An autonomous intelligent framework for optimal orientation detection in 3D printing

Purpose This study addresses the challenge of generating material waste from support structures in 3D printing manufacturing and aims to explore more cost-effective manufacturing strategies for 3D printing manufacturers by considering two strategies: technology upgrading and material recycling. Design/methodology/approach This study examines the optimal decisions for manufacturers under each scenario (including a benchmark model and models for the two strategies) and explores the most profitable strategy by comparing the optimal profits of the manufacturer and analyzing the impact of key factors. Findings This study reveals that the choice of the optimal manufacturing strategy depends on the cost coefficient of technological effort and the fixed cost associated with introducing material recycling. In addition, it finds that material recycling is particularly effective in enhancing consumer surplus. Practical implications The analysis provides an important basis for decision-making for 3D printing manufacturers considering technology upgrading and material recycling, which can not only enhance economic benefits but also contribute to the sustainable advancement of 3D printing technology. Originality/value To the best of the authors’ knowledge, this study is the first to focus on the adverse effects of support structures in 3D printing manufacturing and systematically explore the economic feasibility of improving this issue through both technology upgrading and material recycling.

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Editors’ Choice—Review—Sensor-Based and Computational Methods for Error Detection and Correction in 3D Printing

Mehta,

Mujawar,

Lafrance

et al. 2024

ECS Sens. Plus

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Additive manufacturing in the realm of 3D printing has transformed the manufacturing sector, providing unmatched freedom in design and rapid prototyping abilities. However, a significant obstacle hindering its broader acceptance is the susceptibility to errors. These errors can take diverse forms, including layer shifting, under extrusion, and surface imperfections, ultimately resulting in unsuccessful prints or parts with weakened mechanical characteristics. Advanced error detection and correction methods are crucial for ensuring reliability and enhancing productivity. This paper reviews the current state-of-the-art in error detection techniques using various sensors in 3D printing, summarizing vision-based and fluctuation-based approaches for data collection and the use of a model-based approach for data interpretation. It further describes these techniques enable correction in 3D printing through sensor calibration, predictive modeling, specialized tools, and equipment specific techniques. The paper concludes by proposing a novel approach of combining advanced error detection and correction techniques into a comprehensive end-to-end error detection and correction methodology as a foundational building block for significantly improving the efficiency and yield in additive manufacturing processes.

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An autonomous intelligent framework for optimal orientation detection in 3D printing

Cited by 3 publications

References 48 publications

Geometric Modeling for Microstructure Design and Manufacturing: A Review of Representations and Modeling Algorithms

Geometric Modeling for Microstructure Design and Manufacturing: A Review of Representations and Modeling Algorithms

More cost-effective strategy for 3D printing: technology upgrading or material recycling?

Editors’ Choice—Review—Sensor-Based and Computational Methods for Error Detection and Correction in 3D Printing

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