Efficiency-aware process planning for mask image projection stereolithography: Leveraging dynamic time of exposure

Jing Zhao, Yiran Yang, Lin Li

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Owing to the unique layer-by-layer production method, additive manufacturing can provide higher design freedom and enhanced manufacturing complexity and capabilities, compared to traditional subtractive manufacturing. Currently, additive manufacturing technologies are mostly limited to small-scale rapid prototyping and tooling, due to the insufficient production throughput caused by their relatively low efficiency. Hence, to facilitate high-volume production using additive manufacturing, it is critical to evaluate and enhance the process efficiency in the early process planning stage by adjusting the values of critical process parameters. More specifically, in the mask image projection stereolithography process, layer-wise exposure time is the most influential factor that affects the build time as well as the product performance. The majority of existing studies on stereolithography process planning do not incorporate comprehensive curing models, leading to the lack of evaluation on the resulting performance of fabricated products. In this research, a novel process planning algorithm is proposed aiming to enhance the process efficiency by leveraging the dynamic time of exposure while ensuring achieved dimensional accuracy, surface quality, and mechanical properties. The case study results show that dynamic time of exposure outperforms constant exposure time as it can reduce the total build time by 6.25 % while improving dimensional accuracy, surface roughness, and hardness by 0.59 %, 27.94 %, and 3.07 %, respectively. Also, the sensitivity analysis results suggest that the incident irradiance of the light source and the scale size of the fabricated part are the most critical factors affecting the selection of exposure time, as a variation of 20 % of these two factors can lead to 19.27 % and 38.54 % changes in total production time, respectively.

Original languageEnglish (US)
Article number101407
JournalAdditive Manufacturing
Volume36
DOIs
StatePublished - Dec 2020

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • General Materials Science
  • Engineering (miscellaneous)
  • Industrial and Manufacturing Engineering

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