Virtual commissioning (VC) of manufacturing systems has been researched for more than 10 years. Its intention is to test manufacturing systems and associated control programs through simulation conducted before the real systems are realised. The expected benefits in reducing debugging and correction efforts expended during real commissioning, however, can only be achieved if sufficiently detailed manufacturing system models are available for simulation. To date, the design of such models has certainly required a high level of expertise and considerable effort, which makes virtual commissioning unattractive, especially for small and medium-sized enterprises (SME). After reviewing the current status of VC, this paper describes some new concepts for the systematic and simplified design of manufacturing system models for VC based on model libraries and standardized recipes for the design of component models from CAD data. This work is carried out as part of the research cooperation between the University of Glamorgan and the University of Applied Sciences and Arts Hannover.
In recent years, artificial intelligence played an important role in machine tool automation. Artificial neural networks, as one of the artificial intelligence algorithms, has superiority in representing the relation between the inputs and outputs of the multi-variable system. Hence, it can be applied to sophisticated operations such as grinding operation. The aim of this research is to use artificial neural networks as the brain of grinding machine controller. The target of this controller was to achieve the desired workpiece surface roughness under grinding wheel surface topography variations. The core of the system consists of two multi-layers feed forward artificial neural networks based on back error propagation learning algorithm. The first one was used for process design to achieve the desired surface roughness. It extracts suitable process variables such as grinding wheel speed and feed rate. The second one monitors the cutting operation using sensors' readings. It extracts the different controlling decisions; these are accept the process, redesign the process or start dressing operation under automatic control. According to these decisions, a PC master control program generates the appropriate control codes and sends them to the machine controllers to take the required actions.
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