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CAD-CAM ROBOTICS.CAD-CNC.DESIGN AND ANALYSIS.FINITE ELEMENT ANALYSIS.PRODUCT DESIGN.LIFE CYCLE MANAGEMENT.RAPID PROTOTYPING AND TOOLING.MANUFACTURING PROCESSES.MICROMACHINING.MIMIATURISATION.AUTOMATION.MECHANISM AND ROBOTICS.ARTIFICIAL INTELLIGENCE.ADVANCED CONTROL SYSTEMS.QUALITY RELIABILITY AND MAINTENANCE.SUPPLY CHAIN MANAGEMENT.LOGISTICS MANAGEMENT.MATERIAL HANDLING SYSTEMS.HUMAN ASPECT IN ENGINEERING.ENGINEERING EDUCATION AND TRAINING
STATIC PRESSURE MEASUREMENT IN ATORQUE CONVERTER STATE 1-10 A SYSTEM ENGINEERING APPROACH TO SUBSYSTEM DESIGN AUTOMOTIVE TRANSMISSION 11-16 DESIGN OF VANE PUMP SUCTION PORTING TO REDUCE CAVITATION AT HIGH OPERATION SPEEDS17-22 IMPROVING AUTOMATIC TRANSMISSION SHIFT QUALITY BY FEEDBACK CONTROL WITH A TURBINE SPEED SENSOR 23-32 SATUM TRANSMISSION FAMILY PRODUCT AND PROCESS FLEXIBILITY BY DESIGN 33-42 A GREASE FILLED TORSIONAL COUPLING FOR CVT VEHICLES 43-50 AMETHOD FOR SELECTING PARALLEL CONNECTED PLANETARY GEAR TRAIN ARRAGEMENTS FOR AUTOMOTIVE AUTOMATIC TRANSMISSION 51-60 RESONANCE TYPE GEAR FATIGUE TESTER 61-66 A STUDY ON GEAR NOISE REDUCTION BASED ON HELICAL GEAR TOOTH 67-72 ACC NO-27349 ACC NO-28048
MS-MECH-84 MSTHESIS ABSTRACT. Reconfigurable Manufacturing Systems (RMS) effectively respond to fluctuating market needs and customer demands for finished product. Diagnosability is a supporting characteristic of RMS that has a say in the quality of finished product. Cost and time taken for manufacturing are also considerably affected if proper diagnosability measures are not taken. Previous studies on Diagnosability of RMS have been studied from Axiomatic System Theory as such Design For Diagnosability (DFD). Nevertheless Diagnosability remains to be the least studied characteristic of RMS. With the availability of digitized data, Machine Learning approaches to advance manufacturing have proven to be considerably effective. A research gap existed for the application of Machine Learning techniques in improving the Diagnosability of RMS. A framework of Machine Learning has been proposed to address this gap. The working of the framework has been illustrated by two demonstrations from the available datasets, one in identifying proper signals in semi-conductor manufacturing to predict excursions, and the second in predicting machine failures due to a variety of factors. The framework is rendered in a concurrent-engineering fashion. The framework is tested against two available manufacturing datasets. Increase in Diagnosability will decrease the cost and time taken to production. Key Words: Reconfigurable Manufacturing Systems, Machine Learning, Artificial Intelligence, Preventive Maintenance, Intelligent Manufacturing