Applications of machine learning in metal powder-bed fusion in-process monitoring and control: status and challenges

Zhang, Yingjie; Yan, Wentao

doi:10.1007/s10845-022-01972-7

Cited by 24 publications

(5 citation statements)

References 137 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, intelligent detection technology utilizes on-site detection and intelligent algorithms combined with ML for image processing [ 150 ]. Thus, the sensing method, dataset preparation, feature selection and modelling algorithm are becoming focused along with the data fusion and feedback control, as shown in the framework illustrated in Figure 13 a [ 151 ].…”

Section: Intelligence In State-of-the-art Manufacturing Technology: M...mentioning

confidence: 99%

See 1 more Smart Citation

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects

et al. 2023

View full text Add to dashboard Cite

Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of artificial intelligence (AI) in the manufacturing sector has been ignored in the literature. Therefore, this review provides comprehensive information on smart mechanisms and systems emphasizing additive, subtractive and/or hybrid manufacturing processes in a collaborative, predictive, decisive, and intelligent environment. Relevant electronic databases were searched, and 248 articles were selected for qualitative synthesis. Our review suggests that significant improvements are required in connectivity, data sensing, and collection to enhance both subtractive and additive technologies, though the pervasive use of AI by machines and software helps to automate processes. An intelligent system is highly recommended in both conventional and non-conventional subtractive manufacturing (SM) methods to monitor and inspect the workpiece conditions for defect detection and to control the machining strategies in response to instantaneous output. Similarly, AM product quality can be improved through the online monitoring of melt pool and defect formation using suitable sensing devices followed by process control using machine learning (ML) algorithms. Challenges in implementing intelligent additive and subtractive manufacturing systems are also discussed in the article. The challenges comprise difficulty in self-optimizing CNC systems considering real-time material property and tool condition, defect detections by in-situ AM process monitoring, issues of overfitting and underfitting data in ML models and expensive and complicated set-ups in hybrid manufacturing processes.

show abstract

Section: Intelligence In State-of-the-art Manufacturing Technology: M...mentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects

et al. 2023

View full text Add to dashboard Cite

show abstract

“…While ML has found extensive applications in metal AM, its use in polymerbased AM techniques is still emerging and will be discussed in detail in Section 5. [98][99][100][101][102][103][104] 3.1 | Machine learning tasks ML models can perform tasks such as regression, classification, clustering, and dimensionality reduction. Each of these types of tasks is described in greater detail below, including applications in AM.…”

Section: Machine Learning Technique Categories and Tasks In Polymer A...mentioning

confidence: 99%

“…This complexity creates an opportunity for ML techniques to reduce the time and resources required to evaluate process‐structure–property‐performance relationships as compared to purely trial‐and‐error‐based experiments, numerical, and analytical models. While ML has found extensive applications in metal AM, its use in polymer‐based AM techniques is still emerging and will be discussed in detail in Section 5 98–104 …”

Section: Machine Learning Technique Categories and Tasks In Polymer A...mentioning

confidence: 99%

Application of machine learning in polymer additive manufacturing: A review

Nasrin,

Pourkamali‐Anaraki,

Peterson

2023

Journal of Polymer Science

View full text Add to dashboard Cite

Additive manufacturing (AM) is a revolutionary technology that enables production of intricate structures while minimizing material waste. However, its full potential has yet to be realized due to technical challenges such as the dependence of part quality on numerous process parameters, the vast number of design options, and the occurrence of defects. These complications may be magnified by the use of polymers and polymer composites due to their complex molecular structures, batch‐to‐batch variations, and changes in final part properties caused by small alterations in process settings and environmental conditions. Machine learning (ML), a branch of artificial intelligence, offers approaches to tackle these challenges and significantly reduce the experimental and computational time and expense. This review provides a comprehensive analysis of existing research on integrating ML techniques into polymer AM. It highlights the challenges involved in adopting ML in polymer AM, proposes potential solutions, and identifies areas for future research.

show abstract

“…Currently metal AM is approaching a paradigm shift from prototyping to end-use part fabrication. This makes quality assurance become a prominent and urgent issue to be solved [3]. Numerous studies have been conducted to mitigate this effect by establishing predictive relationships between process parameters or post-detection data and mechanical properties.…”

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.

View full text Add to dashboard Cite

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.

show abstract

Applications of machine learning in metal powder-bed fusion in-process monitoring and control: status and challenges

Cited by 24 publications

References 137 publications

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects

Application of machine learning in polymer additive manufacturing: A review

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

Contact Info

Product

Resources

About