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Subject: Industrial and Production Engineering × Author: Amit Kumar × End date: 2019 × Start date: 2010 ×
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    ThesisItemOpen Access
    Optimization of MIG welding parameters of dissimilar metals using Artificial Neural Network (ANN) and Genetic Algorithm (GA)
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2013-07) Amit Kumar; Jadoun
    Welding is a process by which we can join two similar or dissimilar metals very efficiently. With the help of welding, success of the weld can be achieved up to 100%. Welding is more useful because it is less costly as compared to other fabrication process like casting, forming, machining. In this study dissimilar metals of stainless steel of grade 304 and stainless steel of grade 316 are used in this study. We make 27 samples at different levels. Metal inert gas (MIG) welding is used in this study for welding because MIG welding is automatic machine and even less skill worker can operate it. Phoenix 301 MIG welding machine is used for welding. Joint strength is determined using the universal testing machine (UTM). In this study Artificial neural Network is a very important tool which relates input and output.. For classification Artificial Neural Network was built which shows some inter relationship between Input and Output parameter. ANN work similarly as biological neuron does work. GA is used to optimize selected MIG welding parameters input parameter (voltage, welding speed and current) and output parameter (tensile strength). Artificial neural network and Genetic Algorithm is used to design the experiment. The results were analysed using Artificial Neural Network (ANN) which is a part of MATLAB for the optimal parameters GA tool used which also a part of MATLAB. MIG welding is also known as metal inert gas welding and gas metal-arc welding (GMAW). Gas metal arc welding (GMAW) is widely used in industry due to its high metal deposition and ease of automation with better weld quality.