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

Jingchao Jiang1,2, Yongsheng Ma3

  • 1Department of Mechanical Engineering, University of Auckland, Auckland 1142, New Zealand.

Micromachines
|July 2, 2020
PubMed
Summary
This summary is machine-generated.

This review explores additive manufacturing (AM) path planning strategies for enhanced quality, material savings, and property control. Future research should focus on combining strategies, developing selection platforms, and utilizing machine learning.

Keywords:
additive manufacturingpath planningreview

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Area of Science:

  • Manufacturing Engineering
  • Materials Science
  • Computer-Aided Design

Background:

  • Additive Manufacturing (AM) enables complex geometries and customization, surpassing traditional methods.
  • Path planning is crucial in AM, significantly impacting final product quality and properties.
  • Existing research on AM path planning is rapidly evolving, necessitating a comprehensive review.

Purpose of the Study:

  • To provide a comprehensive understanding of the current status and challenges in AM path planning.
  • To review and categorize path planning strategies based on their objectives: improving quality, saving resources, and achieving specific properties.

Main Methods:

  • Systematic literature review of path planning strategies in additive manufacturing.
  • Categorization of strategies into three main groups: improving printed qualities, saving materials/time, and achieving objective printed properties.

Main Results:

  • Identified opportunities for developing novel path planning strategies by combining existing methods.
  • Highlighted the need for a path planning platform to guide strategy selection based on desired properties.
  • Suggested future research directions including energy consumption, benchmark models, trade-off analysis, and machine learning applications.

Conclusions:

  • Additive manufacturing path planning is a dynamic field with significant potential for optimization.
  • Future advancements lie in integrated approaches, intelligent selection tools, and the application of machine learning.
  • Further research is needed to address energy efficiency, establish performance benchmarks, and manage trade-offs in fabricated properties.