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This paper demonstrates a real-time process monitoring and control framework for the implementation of Industry 4.0 in manufacturing. For a case study, friction stir welding (FSW) has been selected owing to its extensive usage of welding in various manufacturing sectors. Force data has been collected online during the welding and sent to a cloud server enabling telewelding. It is processed through several signal processing and machine learning (ML) techniques for assessment of the weld quality. A control system thereafter controls the weld quality for avoiding the occurrence of weld-defect in realtime. Two ML models have been built, one for assessment of the weld quality, and the other for forecasting the controlled parameters for avoiding defect occurrence. For manufacturing sectors involved in mass production, this technique will be useful to keep a track of the process in real-time and avoid rejection of material in case of occurrence of an anomaly in the process. It is always beneficial for the manufacturing industry to track the quality of a product in real-time, and control the rejection rate, both of which have been successfully shown in this paper.

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The measurement of parameters for obtaining the mathematical model of the Level process station has to make accurately so as to obtain satisfactory performance of the system. The subject of this paper is concerned with the precise level control of a Level process station. The mathematical modeling of the system is done and control law for fuzzy and fuzzy immune PID is established. The Simulink of Matlab is used for the simulation