Advancements in machine learning for material design and process optimization in the field of additive manufacturing

Zhou, Hao-ran; Yang, Hao; Li, Huai-qian; Ma, Ying-chun; Yu, Sen; Shi, Jian; Cheng, Jing-chang; Gao, Peng; Yu, Bo; Miao, Zhi-quan; Wei, Yan-peng

doi:10.1007/s41230-024-3145-3

Cited by 4 publications

(1 citation statement)

References 57 publications

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“…Alloy composition design: Extensive and repetitive experimentation is required for developing alloy compositions which are cost-and time-consuming, and machine learning can expedite the alloy composition design process and can develop prediction models for alloy development and properties [316][317][318][319][320]. Predicting alloy structure: ML can be used to predict the microstructural changes that occur during additive manufacturing [321][322][323].…”

Section: Summary and Future Prospects For Recycling Of Chips Made Of ...mentioning

confidence: 99%

Recent Advancements in Material Waste Recycling: Conventional, Direct Conversion, and Additive Manufacturing Techniques

Golvaskar,

Ojo,

Kannan

2024

Recycling

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To improve the microstructure and mechanical properties of fundamental materials including aluminum, stainless steel, superalloys, and titanium alloys, traditional manufacturing techniques have for years been utilized in critical sectors including the aerospace and nuclear industries. However, additive manufacturing has become an efficient and effective means for fabricating these materials with superior mechanical attributes, making it easier to develop complex parts with relative ease compared to conventional processes. The waste generated in additive manufacturing processes are usually in the form of powders, while that of conventional processes come in the form of chips. The current study focuses on the features and uses of various typical recycling methods for traditional and additive manufacturing that are presently utilized to recycle material waste from both processes. Additionally, the main factors impacting the microstructural features and density of the chip-unified components are discussed. Moreover, it recommends a novel approach for recycling chips, while improving the process of development, bonding quality of the chips, microstructure, overall mechanical properties, and fostering sustainable and environmentally friendly engineering.

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Section: Summary and Future Prospects For Recycling Of Chips Made Of ...mentioning

confidence: 99%

Recent Advancements in Material Waste Recycling: Conventional, Direct Conversion, and Additive Manufacturing Techniques

Golvaskar,

Ojo,

Kannan

2024

Recycling

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An overview of strategies for identifying manufacturing process window through design of experiments and machine learning techniques while considering the uncertainty associated with

Cabrera,

Zouhri,

Zimmer-Chevret

et al. 2024

Int J Adv Manuf Technol

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A Review of the Applications of Machine Learning for Prediction and Analysis of Mechanical Properties and Microstructures in Additive Manufacturing

Deshmankar,

Challa,

Singh

et al. 2024

Journal of Computing and Information Science in Engineering

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This article provides an insightful review of the recent applications of machine learning (ML) techniques in additive manufacturing (AM) for the prediction and amelioration of mechanical properties, as well as the analysis and prediction of microstructures. AM is the modern digital manufacturing technique adopted in various industrial sectors because of its salient features, such as the fabrication of geometrically complex and customized parts, the fabrication of parts with unique properties and microstructures, and the fabrication of hard-to-manufacture materials. The functioning of the AM processes is complicated. Several factors, such as process parameters, defects, cooling rates, thermal histories, and machine stability, have a prominent impact on AM products' properties and microstructure. To establish the relationship between these AM factors and the end product properties and microstructure is difficult. Several studies have utilized different ML techniques to optimize AM processes and predict mechanical properties and microstructure. This paper discusses the applications of various ML techniques in AM to predict mechanical properties and optimization of AM processes for the amelioration of mechanical properties of end parts. Also, ML applications for segmentation, prediction, and analysis of AM fabricated material's microstructures and acceleration of microstructure prediction procedures are discussed in this paper.

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Advancements in machine learning for material design and process optimization in the field of additive manufacturing

Cited by 4 publications

References 57 publications

Recent Advancements in Material Waste Recycling: Conventional, Direct Conversion, and Additive Manufacturing Techniques

Recent Advancements in Material Waste Recycling: Conventional, Direct Conversion, and Additive Manufacturing Techniques

An overview of strategies for identifying manufacturing process window through design of experiments and machine learning techniques while considering the uncertainty associated with

A Review of the Applications of Machine Learning for Prediction and Analysis of Mechanical Properties and Microstructures in Additive Manufacturing

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