Yongsheng Ma

Prof. Yongsheng Ma

University of Alberta
orcid

Research Focus & Metrics

Academic Career Length

25Years (2002-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
1 October 2016 in Advances in Engineering Software
Verified
Journal Article
Closed access
JLJikai Liu
Jikai Liu
YMYongsheng Ma
Yongsheng Ma

28 June 2020 in Micromachines
Verified
Journal Article
Open access
PDF file
Jingchao JiangJingchao Jiang
Jingchao Jiang
YMYongsheng Ma
Yongsheng Ma
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.

1 September 2015 in Surface and Coatings Technology
Verified
Journal Article
Closed access
HLHong Luo
Hong Luo
MLMichael Leitch
Michael Leitch
YBYashar Behnamian
Yashar Behnamian
YMYongsheng Ma
Yongsheng Ma
HZHongbo Zeng
Hongbo Zeng
JLJing-Li Luo
Jing-Li Luo

1 February 2018 in Computer Methods in Applied Mechanics and Engineering
Verified
Journal Article
Closed access
JLJikai Liu
Jikai Liu
YMYongsheng Ma
Yongsheng Ma

1 May 2003 in Computers in Industry
Verified
Journal Article
Closed access
YMYongsheng Ma
Yongsheng Ma
TTT. Tong
T. Tong

8 March 2019 in International Journal of Precision Engineering and Manufacturing-Green Technology
Verified
Journal Article
Closed access
PDF file
JLJikai Liu
Jikai Liu
YZYufan Zheng
Yufan Zheng
YMYongsheng Ma
Yongsheng Ma
AQAhmed Qureshi
Ahmed Qureshi
Rafiq AhmadRafiq Ahmad
Rafiq Ahmad