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Artificial Intelligence and Robotics

Industrial education and technology

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Full-Text Articles in Engineering

Development Of Four In-Process Surface Recognition Systems To Predict Surface Roughness In End Milling , Shi-Jer Lou Jan 1997

Development Of Four In-Process Surface Recognition Systems To Predict Surface Roughness In End Milling , Shi-Jer Lou

Retrospective Theses and Dissertations

Surface roughness is one of the important factors in tribology and in evaluating the quality of machining operations. To realize full automation and achieve zero defect production, an effective technique is needed for on-line, real-time monitoring of surface roughness during machining. An in-process surface recognition system (ISRS), was developed for predicting real-time surface roughness, Ra, in end-milling operations. The parameters are spindle speed, feed rate, depth of cut, and the cutting, vibration between tool and workpiece. The cutting vibration is measured by an accelerometer and a proximity sensor;The analyses of the data and the ISRS building model are carried ...


The Fuzzy-Nets Based Approach In Predicting The Cutting Power Of End Milling Operations , Chuan-Teh Chang Jan 1997

The Fuzzy-Nets Based Approach In Predicting The Cutting Power Of End Milling Operations , Chuan-Teh Chang

Retrospective Theses and Dissertations

Process planning is a major determinant of manufacturing cost. The selection of machining parameters is an important element of process planning. The development of a utility to show the cutting power on-line would be helpful to programmers and process planners in selecting machining parameters. The relationship between the cutting power and the machining parameters is nonlinear. Presently there is no accurate or simple algorithm to calculate the required cutting power for a selected set of parameters. Although machining data handbooks, machinability data systems, and machining databases have been developed to recommend machining parameters for efficient machining, they are basically for ...


An Expert Fuzzy Logic Controller Employing Adaptive Learning For Servo Systems , Zong-Mu Yeh Jan 1992

An Expert Fuzzy Logic Controller Employing Adaptive Learning For Servo Systems , Zong-Mu Yeh

Retrospective Theses and Dissertations

An expert fuzzy logic controller with adaptive learning is proposed as an intelligent controller for servo systems. A key component of this controller is an adaptive learning mechanism which is used to self-regulate the scaling factors and the control action based on the error between the desired value and the plant output. The inference engine of this controller is based on the principle of approximate reasoning and the learning strategy is based on reinforcement learning. A novel approach of model reference adaptive control is also proposed for servo systems. The comparison of the performance between the proposed controller and PID ...