Shan-Ben Chen – författare

The primary aim of this volume is to provide researchers and engineers from both academic and industry with up-to-date coverage of new results in the field of robotic welding, intelligent systems and automation. The book is mainly based on papers selected from the 2014 International Conference on Robotic Welding, Intelligence and Automation (RWIA’2014), held Oct. 25-27, 2014, at Shanghai, China. The articles show that the intelligentized welding manufacturing (IWM) is becoming an inevitable trend with the intelligentized robotic welding as the key technology. The volume is divided into four logical parts: Intelligent Techniques for Robotic Welding, Sensing of Arc Welding Processing, Modeling and Intelligent Control of Welding Processing, as well as Intelligent Control and its Applications in Engineering.

This research report brings together present trends in advanced welding robots, robotic welding, artificial intelligent and automatic welding. It includes important technical subjects on welding robots such as intelligent technologies and systems, and design and analysis. Modeling, identification and control of the welding process are presented, as well as knowledge-based systems for welding and tele-robotic welding. Other topics covered are sensing and data fusion, computer vision and virtual-reality applications of the welding process. An overview of intelligent and flexible manufacturing systems is given in addition to artificial intelligent technologies for industrial processes.

Robotic welding systems have been used in different types of manufacturing. They can provide several benefits in welding applications. The most prominent advantages of robotic welding are precision and productivity. Another benefit is that labor costs can be reduced. Robotic welding also reduces risk by moving the human welder/operator away from hazardous fumes and molten metal close to the welding arc. The robotic welding system usually involves measuring and identifying the component to be welded, we- ing it in position, controlling the welding parameters and documenting the produced welds. However, traditional robotic welding systems rely heavily upon human interv- tion. It does not seem that the traditional robotic welding techniques by themselves can cope well with uncertainties in the welding surroundings and conditions, e. g. variation of weld pool dynamics, fluxion, solid, weld torch, and etc. On the other hand, the advent of intelligent techniques provides us with a powerful tool for solving demanding re- world problems with uncertain and unpredictable environments. Therefore, it is intere- ing to gather current trends and to provide a high quality forum for engineers and researchers working in the filed of intelligent techniques for robotic welding systems. This volume brings together a broad range of invited and contributed papers that describe recent progress in this field.

This book presents some application researches on intelligentized methodology in arc welding process, such as machine vision, image processing, fuzzy logical, neural networks, rough set, intelligent control and other arti?cial intelligence me- ods for sensing, modeling and intelligent control of arc welding dynamical process.

Welding handicraft is one of the most primordial and traditional technics, mainly by manpower and human experiences. Weld quality and ef?ciency are, therefore, straitly limited by the welder’s skill. In the modern manufacturing, automatic and robotic welding is becoming an inevitable trend. However, it is dif?cult for au- matic and robotic welding to reach high quality due to the complexity, uncertainty and disturbance during welding process, especially for arc welding dynamics. The information acquirement and real-time control of arc weld pool dynamical process during automatic or robotic welding always are perplexing problems to both te- nologist in weld ?eld and scientists in automation. This book presents some application researches on intelligentized methodology in arc welding process, such as machine vision, image processing, fuzzy logical, neural networks, rough set, intelligent control and other arti?cial intelligence me- ods for sensing, modeling and intelligent control of arc welding dynamical process. The studies in the book indicate that the designed vision sensing and control s- tems are able to partially emulate a skilled welder’s intelligent behaviors: observing, estimating, decision-making and operating, and show a great potential and prom- ing prospect of arti?cial intelligent technologies in the welding manufacturing.