Hybrid neuro-fuzzy approach to the generation of measuring points for knowledge-based inspection planning

Selection of sampling points for accurate evaluation of flatness error using coordinate measuring machine

“…Zhang et al (1996) have used a neural network approach to determine the sample size for the inspection of holes using the process type, size of hole and tolerance band as factors. Hwang et al (2002) have used a hybrid neuro-fuzzy approach considering the tolerance and geometry features as factors. It becomes apparent that arriving at the sample size relies a lot on the manufacturing process used, but as mentioned earlier, problems can arise to establish concrete results due to the variety in manufacturing processes that can be used to produce the same kind of surface and also the variability of a manufacturing process itself.…”

“…Traditionally inspection planning has relied on the experience of inspection planners, which tends to lead to inconsistencies in the inspection plan. Hence it is important to have a systematic inspection plan that can help in the gathering of meaningful data in order to analyse the process and equipment conditions (Hwang et al, 2002). Automating the task of inspection planning would greatly enable to reduce the inconsistencies and result in more robust plans.…”

Surface Roughness Prediction Using Hybrid Neural Networks

“…Neural network and fuzzy logic are combined for surface roughness recognition in milling operations (Chen, 2001;Dweiri, 2003). A hybrid of three techniques is proposed by combining neuro-fuzzy method with GA for a knowledge-based inspection planning system (Hwang, 2002).…”

A study on mutative scale straightness measurement based on uncertainty analysis

“…Fan and Leu [15] considered the sample size based on the shape of the planar face and the ratio between the length and breadth of the measured area. Hwang et al [16] presented a knowledge-based inspection planning system that integrated product geometry information, tolerance information and heuristic knowledge of experienced inspection planners to determine the number and positions of sample points. Badar et al [17,18] presented an adaptive sampling method utilizing manufacturing error patterns and optimization search techniques for straightness and flatness evaluation.…”