Institute for Systems Research Technical Reports
Permanent URI for this collectionhttp://hdl.handle.net/1903/4376
This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.
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Item Building MRSEV Models for CAM Applications(1993) Gupta, Satyandra K.; Kramer, Thomas R.; Nau, D.S.; Regli, W.C.; Zhang, G.M.; ISRIntegrating CAD and CAM applications, one major problems is how to interpret CAD information in a manner that makes sense for CAM. Our goal is to develop a general approach that can be used with a variety of CAD and CAM applications for the manufacture of machined parts.In particular, we present a methodology for taking a CAD model, extracting alternative interpretations of the model as collections of MRSEVs (Material Removal Shape Element Volumes, a STEP-based library of machining features), and evaluating these interpretations to determine which one is optimal. The evaluation criteria may be defined by the user, in order to select the best interpretation for the particular application at hand.
Item Optical Area-Based Surface Quality Assessment for In-Process Measurement(1993) DeVoe, Don L.; Zhang, G.M.; ISRThe measurement of surface finish has been recognized as an important element of Computer Integrated Manufacturing (CIM) systems which perform on-line machining systems control. Optical methods for the in-process measurement of surface roughness have been developed for this purpose, but these systems have in many cases introduced excessive complexity in the CIM system. This work presents an area-based surface characterization technique which applies the basic light scattering principles used in other optical measurement systems. These principles are applied in a novel fashion which is especially suitable for in-process measurement and control. A prototype of the optical system to implement these principles is developed in this work. The experimental results are presented to demonstrate the capabilities and future potential for integrating the measurement system into a machining process to achieve significant improvement of quality and productivity.Item Study of the Formation of Macro-and-Micro-Cracks during Machining of Ceramics(1993) Zhang, G.M.; Anand, Davinder K.; Ghosh, Subrata; Ko, Wing F.; ISRThis paper presents an experimental study on the formation of macro- and micro- cracks formed during the machining of ceramic materials. Aluminum oxide (Al2O3) was used as the testing material and polycrystalline diamond tipped carbide inserts were used for material removal. The cutting force was recorded during machining and surface finish was measured after machining. an environmental SEM was used to obtain high-magnification images of macro- and microcracks induced by machining. With the assistance of a computer-based vision system, qualification of fracture surfaces with respect to crack nucleation, growth, and cleavage was attempted. Results from this research provide an insight into the prevailing mechanisms of material removal during the machining of ceramics, and suggest the development of crack- controlled machining technologies.Item Mathematical Modeling of the Uncertainty for Improving Quality in Machining Operations(1993) Zhang, G.M.; Hwang, Tsu-Wei; Ratnakar, R.; ISRThe difficulty in quality improvement of machining performance comes from the uncertainty about the cutting force generated during the material removal process. This paper presents the results from the research aimed at developing a new approach to capture the uncertainty through mathematically modeling the physical machining system. a case study is used to demonstrate the procedure to interpret the cutting force variation through a three stage process. By integrating deterministic and stochastic approaches, an observed cutting force variation, which was recorded from an experiment, can be explained satisfactorily. The reduction of uncertainty allows an accurate prediction of the cutting force variation and forms a basis for developing a control strategy for improving the machining performance.Item Analysis of Elastoplastic Deformation Observed on Machined Surfaces(1993) Hwang, Tsu-Wei; Zhang, G.M.; ISRIn this paper, the study of material removal mechanism is focused on a non-linear quasi-static analysis of the elastoplastic interaction between a single-point cutting tool and the material being cut. An updated Lagrange procedure is applied to solve the large strain elastoplastic deformation problem which generates part of the irregularities observed on machined surfaces. A unique three-dimensional finite element model is developed to simulate the single-point metal cutting process. The effects of cutting parameter settings and workpiece material on the elastoplastic deformation of machined surfaces are investigated. The validity of this analysis is verified by experiments. The results of this analysis can be applied as a surface texture modification model to enhance the accuracy of a computer-aided surface texture simulator, an important part of a computer integrated manufacturing system.Item Implementation of an Integrated CAD and CAM System(1992) Chen, A.T.; Zhang, G.M.; ISRThis is a progress report on a joint research project between the University and the M.S. Willett, Inc. The research focus is on the integration of a CAD system and a CAM system currently being used at the Willett.The development of CAD systems has revolutionized the process of preparing engineering designs and drawings. Likewise, CAM systems have significantly impacted the stop floor production process. Numerically controlled machines have improved accuracy and productivity in many applications. Integration of these two systems would tie the design phase of a project to the production process, and if done efficiently, could result in significant cost reduction and quality improvement.
