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The present work deals with modeling and analysis of laser material processing technologies which were commonly used in the recent past. The characteristics of laser machining and laser welding have been determined using response surface method (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). For each process, an experimental setup was designed and site-conducted using central composite design (CCD). Then their performance measures (responses) have been modeled and predicted based on RSM, ANN and ANFIS. The accuracies of developed models were compared with each other based on prediction error percent. The effects of each process's parameters on its performance measures were analyzed based on graphs which were plotted using the most accurate model. Results indicated that for both types of laser manufacturing processes, the ANFIS method predicted more accurate results. Following ANFIS, ANN and RSM showed almost precise prediction in modeling of performance measures. Hence, the ANFIS technique can be applied for modeling of laser material processing technologies.

soft computing

Erratum to: Application of soft computing techniques for modeling and analysis of MRR and taper in laser machining process as well as weld strength and weld width in laser welding process