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Information-based inspection allocation for real-time inspection systems
Journal of Mamfacturing Systems
Vol. 21A'o. 6 2002
2000-2002 abstract and keyword index requirements of engineering functions in various manufacturing enterprises. These interrelated functions include product design, process planning, capacity planning, production costing, quality control, acquisition or reconfiguration of resources, planning and scheduling of shop activities, and execution of shop activities. These taxonomies and functions are not typically integrated in today’s manufacturing enterprise. This results in inefficient manual transfer of knowledge between domains and the unavailability of critical information required for decision making. Object-oriented design methodologies are useful for modeling diverse information and behavior. Furthermore, planning, analysis, and control of resources such as machine tools, fixtures, and tooling increasingly dominate the engineering functions. This paper demonstrates how to integrate these functions with an object-oriented resource model that links information from different knowledge domains. These functions are implemented using different software packages that can easily access the common resource data because the data are embedded in the resource class structure. This resource model is based on software objects that have a one-to-one correspondence with physical objects. This resource model is illustrated using object-oriented software, but the model may also be applied to distributed object and agent architectures. Keywords: Information Technology, Manufacturing Systems, Computer Control Fault Diagnosis Thermal Error
and Recovery for a CNC Machine Compensation System, Jin-Hyeon
Journal of Manufacturing Systems Volume 20, Number I,2001 Quadratic Loss Functions and Signal-to-Noke Ratios for a Bivatite Response, Saeed Maghsoodloo, Chun-Lang Chang,
~20, nl, 2001, ~~1-12 In this paper, the quadratic quality loss functions and signal-to-noise (S/N) ratios for a bivariate response are developed. Quality characteristics by variables are divided into three types: (1) Smaller the better (STB), (2) Larger the better (LTB), and (3) Nominal the best (NTB). The focus is on the bivariate quality characteristic response combinations (NTB, NTB), (STB, STB), and (LTB, LTB) cases. The treatment of mixed bivariate (STB, LTB), (STB, NTB), and (LTB, NTB) response cases will be forthcoming as a sequel to this paper on the next issue of this journal (Vol. 20, No. 2, 2001). The relationships among quality loss constants are also discussed in each case. An example of a robust parameter design experiment using simulated data is provided to illustrate the use of signal-to-noise ratios, which were developed to identify the optimal factor settings. The design matrix used in the example was an L8(2’) Taguchi orthogonal array (OA) as a one-half fraction of a 24 full-factorial experiment. Ktywor& Taguchi Methods, Bivariate Quality Loss Functions, Robust Parameter Design, Signal-to-Noise Ratios
Tool Information-Based Inspection Allocation for Real-Time Inspection Systems, Adan Verduzco, J. Rene Villalobos,
Lee,
Seung-Han Yang, ~19, n6,2000, ~~428-434 The major role of temperature sensors in a machine tool thermal error compensation system is the improvement of machining accuracy through the supply of reliable temperature data on the machine structure. This paper presents a new method for the fault diagnosis of temperature sensors along with the recovery of faulty data, thereby protecting the reliability of a thermal error compensation system. The proposed method of detecting a fault and its location is based on the correlation coefficients among the temperature data produced by the sensors. Thereafter, a multiple linear regression model, prepared using the complete normal data, is used for the recovery of faulty data. The effectiveness of this method was tested by comparing computer simulation results with measured data from a CNC machining center. Keywords: Thermal Error Compensation System, Fault Diagnosis, Recovery, Correlation Coefficient, Multiple Linear Regression Model
Benjamin Vega, ~20, nl, 2001, ~~13-22 The use of real-time Automated Visual Inspection (AVI) in the electronics assembly industry is subject to cycle time constraints determined by the manufacturing system. These constraints must be met to maintain the nominal production rate. A problem thus created is determining which components to inspect at each one of the AVI stations such that the inspection time available is used in an optimal way. This paper presents a real-time inspection allocation that is based on the information gained by inspecting one additional component. The selection of which components to inspect is modeled as an information maximization problem. A modified Knapsack greedy heuristic is used to find near-optimal solutions to this optimization problem within the required time constraints. Keywords: Inspection Allocation, Automated Visual Inspection, Information Gain, Flexible Inspection Systems