In this project, two computer programs have been developed to automate NC code generation directly from a CAD file, either in DXF format or in IGES format. These two programs have been successful in identifying the important elements of an integrated CAD and CAM system. The initial results also indicated how the Willett could shorten the time of product development cycle, low the production cost, and improve the quality of end products.
This project has been supported by the Center for Manufacturing on the College Park campus.
Item Neural Network Applications in On-line Monitoring of Turning Processes(1992) Zhang, G.M.; Khanchustambham, Raju G.; ISRThe need to improve quality and decrease scrap rate while increasing the production rate is motivating industry to consider untended machining as viable alternative. On-line monitoring of a machining process is the key component to success for an untended machining operation. In this chapter, a framework for sensorbased intelligent decision-making systems to perform on- line monitoring is proposed. Such a monitoring system interprets the detected signals from the sensors, extracts the relevant information, and decides on the appropriate control action. Emphasis is laid on applying neural networks to perform information processing, and to recognize the process abnormalities in a machining operation. A prototype monitoring system is implemented to demonstrate the working mechanism. For successful implementation of the developed intelligent monitor, an instrumented force transducer is designed for signal detection and is used in a real time turning operation. A neural network monitor based on feedforward back-propagation algorithm is developed and tested under the machining of advanced ceramic materials and steel. The monitor is trained by the detected cutting force signal, the measured surface finish, and the observed tool wear. The superior learning and noise suppression abilities of the developed monitor enable high success rates for monitoring in machining process.Item A Stochastic Modeling for the Characterization of Random Tool Motion during Machining(1992) Hwang, Tsu-Wei; Zhang, G.M.; ISRThis paper presents the development of a new stochastic approach to characterize random tool motion during machining. The complexity of cutting mechanism is represented by a random excitation system related to physical properties of the material being machined. A Markov-chain based stochastic approach is developed to model the random tool motion as the response of a machining system under the random excitation. In considering a turning operation, a concept of group distributions is introduced to characterize the global effect on the cutting force due to the variation of a certain material property. A model of segment excitation is used to describe its micro function within an individual revolution. A distribution pattern observed in the material property is represented by a transition model. The simulation of random tool motion during machining resembles the generation of Markov chains. Microstructure analysis and image process are used to collect data, calculate relevant statistics, and estimate the system parameters specified in the developed stochastic model. As illustrated in this paper, the developed stochastic model can be effectively used to simulate the random tool motion and to learn rich information on the performance measures of interest such as machining accuracy and finish quality. The new approach represents a major advance to create a fundamental scientific basis for the realization of a reliable and effective prediction system for information processing in sensor-based manufacturing.Item A Neural Network Approach to On-line Monitoring of a Turning Process(1992) Khanchustambham, Raju G.; Zhang, G.M.; ISRProduction automation has been the focus of the research to improve product quality and to increase productivity. Implementation of computer-based untended machining has attracted great attention in the manufacturing community. In this paper, a framework for sensor-based intelligent decision-making systems to perform on-line monitoring is proposed. Such a monitoring system interprets the detected signals from the sensors, extracts the relevant information, and decide on the appropriate control action. Emphasis is given to applying neural networks to perform information processing, and to recognize the process abnormalities in a machining operation. A prototype monitoring system is implemented. For signal detection, an instrumented force transducer is designed and used in a real time turning operation. A neural network monitor based on a feedforward back- propagation algorithm is developed. The monitor is trained by the detected cutting force signal and measured surface finish. The superior learning and noise suppression abilities of the developed monitor enable high success rates for monitoring the cutting force and the quality of surface finish under the machining of advanced ceramic materials.Item Generation and Evaluation of Alternative Operation(1992) Nau, D.S.; Zhang, G.M.; Gupta, Satyandra K.; ISRThis paper presents a new and systematic approach to assist decision-making in selecting machining operation sequences. The approach is to produce alternative interpretations of design as different collections of machinable features, use these interpretations to generate alternative machining operation sequences, and evaluate the cost and achievable machining accuracy of each operations sequence. Given the operation sequences and their evaluations, it is then possible to calculate the performance measures of interest, and use these performance measures to select, from among the various alternatives, one or more of them that can best balance the need for a quality product against the need for efficient machining.