Data-driven models for predictions of geometric characteristics of bead fabricated by selective laser melting

Le-Hong, Thai; Lin, P.-C.; Chen, Jianzhong; Pham, Thinh Duc Quy; Tran, Xuan Van

doi:10.1007/s10845-021-01845-5

Cited by 8 publications

(2 citation statements)

References 56 publications

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“…The most basic structure, which is composed of an input layer, a hidden layer and one neuron in the output layer, has proven to be an effective tool for dealing with SLM features such as SR (La Fé-Perdomo et al , 2021, 2022). In addition, some representative hyperparameters, such as the number of hidden neurons and training algorithm, were appropriately selected through Bayesian optimization (BO) (Le-Hong et al , 2021). Furthermore, a Gaussian process (GPR) model is evaluated.…”

Section: Methodsmentioning

confidence: 99%

A novel optimization framework for minimizing the surface roughness while increasing the material processing rate in the SLM process of 316L stainless steel

La Fé-Perdomo,

Ramos-Grez,

Quiza

et al. 2023

RPJ

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Purpose 316 L stainless steel alloy is potentially the most used material in the selective laser melting (SLM) process because of its versatility and broad fields of applications (e.g. medical devices, tooling, automotive, etc.). That is why producing fully functional parts through optimal printing configuration is still a key issue to be addressed. This paper aims to present an entirely new framework for simultaneously reducing surface roughness (SR) while increasing the material processing rate in the SLM process of 316L stainless steel, keeping fundamental mechanical properties within their allowable range. Design/methodology/approach Considering the nonlinear relationship between the printing parameters and features analyzed in the entire experimental space, machine learning and statistical modeling methods were defined to describe the behavior of the selected variables in the as-built conditions. First, the Box–Behnken design was adopted and corresponding experimental planning was conducted to measure the required variables. Second, the relationship between the laser power, scanning speed, hatch distance, layer thickness and selected responses was modeled using empirical methods. Subsequently, three heuristic algorithms (nonsorting genetic algorithm, multi-objective particle swarm optimization and cross-entropy method) were used and compared to search for the Pareto solutions of the formulated multi-objective problem. Findings A minimum SR value of approximately 12.83 μm and a maximum material processing rate of 2.35 mm3/s were achieved. Finally, some verification experiments recommended by the decision-making system implemented strongly confirmed the reliability of the proposed optimization methodology by providing the ultimate part qualities and their mechanical properties nearly identical to those defined in the literature, with only approximately 10% of error at the maximum. Originality/value To the best of the authors’ knowledge, this is the first study dealing with an entirely different and more comprehensive approach for optimizing the 316 L SLM process, embedding it in a unique framework of mechanical and surface properties and material processing rate.

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Section: Methodsmentioning

confidence: 99%

A novel optimization framework for minimizing the surface roughness while increasing the material processing rate in the SLM process of 316L stainless steel

La Fé-Perdomo,

Ramos-Grez,

Quiza

et al. 2023

RPJ

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show abstract

“…Despite their promising results, ANNs are commonly classified as “black box” models (Oliveira et al, 2015 ) since, for example in the context of joint strength prediction, they do not explain the underlying bonding mechanisms that give rise to increased joint performance. Furthermore, the model’s performance and its training convergence have a strong dependency on the selection of the model hyperparameters (Le-Hong et al, 2021 ). Therefore, identifying an appropriate set of hyperparameters is essential to obtain acceptable results.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimisation of ultrasonically welded dissimilar joints through machine learning

et al. 2022

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The use of composite materials is increasing in industry sectors such as renewable energy generation and storage, transport (including automotive, aerospace and agri-machinery) and construction. This is a result of the various advantages of composite materials over their monolithic counterparts, such as high strength-to-weight ratio, corrosion resistance, and superior fatigue performance. However, there is a lack of detailed knowledge in relation to fusion joining techniques for composite materials. In this work, ultrasonic welding is carried out on a carbon fibre/PEKK composite material bonded to carbon fibre/epoxy composite to investigate the influence of weld process parameters on the joint’s lap shear strength (LSS), the process repeatability, and the process induced defects. A 33 parametric study is carried out and a robust machine learning model is developed using a hybrid genetic algorithm–artificial neural network (GA–ANN) trained on the experimental data. Bayesian optimisation is employed to determine the most suitable GA–ANN hyperparameters and the resulting GA–ANN surrogate model is exploited to optimise the welding process, where the process performance metrics are LSS, repeatability and joint visual quality. The prediction for the optimal LSS was subsequently validated through a further set of experiments, which resulted in a prediction error of just 3%.

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A review of machine learning techniques for process and performance optimization in laser beam powder bed fusion additive manufacturing

Liu

Izquierdo

et al. 2022

J Intell Manuf

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Data-driven models for predictions of geometric characteristics of bead fabricated by selective laser melting

Cited by 8 publications

References 56 publications

A novel optimization framework for minimizing the surface roughness while increasing the material processing rate in the SLM process of 316L stainless steel

A novel optimization framework for minimizing the surface roughness while increasing the material processing rate in the SLM process of 316L stainless steel

Multi-objective optimisation of ultrasonically welded dissimilar joints through machine learning

A review of machine learning techniques for process and performance optimization in laser beam powder bed fusion additive manufacturing

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