The paper presents an analysis and summary of the current research state concerning the application of machine learning and fuzzy logic for solving problems in electronics. The investigated domain is conceptualized with aim the achievements, trending topics and future research directions to be outlined. The applied research methodology includes a bibliographic approach in combination with a detailed examination of 66 selected papers. The findings reveal the gradually increasing interest over the last 10 years in the machine learning and fuzzy logic techniques for modeling, implementing and improving different hardware-based intelligent systems.
One of the latest solutions in automated production systems is developing of Reconfigurable Manufacturing Systems (RMSs), which have an open structure that allows the integration of new technologies and modifying manufacturing for a new type of production based on adding, removing or updating the elements and units in the hardware and software level. To realize the automated processing RMS needs to have decentralized architecture, including hardware (module based) and software reconfiguration, which to enable distributed control, monitoring and diagnostics in real time and dynamic planning processes and resources in the system. One of the ways to realize this architecture is to development RMS control system on the base of IEC 61499 function blocks (FBs). Important part of the control system is a module for distributed planning and scheduling of processes, which consists all available information for technology of the products. This paper presents an approach for development of scheduling and planning modules in reconfiguration cutting work station on the base of IEC 61499 function blocks.
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