• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.109-118
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• 1999

The objective of this research is to develop an effective inspection planning strategy for sculpture surfaces in OMM(On-Machine Measurement) process. As a first step, effective measuring point locations are determined to obtain optimum results for given sampling numbers. Two measuring point selection methods are suggested in this study based on newly proposed CAD/CAM/CAI integration concept: (1) by the prediction of cutting errors, (2) by considering cutter contact points to avoid the measurement errors caused by cusps. As a next step, the TSP(Traveling Salesman Problem)algorithm is applied to minimize the probe moving distance. Appropriate simulations and experiments are performed to verify the proposed inspection planning strategy in this study, and the results are analyzed.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.10
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• pp.172-181
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• 1999

The objective of this research is to develop a measurement error model for sculptured surfaces in On-Machine Measurement (OMM) process based on a closed-loop configuration. The geometric error model of each axis of a vertical CNC Machining center is derived using a 4${\times}$4 homogeneous transformation matrix. The ideal locations of a touch-type probe for the scupltured surface measurement are calculated from the parametric surface representation and X-, Y- directional geometric errors of the machine. Also, the actual coordinates of the probe are calculated by considering the pre-travel variation of a probe and Z-directional geometric errors. Then, the step-by-step measurement error analysis method is suggested based on a closed-loop configuration of the machining center including workpiece and probe errors. The simulation study shows the simplicity and effectiveness of the proposed error modeling strategy.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.4
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• pp.92-97
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• 2006

This paper presents an integrated machining error compensation method based on PNN(Polynomial Neural Network) approach and inspection database of OMM(On-Machine-Measurement) system. To efficiently analyze the machining errors, two machining error parameters are defined and modeled using the PNN approach, which is used to determine machining errors for the considered cutting conditions. Experiments are carried out to validate the approaches proposed in this paper. In result, the proposed methods can be effectively implemented in a real machining situation, producing much fewer errors.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.581-582
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• 2006

This paper presents an integrated machining error compensation method based on PNN(Polynomial Neural Network) approach and inspection database of OMM(On-Machine-Measurement) system. To efficiently analyze the machining errors, two machining error parameters are defined and modeled using the PNN approach, which is used to determine machining errors for the considered cutting conditions. Experiments are carried out to validate the approaches proposed in this paper. In result, the proposed methods can be effectively implemented in a real machining situation, producing much fewer errors.

• Journal of Mechanical Science and Technology
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• v.18 no.8
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• pp.1349-1357
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• 2004

Computer-Aided Inspection Planning (CAIP) is the integration bridge between CAD/CAM and Computer Aided Inspection (CAI). A CAIP system for On-Machine Measurement (OMM) is proposed to inspect the complicated mechanical parts efficiently during machining or after machining. The inspection planning consists of Global Inspection Planning (GIP) and Local Inspection Planning (LIP). In the GIP, the system creates the optimal inspection sequence of the features in a part by analyzing the various feature information such as the relationship of the features, Probe Approach Directions (PAD), etc. Feature groups are formed for effective planning, and special feature groups are determined for sequencing. The integrated process and inspection plan is generated based on the sequences of the feature groups and the features in a feature group. A series of heuristic rules are developed to accomplish it. In the LIP of Part II, the system generates inspection parameters. The integrated inspection planning is able to determine optimum manufacturing sequence for inspection and machining processes. Finally, the results are simulated and analyzed to verify the effectiveness of the proposed CAIP.