By Xiaoqi Chen, Rajagopalan Devanathan, Aik Meng Fong
This quantity provides the editors' learn in addition to similar findings at the purposes of contemporary applied sciences in electric and digital engineering to the automation of a few of the typical production methods that experience commonly been dealt with in the mechanical and fabric engineering disciplines. specifically, the textual content contains the most recent study effects completed via utilized study and improvement tasks over the last few years on the Gintic Institute of producing know-how, Singapore. It discusses complex automation applied sciences equivalent to in-process sensors, laser imaginative and prescient structures, and laser strobe imaginative and prescient, in addition to complex suggestions akin to sensory sign processing, adaptive method keep watch over, fuzzy common sense, neural networks, professional structures, laser processing regulate, and so forth. The methodologies and strategies are utilized to a couple very important fabric processing purposes, together with grinding, sharpening, machining, and welding. sensible automation options, that are advanced by way of half distortions, software put on, strategy dynamics, and versions, are defined.
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Extra info for Advanced Automation Techniques in Adaptive Material Processing
Researchers are working on intelligent knowledge processing tools to generate optimum task specifications automatically. A model-based robotic machining system offers the following advantages: • • • • • To facilitate the integration of robotic machining systems into a CEVI environment. To minimise or eliminate robot down time and to shorten production time, hence to reduce production costs. To overcome the disadvantages of on-line manual programming which is time-consuming and tedious. To support planning engineers for efficient robotic task planning and analysis, graphical simulation, calculation of cycle time, collision detection, and program generation.
In the context of computer integrated, flexible and automated manufacturing, robots play an important role as a flexible component in automation. Computer-aided tools for robotic modelling, task planning and simulation, programming, and sensor integration have been vigorously researched. In recent years, there has been X Q Chen, Z M Gong, H Huang, S Z Ge, and L B Zhou 36 an explosion of research interests in robot task planning, off-line programming, automatic program synthesis, and robot integration into manufacturing system.
To minimise or eliminate robot down time and to shorten production time, hence to reduce production costs. To overcome the disadvantages of on-line manual programming which is time-consuming and tedious. To support planning engineers for efficient robotic task planning and analysis, graphical simulation, calculation of cycle time, collision detection, and program generation. To integrate calibration sensors into robotic systems so that robots can be more adaptable to the actual environment. One of the challenges in applying model-based robotic systems is the discrepancy between the ideal world (computer model) and the real world.