The rising demand for precision and quality in manufacturing necessitates that vast amounts of manufacturing knowledge be incorporated in manufacturing systems. Surface finish in end milling depends upon a number of variables such as cutting speed, feed rate, spindle speed, radial depth of cut, etc. The relative effect of these variables on surface roughness and machining time is quite considerable. A complex relationship exists between these process parameters and hence there is a need to develop models which can capture this complex interrelationship and enable fast computation of the average surface roughness and machining time based on process parameters. Neuro Fuzzy (NF) modeling has gained prominence recently on account of its fast reaction times, improved ease of operation and flexibility to respond to change in process parameters. In the present work, initially a Neuro Fuzzy Model is trained with experimental results of end milling. Subsequently, a generic approach is developed for optimization of end milling where the applicability and effectiveness of Neuro Fuzzy Model for function approximation is used to rapidly estimate average surface roughness and machining time in an integrated framework of Hybrid Stochastic Search Technique (HSST) to form a Neuro Fuzzy Hybrid Stochastic Search Technique (NFHSST). The results indicate that the NFHSST heuristic converges to better solutions rapidly as it provides the values of various process parameters for optimizing the objectives in a single run. Thus, NFHSST assists in the improvement of quality by developing multiple sound parts in an agile manner.
Stochastic Search Technique with Variable Deterministic Constraints for the Estimation of Induction Motor Parameters
