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Open AccessReview

Path Planning Strategies to Optimize Accuracy, Quality, Build Time and Material Use in Additive Manufacturing: A Review

by Jingchao Jiang 1,2 and Yongsheng Ma 3,*
Department of Mechanical Engineering, University of Auckland, Auckland 1142, New Zealand
Digital Manufacturing and Design Center, Singapore University of Technology and Design, Singapore 486842, Singapore
Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 2V4, Canada
Author to whom correspondence should be addressed.
Micromachines 2020, 11(7), 633;
Received: 18 June 2020 / Revised: 24 June 2020 / Accepted: 26 June 2020 / Published: 28 June 2020
(This article belongs to the Special Issue Next-Generation Additive Manufacturing)
Additive manufacturing (AM) is the process of joining materials layer by layer to fabricate products based on 3D models. Due to the layer-by-layer nature of AM, parts with complex geometries, integrated assemblies, customized geometry or multifunctional designs can now be manufactured more easily than traditional subtractive manufacturing. Path planning in AM is an important step in the process of manufacturing products. The final fabricated qualities, properties, etc., will be different when using different path strategies, even using the same AM machine and process parameters. Currently, increasing research studies have been published on path planning strategies with different aims. Due to the rapid development of path planning in AM and various newly proposed strategies, there is a lack of comprehensive reviews on this topic. Therefore, this paper gives a comprehensive understanding of the current status and challenges of AM path planning. This paper reviews and discusses path planning strategies in three categories: improving printed qualities, saving materials/time and achieving objective printed properties. The main findings of this review include: new path planning strategies can be developed by combining some of the strategies in literature with better performance; a path planning platform can be developed to help select the most suitable path planning strategy with required properties; research on path planning considering energy consumption can be carried out in the future; a benchmark model for testing the performance of path planning strategies can be designed; the trade-off among different fabricated properties can be considered as a factor in future path planning design processes; and lastly, machine learning can be a powerful tool to further improve path planning strategies in the future. View Full-Text
Keywords: additive manufacturing; path planning; review additive manufacturing; path planning; review
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Jiang, J.; Ma, Y. Path Planning Strategies to Optimize Accuracy, Quality, Build Time and Material Use in Additive Manufacturing: A Review. Micromachines 2020, 11, 633.

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