Predicting laser powder bed fusion defects through in-process monitoring data and machine learning

Feng, Shuo; Chen, Zhuoer; Bircher, Benjamin A.; Ji, Ze; Nyborg, Lars; Bigot, Samuel

doi:10.1016/j.matdes.2022.111115

Cited by 29 publications

(7 citation statements)

References 35 publications

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“…By using validity indices, a suitable number of classes can be determined [ 35 , 36 ]. Due to its widespread popularity and user-friendly nature, this algorithm is becoming increasingly prevalent in various fields [ 25 ]. As physical processes are always subject to a certain amount of noise, the second chosen model is the DBSCAN algorithm.…”

Section: Methodsmentioning

confidence: 99%

“…This entails combining in-process optical tomography images with the initial part geometry and post-processing X-ray computer tomography data. Through this integration, the real-time formation of defects during the manufacturing process can be monitored, enabling the derivation of insights for optimizing processing parameters [ 25 ]. Splashes and delaminations were identified by Baumgartl et al [ 26 ] via a convolutional neural network on thermographic image data.…”

Section: Introductionmentioning

confidence: 99%

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Defect Classification for Additive Manufacturing with Machine Learning

Altmann,

Benthien,

Ellendt

et al. 2023

Materials

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Additive manufacturing offers significant design freedom and the ability to selectively influence material properties. However, conventional processes like laser powder bed fusion for metals may result in internal defects, such as pores, which profoundly affect the mechanical characteristics of the components. The extent of this influence varies depending on the specific defect type, its size, and morphology. Furthermore, a single component may exhibit various defect types due to the manufacturing process. To investigate these occurrences with regard to other target variables, this study presents a random forest tree model capable of classifying defects in binary images derived from micrographs. Our approach demonstrates a classification accuracy of approximately 95% when distinguishing between keyhole and lack of fusion defects, as well as process pores. In contrast, unsupervised models yielded prediction accuracies below 60%. The model’s accuracy in differentiating between lack of fusion and keyhole defects varies based on the manufacturing process’s parameters, primarily due to the irregular shapes of keyhole defects. We provide the model alongside this paper, which can be utilized on a standard computer without the need for in situ monitoring systems during the additive manufacturing process.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Defect Classification for Additive Manufacturing with Machine Learning

Altmann,

Benthien,

Ellendt

et al. 2023

Materials

View full text Add to dashboard Cite

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“…The study in [39] focused on monitoring multilayer optical tomography images to predict local porosity in additive manufacturing. Random forest (RF) was utilized for porosity prediction, providing interpretability, and identifying the most important features (layers) contributing to the prediction.…”

Section: Process Optimizationmentioning

confidence: 99%

A Systematic Literature Review on Artificial Intelligence and Explainable Artificial Intelligence for Visual Quality Assurance in Manufacturing

Hoffmann,

Reich

2023

Electronics

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Quality assurance (QA) plays a crucial role in manufacturing to ensure that products meet their specifications. However, manual QA processes are costly and time-consuming, thereby making artificial intelligence (AI) an attractive solution for automation and expert support. In particular, convolutional neural networks (CNNs) have gained a lot of interest in visual inspection. Next to AI methods, the explainable artificial intelligence (XAI) systems, which achieve transparency and interpretability by providing insights into the decision-making process of the AI, are interesting methods for achieveing quality inspections in manufacturing processes. In this study, we conducted a systematic literature review (SLR) to explore AI and XAI approaches for visual QA (VQA) in manufacturing. Our objective was to assess the current state of the art and identify research gaps in this context. Our findings revealed that AI-based systems predominantly focused on visual quality control (VQC) for defect detection. Research addressing VQA practices, like process optimization, predictive maintenance, or root cause analysis, are more rare. Least often cited are papers that utilize XAI methods. In conclusion, this survey emphasizes the importance and potential of AI and XAI in VQA across various industries. By integrating XAI, organizations can enhance model transparency, interpretability, and trust in AI systems. Overall, leveraging AI and XAI improves VQA practices and decision-making in industries.

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“…In LPBF, the variation of features in space and time is considered to be very important. Feng et al [21] showed that process deviations do not necessarily lead to defects, or that defects can be repaired by following layers. Therefore, to predict the formation of pores in a layer, it is necessary to analyze OT images of the current layer and several subsequent layers.…”

Section: Introductionmentioning

confidence: 99%

Prediction of mechanical properties of LPBF built part based on process monitoring and Gaussian process regression

Yuan,

Peng,

et al. 2024

Meas. Sci. Technol.

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As a highly promising technology in additive manufacturing, the laser powder bed fusion (LPBF) has only limited application due to its low reproducibility. In this study, the image information of the 316L specimen after laser scanning and powder paving of each layer was acquired by a CMOS industrial camera. The important features were selected,extrated and quantificated by analyzing the tensile test results. Finally,combined with the laser power, the quantified features were as input of a Gaussian process regression model based on optimisation algorithm of grid search to predict the tensile strength of 316L specimen. The results show that the quantized image features have a significant improvement on the regression effect, and the coefficient of determination(R²) is improved from 63% to 90.57% compared to using only the laser power as input.

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Predicting laser powder bed fusion defects through in-process monitoring data and machine learning

Cited by 29 publications

References 35 publications

Defect Classification for Additive Manufacturing with Machine Learning

Defect Classification for Additive Manufacturing with Machine Learning

A Systematic Literature Review on Artificial Intelligence and Explainable Artificial Intelligence for Visual Quality Assurance in Manufacturing

Prediction of mechanical properties of LPBF built part based on process monitoring and Gaussian process regression

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