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Vol. 21A'o. 6 2002
2000-2002 abstract and keyword index requirements of engineering functions in various manufacturing enterprises. These interrelated functions include product design, process planning, capacity planning, production costing, quality control, acquisition or reconfiguration of resources, planning and scheduling of shop activities, and execution of shop activities. These taxonomies and functions are not typically integrated in today’s manufacturing enterprise. This results in inefficient manual transfer of knowledge between domains and the unavailability of critical information required for decision making. Object-oriented design methodologies are useful for modeling diverse information and behavior. Furthermore, planning, analysis, and control of resources such as machine tools, fixtures, and tooling increasingly dominate the engineering functions. This paper demonstrates how to integrate these functions with an object-oriented resource model that links information from different knowledge domains. These functions are implemented using different software packages that can easily access the common resource data because the data are embedded in the resource class structure. This resource model is based on software objects that have a one-to-one correspondence with physical objects. This resource model is illustrated using object-oriented software, but the model may also be applied to distributed object and agent architectures. Keywords: Information Technology, Manufacturing Systems, Computer Control Fault Diagnosis Thermal Error
and Recovery for a CNC Machine Compensation System, Jin-Hyeon
Journal of Manufacturing Systems Volume 20, Number I,2001 Quadratic Loss Functions and Signal-to-Noke Ratios for a Bivatite Response, Saeed Maghsoodloo, Chun-Lang Chang,
~20, nl, 2001, ~~1-12 In this paper, the quadratic quality loss functions and signal-to-noise (S/N) ratios for a bivariate response are developed. Quality characteristics by variables are divided into three types: (1) Smaller the better (STB), (2) Larger the better (LTB), and (3) Nominal the best (NTB). The focus is on the bivariate quality characteristic response combinations (NTB, NTB), (STB, STB), and (LTB, LTB) cases. The treatment of mixed bivariate (STB, LTB), (STB, NTB), and (LTB, NTB) response cases will be forthcoming as a sequel to this paper on the next issue of this journal (Vol. 20, No. 2, 2001). The relationships among quality loss constants are also discussed in each case. An example of a robust parameter design experiment using simulated data is provided to illustrate the use of signal-to-noise ratios, which were developed to identify the optimal factor settings. The design matrix used in the example was an L8(2’) Taguchi orthogonal array (OA) as a one-half fraction of a 24 full-factorial experiment. Ktywor& Taguchi Methods, Bivariate Quality Loss Functions, Robust Parameter Design, Signal-to-Noise Ratios
Tool Information-Based Inspection Allocation for Real-Time Inspection Systems, Adan Verduzco, J. Rene Villalobos,
Lee,
Seung-Han Yang, ~19, n6,2000, ~~428-434 The major role of temperature sensors in a machine tool thermal error compensation system is the improvement of machining accuracy through the supply of reliable temperature data on the machine structure. This paper presents a new method for the fault diagnosis of temperature sensors along with the recovery of faulty data, thereby protecting the reliability of a thermal error compensation system. The proposed method of detecting a fault and its location is based on the correlation coefficients among the temperature data produced by the sensors. Thereafter, a multiple linear regression model, prepared using the complete normal data, is used for the recovery of faulty data. The effectiveness of this method was tested by comparing computer simulation results with measured data from a CNC machining center. Keywords: Thermal Error Compensation System, Fault Diagnosis, Recovery, Correlation Coefficient, Multiple Linear Regression Model
Benjamin Vega, ~20, nl, 2001, ~~13-22 The use of real-time Automated Visual Inspection (AVI) in the electronics assembly industry is subject to cycle time constraints determined by the manufacturing system. These constraints must be met to maintain the nominal production rate. A problem thus created is determining which components to inspect at each one of the AVI stations such that the inspection time available is used in an optimal way. This paper presents a real-time inspection allocation that is based on the information gained by inspecting one additional component. The selection of which components to inspect is modeled as an information maximization problem. A modified Knapsack greedy heuristic is used to find near-optimal solutions to this optimization problem within the required time constraints. Keywords: Inspection Allocation, Automated Visual Inspection, Information Gain, Flexible Inspection Systems
474