al. 2022

J Mater Inf

Self Cite

View full text Add to dashboard Cite

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“…Gaussian Process Regression (GPR) has also been adopted in the literature to model the effect of process parameters on weld bead geometry of stainless steel 21 and of aluminium alloys 22 …”

Section: Introductionmentioning

confidence: 99%

“…Indeed, similar approaches have been deployed to other welding processes, for example, spot welding by Satpathy et al 19 and friction stir welding by Gagliardi et al 20 Gaussian Process Regression (GPR) has also been adopted in the literature to model the effect of process parameters on weld bead geometry of stainless steel 21 and of aluminium alloys. 22 Kernel-based regression models have also been adopted to study the effect of process parameters on penetration depth and mechanical properties. 7,8 For example, Petković 23 exploited support vector machine regression (SVM or SVR) to model the geometry and the resistance of the weld based on laser welding process parameters, including clamping conditions.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of laser welding of deep drawing steel for automotive applications by Machine Learning: A comparison of different techniques

Maculotti

Genta

Galetto

2023

Quality & Reliability Eng

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Laser welding is particularly relevant in the industry thanks to its simplicity, flexibility and final quality. The industry 4.0 and sustainable manufacturing framework gives massive attention to in situ and non‐destructive inspection methods to predict laser weld final quality. Literature often resorts to supervised Machine Learning approaches. However, selecting the ApTest method is non‐trivial and often decision making relies on diverse and unclearly defined criteria. This work addresses this task by proposing a statistical comparison method based on nonparametric tests. The method is applied to the most relevant supervised Machine Learning approaches exploited in literature to predict laser weld quality, specifically, considering the optimisation of a new production line, hence focussing on supervised Machine Learning methods that do not require massive data set, that is, Generalized Linear Model (GLM), Gaussian Process Regression, Support Vector Machine, Classification and Regression Tree, and Genetic Algorithms. The statistical comparison is carried out to select the best‐performing model, which is then exploited to optimise the production process. Additionally, an automatic process to optimise Machine Learning models and process parameters is resorted to, basing on Bayesian approaches, to reduce operator effect. This work provides quality and process engineers with a simple framework to compare Machine Learning approaches performances and select the most suitable process modelling technique.

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“…The basic goal of MOGA is to find the best Pareto front in the objective space so that no more improvements in any fitness function can be made without affecting others (Li et al , 2015). This optimization technique gives an optimal combination of input parameters with optimal response value and Pareto fronts (Yang et al , 2018; Li et al , 2022; Jiang et al , 2016; Sathiya et al , 2012; Wu et al , 2021).…”

Section: Introductionmentioning

confidence: 99%

Modelling and optimization of laser welding of Al2024 aluminium alloy

Dey

Duggirala

Mitra

2023

WJE

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Purpose Aluminium alloys can be used as lightweight and high-strength materials in combination with the technology of laser beam welding, an efficient joining method, in the manufacturing of automotive parts. The purposes of this paper are to conduct laser welding experiments with Al2024 in the lap joint configuration, model the laser welding process parameters of Al2024 alloys and use propounded models to optimize the process parameters. Design/methodology/approach Laser welding of Al2024 alloy has been conducted in the lap joint configuration. Then, the influences of explanatory variables (laser peak power, scanning speed and frequency) on outcome variables (weld width [WW], throat length [TL] and breaking load [BL]) have been investigated with Poisson regression analysis of the data set derived from experimentation. Thereafter, a multi-objective genetic algorithm (MOGA) has been used using MATLAB to find the optimum solutions. The effects of various input process parameters on the responses have also been analysed using response surface plots. Findings The promulgated statistical models, derived with Poisson regression analysis, are evinced to be well-fit ones using the analysis of deviance approach. Pareto fronts have been used to demonstrate the optimization results, and the maximized load-bearing capacity is computed to be 1,263 N, whereas the compromised WW and TL are 714 µm and 760 µm, respectively. Originality/value This work of conducting laser welding of lap joint of Al2024 alloy incorporating the Taguchi method and optimizing the input process parameters with the promulgated statistical models proffers a neoteric perspective that can be useful to the manufacturing industry.

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Data-driven multi-objective optimization of laser welding parameters of 6061-T6 aluminum alloy

Cited by 5 publications

References 11 publications

Process parameter optimization of metal additive manufacturing: a review and outlook

Process parameter optimization of metal additive manufacturing: a review and outlook

Optimisation of laser welding of deep drawing steel for automotive applications by Machine Learning: A comparison of different techniques

Modelling and optimization of laser welding of Al2024 aluminium alloy

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