Application of Welding Process Parameters Using AI Algorithm (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2018)
Welding is a complex process that plays a critical role in various industries, including manufacturing, construction, and automotive. Achieving optimal welding process parameters is essential to ensure high-quality welds with minimal defects. However, determining the optimal parameters requires extensive experimentation and expertise, which can be time-consuming and costly. In artificial intelligence (AI) algorithms have shown great potential in optimizing welding process parameters. This study focuses on the application of AI algorithms for optimizing welding process parameters. The objective is to develop a machine learning model that can accurately predict the optimal welding parameters based on various input factors such as material type, joint configuration, and desired weld quality. The model utilizes a dataset of past welding experiments, where the parameters and corresponding weld quality were recorded. The significance of this research lies in its potential to revolutionize the welding industry by improving the efficiency and effectiveness of the welding process. By leveraging AI algorithms, the model can quickly analyze and identify the relationships between input factors and welding parameters, leading to optimal parameter recommendations. This can result in reduced welding defects, improved weld quality, and increased productivity.To achieve the research objectives, a comprehensive literature review is conducted to understand the current state of AI algorithms applied to welding process optimization. Various AI techniques such as neural networks, genetic algorithms, and support vector machines are explored for their suitability in this context. The literature review also covers previous studies and their findings on the application of AI algorithms in welding parameter optimization. The methodology of this research involves the collection of welding process data from different welding experiments. This dataset is then pre- processed to handle missing values, outliers, and normalize the data. Feature engineering techniques are applied to select the most relevant input factors. Several AI algorithms are implemented and trained using the dataset, and their performance is evaluated using appropriate metrics such as accuracy and mean squared error. The accuracy of parameter predictions and the effectiveness of the optimized welding process parameters are evaluated. Furthermore, the implications of using AI algorithms for welding parameter optimization are discussed, highlighting the potential benefits and limitations of this approach. This research demonstrates the applicability and effectiveness of AI algorithms in optimizing welding process parameters. The developed machine learning model offers a promising solution to expedite the parameter optimization process, leading to improved weld quality and reduced costs. The practical implications of this study include increased productivity, enhanced weld quality control, and improved overall efficiency in the welding industry. The findings of this research contribute to the growing field of AI-assisted welding process optimization and pave the way for further advancements in this area.