Special Issue on Trends in Intelligentizing Robotic Welding Processes
摘要截稿:
全文截稿: 2019-08-01
影响因子: 4.086
期刊难度:
CCF分类: 无
中科院JCR分区:
• 大类 : 工程技术 - 2区
• 小类 : 工程:制造 - 2区
Overview
Welding robots account for over 50% of the market of industrial robots in the US and in the world, according to the International Federation of Robotics (IFR). However, only relatively simple welding processes have been robotized where capabilities in adaptation are not critical. Enhancing robots with adaptation requires enhanced abilities to sense the welding process despite the harsh environment and make effective decisions despite the complexity of the process. Increased computation powers open opportunities to explore the use of advanced, often computation intensive, methods. Machine learning, big data analytics, sensor fusion, human-robot collaboration, etc. are among such methods that have recently achieved great success in extending the adaptation abilities of robotic welding systems.
In response to such recent rapid progresses, this special issue wishes to publish a collection of the state-of-the-art innovative applications of advanced methods to creatively solve major challenges in intelligentizing welding robots and machines. The special issue is also open to review papers that provide critical analysis of such state-of-the-art innovative applications. We plan for the special issue to publish ten to twenty research papers, three to five review papers, and an overview paper for the analysis of the state-of-the-art and future directions contributed by the guest editors.
All papers that develop innovative methods to make the welding machines to be more intelligent are encouraged. Authors can directly submit papers at JMP submission site. To be considered for this special issue, choose “SI: Intelligent Welding”. Feel free to choose your paper category either as Full Length Article or Research Paper, or Short Communication, or Review Article. Authors, specially those who are interested in contributing a review article/paper, are encouraged to send abstracts to one of the Guest Editors for an optional evaluation of suitability.