Machine-learning-based monitoring and optimization of processing parameters in 3D printing

Tamir, Tariku Sinshaw; Xiong, Gang; Fang, Qihang; Yang, Yong; Shen, Zhongyao; Zhou, MengChu; Jiang, Jingchao

doi:10.1080/0951192x.2022.2145019

Cited by 44 publications

(15 citation statements)

References 51 publications

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“…Utilizing a model predictive scheme that predicts future states based on prior experiences might help to overcome these restrictions, especially when the target’s state fluctuation is highly organized. An approach of both the quasi-periodic efficiency in detection and deformation could be created when 3D bioprinting on organs utilizing machine-learning algorithms based on previous input data. , …”

Section: Scaffold Engineeringmentioning

confidence: 99%

Artificial Intelligence-Based 3D Printing Strategies for Bone Scaffold Fabrication and Its Application in Preclinical and Clinical Investigations

Elsen,

Nayak

2024

ACS Biomater. Sci. Eng.

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3D printing has become increasingly popular in the field of bone tissue engineering. However, the mechanical properties, biocompatibility, and porosity of the 3D printed bone scaffolds are major requirements for tissue regeneration and implantation as well. Designing the scaffold architecture in accordance with the need to create better mechanical and biological stimuli is necessary to achieve unique scaffold properties. To accomplish this, different 3D designing strategies can be utilized with the help of the scaffold design library and artificial intelligence (AI). The implementation of AI to assist the 3D printing process can enable it to predict, adapt, and control the parameters on its own, which lowers the risk of errors. This Review emphasizes 3D design and fabrication of bone scaffold using different materials and the use of AI-aided 3D printing strategies. Also, the adaption of AI to 3D printing helps to develop patient-specific scaffolds based on different requirements, thus providing feedback and adequate data for reproducibility, which can be improvised in the future. These printed scaffolds can also serve as an alternative to preclinical animal test models to cut costs and prevent immunological interference.

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Section: Scaffold Engineeringmentioning

confidence: 99%

Artificial Intelligence-Based 3D Printing Strategies for Bone Scaffold Fabrication and Its Application in Preclinical and Clinical Investigations

Elsen,

Nayak

2024

ACS Biomater. Sci. Eng.

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“…A. 3D printing 3D printing is a method of producing a 3D object by layerby-layer material accumulation fashion by using a computer pre-designed object [9], [10]. The process of 3D printing to fabricate a part starts from the bottom of the part in a line-byline, then layer-by-layer process, to the top of the part until the whole part is printed [11], [12].…”

Section: Definitions Of the Termsmentioning

confidence: 99%

Terms Development of Additive Manufacturing

Tamir

Xiong

Jiang

et al. 2022

2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)

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Nowadays, additive manufacturing has been increasingly developed and applied in many fields. As a rapid development of additive manufacturing, similar terms are appeared, such as 3D printing, freeform fabrication, rapid tooling, rapid manufacturing, direct digital manufacturing, layer manufacturing, additive process, 4D printing, 3D bioprinting, and 3D biofabrication. This paper reports and analyze the development of these terms. Moreover, it tries to give the readers a comprehensive understanding of these terms and their development.

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“…The common printing defects include overfill, underfill, surface roughness, and warping [6]. To address these problems, there are various field monitoring technologies that can easily and automatically correct the detected defects in real time to improve the quality of AM components and reduce the variation of their mechanical properties [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Combination of Vision‐ and Sensor‐Based Defect Classifications in Extrusion‐Based Additive Manufacturing

Liu

Zhang

et al. 2023

Journal of Sensors

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Additive manufacturing, also known as 3D printing, has been facing the problem of inconsistent processing defects and product quality as a transformative technology, thus hindering its wide application in industry and other fields. In this context, machine learning (ML) algorithms are increasingly used for automatic classification of process data to achieve computer-aided defect detection. Specifically, in this paper, two data-driven classification prediction models are built by monitoring the sensing signals (temperature and vibration data) and interlayer images during the printing process, using the fused deposition model (FDM) as the base case, and the prediction results of the two machine learning models are fused for prediction. The experimental results show that by fusing the prediction results of the two models, the classification accuracy is significantly higher than the prediction results of a single model. These findings can benefit researchers studying FDM with the goal of producing systems that self-adjust online for quality assurance.

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Machine-learning-based monitoring and optimization of processing parameters in 3D printing

Cited by 44 publications

References 51 publications

Artificial Intelligence-Based 3D Printing Strategies for Bone Scaffold Fabrication and Its Application in Preclinical and Clinical Investigations

Artificial Intelligence-Based 3D Printing Strategies for Bone Scaffold Fabrication and Its Application in Preclinical and Clinical Investigations

Terms Development of Additive Manufacturing

A Combination of Vision‐ and Sensor‐Based Defect Classifications in Extrusion‐Based Additive Manufacturing

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