This paper focuses on the development of an effective methodology to determine the optimum welding conditions that maximize the strength of joints produced by ultrasonic welding using response surface methodology (RSM) coupled with genetic algorithm (GA). RSM is utilized to create an efficient analytical model for welding strength in terms of welding parameters namely pressure, weld time, and amplitude. Experiments were conducted as per central composite design of experiments for spot and seam welding of 0.3-and 0.4-mm-thick Al specimens. An effective second-order response surface model is developed utilizing experimental measurements. Response surface model is further interfaced with GA to optimize the welding conditions for desired weld strength. Optimum welding conditions produced from GA are verified with experimental results and are found to be in good agreement.

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As the usage of plastic components has increased in various industries, the methods for fastening have increased rapidly. When the plastic components are fastened by self-tapping screws or bolts, failure occurs because of stripped threads or plastic creep. In these circumstances, threaded metal inserts provide improved joint performance and ability to assemble and disassemble the components without degrading them. Even though many techniques such as insert moulding, thermal insertion and cold insertion are available for joining thermoplastic material with metal insert, ultrasonic insertion is one of the most preferred processes because of the shorter cycle time usually less than a second, possibility of simultaneous installation of the multiple inserts and large-scale automation possibilities for higher production operations. The technical problems faced by the industries in ultrasonic insertion process are poor insertion quality which affects the function of the product. These problems arise because of the improper selection of insertion parameters. The objective of this paper is to optimize the ultrasonic insertion parameters for improving the quality of joint through non-traditional optimization techniques. Response surface methodology (RSM) is used to design the experiments, and then pullout strength and stripping torque are measured. Data obtained from the measurement are utilized to develop a nonlinear equation between the responses and predictors, and optimal combinations of insertion parameters are found out by fuzzy logic and genetic algorithm (GA) approach. From the confirmatory test, it was observed that the fuzzy logic yields better output results than GA.

This article deals with the optimization of process parameters for friction welding of Incoloy 800 H rod and compares the results obtained by response surface methodology (RSM) and artificial neural network (ANN). The experiments were carried out on the basis of a five-level, four-variable central composite design. The output parameters were the tensile strength and burn-off length (BOL). They were considered as a function of four independent input variables, namely heating pressure (HP), heating time, upsetting pressure (UP), and upsetting time. The RSM results showed that the quadratic polynomial model depicted the interconnection between individual element and response. For optimizing the process parameters, ANN analysis was used, and the optimal configuration of the ANN model was found to be 4-9-2. For modeling aspect, a requisite trained multilayer perceptron neural network was rooted, and a quick propagation training algorithm was used to train ANN. The purpose of optimization was to decide the maximum tensile strength and minimum burn-off length of the welded joint which was done by varying the friction welding process variables. The order of importance of input parameters for friction welding of Incoloy 800 H was HP [ UP [ N [ BOL. After predicting the model using RSM and ANN, a comparison was made for predicting the effectiveness of two methodologies. By analyzing the results, it was observed that as compared to RSM, ANN model was more specific.

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