Did you mean: Search also for related subjects Search also for narrower subjects Search also for broader subjects COMPLETE AIDED DESIGN ENGINEERING DESIGN STATISTICAL METHOD MICROWAVES INTEGRATED CIRCUIT DESIGN AND CONSTRUCTION STATISTICAL METHOD INDUSTRIAL ENGINEERING HANDBOOK. PRODUCTIVITY, PERFORMANCE AND ETHICS.ENGINEERING ECONOMICS.WORK ANALYSIS AND DESIGN.WORK MEASUREMENT AND TIME STANDARDS.ERGONOMICS AND SAFETY.COMPENSATION MANAGEMENT AND LABOR RELATIONS.LOGISTICS AND DISTRIBUTION.INFORMATION AND COMMUNICATION MANAGEMENT.PRODUCT DESIGN AND QUALITY MANAGEMENT.MANUFACTURING TECHNOLOGIES.MAINTENANCE MANAGEMENT MECHANICAL ENGINEERING - HANDBOOKS MANUALS.MECHANICS OF SOLIDS.ENGINEERING THERMODYNAMICS.FLUID MECHANICS.HEAT AND MASS TRANSFER.ELECTRICAL ENGINEERING.CONTROL MECHANICAL SYSTEMS.CONTROL SYSTEM ANALYSIS.CONTROL SYSTEM DESIGN.ENERGY RESOURCES.ENERGY CONVERSION.AIR CONDITIONING AND REFRIGERATION.TRANSPORTATION.ENGINEERING DESIGN.MATERIALS.MODERN MANUFACTURING.ROBOTICS.MEMS TECHNOLOGY.ENVIRONMENTAL ENGINEERING.ENGINEERING ECONOMICS.PROJECT MANAGEMENT.NANOTECHNOLOGY.MATHEMATICS.PATENT LAW.PRODUCT LIABILITY AND SAFETY.BIOMECHANICS.MECHANICAL ENGINEERING CODES AND STANDARDS.OPTICS.WATER DESALINATION.NOISE CONTROL.LIGHTING TECHNOLOGY CHEMICAL PLANTS-DESIGN AND CONSTRUCTION PRESSURE VESSELS-PIPING ENGINEERING MECHANICS-PRESSURE VESSELS SPEECH, IMAGE, VIDEO AND SIGNAL PROCESSING, WIRELESS, MOBILE AND SENSOR NETWORKS, COMPUTER VISION AND GRAPHICS, COMMUNICATION TECHNIQUES AND SYSTEMS, ARTIFICIAL INTELLIGENCE AND FUZZY LOGIC, COMPUTER NETWORKS AND UBIQUITOUS COMPUTING, ASIC DESIGN AND EMBEDDED SYSTEMS, MICROELECTRONICS AND MEMS, NANOTECHNOLOGY AND QUANTUM COMPUTING, ALGORITHMS, TOOLS & APPLICATIONS, GEO-INFORMATICS, MODELING & SIMULATION, POWER AND ALTERNATE ENERGY RESOURCES, SOFTWARE ENGINEERING, AUTOMATION AND CONTROL SYSTEMS, SOFTWARE ENGINEERING, DISTRIBUTED COMPUTING AND DATABASES 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 MS-COMPUTER ENGINEERING-2017 MSTHESIS ABSTRACT. The use and vast implementation of Discrete Fourier Transform has revolutionized the world and allowed the researchers to think of the modern world from a different perspective. The discovery of Fast Fourier Transform has laid the foundation of an entirely new dimension to the modern world. Keeping in view its utmost importance in the future industry researchers tried to design its hardware architecture as per the requirement of the application. Several architectures have been proposed time to time with new inventions in the previous designs. Some architectures consider clock rate, some take architectural area into consideration, some focuses on parallel execution of the algorithm, so on and so forth. Considering all these inputs to the industry that has been a part to modern world time to time, this research presents an empirical model based upon the optimal architectures for Fast Fourier Transform algorithm for n-bits m-points input. This empirical model is obtained by making several architectures and their respective characteristics are obtained. The data obtained is then passed through a machine learning algorithm known as Regression Algorithm. Linear, quadratic and cubic regression technique is applied to achieve the hierarchy of the designed architectural parameters and this intern will provide us with the empirical models of the architecture. This model will provide us with the specifications of the futuristic architecture that mainly depends upon the one's requirement i.e. either one considers a single parameter or a tradeoff between different hardware parameters. The parameters that are mainly considered are number of Slice LUT's, LUT FF Pairs, clock rate, number of processing elements and number of clock cycles required. This proposed methodology can be applied to any hardware architectural designs for analysis and generation of empirical models. Key Words: Discrete Fourier Transform, Fast Fourier Transform, Processing Element, Butterfly Architecture, n Radix FFT, Permutation Matrix, Kronecker Product