Advanced Informatics Modeling and Analysis Approach in Additive Manufacturing

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Guest Editors
Prof. Yongsheng Ma
Department of Mechanical Engineering, University of Alberta, Edmonton, Canada
Email: yongsheng.ma@ualberta.ca

Prof. Jikai Liu
School of Mechanical Engineering, Shandong University, Jinan, China
Email: jikai_liu@sdu.edu.cn

Dr. Jingchao Jiang
Department of Mechanical Engineering, University of Auckland, New Zealand; Digital Manufacturing and Design Center, Singapore University of Technology and Design, Singapore
Email: jjia547@aucklanduni.ac.nz

Manuscript Topics
The rapid development of additive manufacturing (AM) technology has revolutionized the computer-aided systems for product design, manufacturing, assembly, supply chain management, maintenance, recycling and many other aspects. The unprecedented manufacturing capability and flexibility of AM has greatly complicated these activities and made great demands on data-driven, knowledge-based, computing-intensive systems to unleash the creativities and automate the complex missions. Taking feature technology for example, the traditional design-by-feature technology is now replaced by the generative design methods, wherein computational intelligence automates the product design. Functional features are more focused rather than manufacturability issues owing to the superior geometric complexity handling of AM. On the other hand, the paradigm shift is far from complete. There are still many computer-aided systems that lack knowledge discovery and intelligent usage of knowledge. For example, process planning for hybrid additive-subtractive manufacturing has very low-level automation which mainly counts on manual efforts to interpret the process planning knowledge and performs the operations. Therefore, more sophisticatedly developed systems mixing techniques of artificial intelligence, computational graphics, numerical optimization, and computational mechanics are expected to address the challenges in developing CAx systems for AM.

To address the above issues, this special issue is dedicated to bringing together researchers with diverse research backgrounds into a common forum, contributing thoughts to this cutting-edge research topic, and accelerating the development of AM technology with the aid of knowledge-based CAx systems. The topics of this special issue include, but are not limited to the following:

• Generative design-for-AM
• Artificial intelligence in design-for-AM
• Design-for-social AM
• Intelligent systems for tolerance management and model compensation
• Intelligent systems for part consolidation/deconsolidation decision making
• Automation in process planning for multi-axis AM
• Automation in process planning for hybrid additive-subtractive manufacturing
• Supply chain management with AM
• Intelligent systems for AM-based remanufacturing decision making

Paper Submission
All manuscripts will be peer-reviewed before their acceptance for publication.
The deadline for manuscript submission is October 15, 2020.

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Zhihao Wei, Jiacai Wu, Nan Shi, Lei Li
+ Abstract     + HTML     + PDF(1495 KB)
Yun-Fei Fu
+ Abstract     + HTML     + PDF(662 KB)
Jingchao Jiang, Chunling Yu, Xun Xu, Yongsheng Ma, Jikai Liu
+ Abstract     + HTML     + PDF(1688 KB)
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