One of the most suitable methods for the mass production of complicated shapes is injection molding due to its superior production rate and quality. The key to producing higher quality products in injection molding is proper injection speed, pressure, and mold design. Conventional methods relying on the operator’s expertise and defect detection techniques are ineffective in reducing defects. Hence, there is a need for more close control over these operating parameters using various machine learning techniques. Neural networks have considerable applications in the injection molding process consisting of optimization, prediction, identification, classification, controlling, modeling, and monitoring, particularly in manufacturing. In recent research, many critical issues in applying machine learning and neural network in injection molding in practical have been addressed. Some problems include data division, collection, and preprocessing steps, such as considering the inputs, networks, and outputs, algorithms used, models utilized for testing and training, and performance criteria set during validation and verification. This review briefly explains working on machine learning and artificial neural network and optimizing injection molding in industries.
Any metal surface’s usefulness is essential in various applications such as machining and welding and aerospace and aerodynamic applications. There is a great deal of wear in metals, used widely in machines and appliances. The gradual loss of the upper metal layers in all metal parts is inevitable over the machine or component’s lifetime. Artificial intelligence implementations and computational models are being studied to evaluate different metals’ tribological behavior, as technological progress has been made in this field. Different neural networks were used for different metals. They are classified in this paper, together with a description of their benefits and inconveniences and an overview and use of the different types of wear. Artificial intelligence is a relatively new term that uses mechanical engineering. There is still no scientific progress to examine various metal wear cases and compare AI and computational models’ accuracy in wear behavior.
Artificial Intelligence has left no stone unturned, and mechanical engineering is one of its biggest consumers. Such technological advancements in metal melting can help in process simplification, hazard reduction, human involvement reduction & lesser process time. Implementing the AI models in the melting technology will ultimately help various industries, i.e., Foundry, Architecture, Jewelry Industry, etc. This review extensively sheds light on Artificial Intelligence models implemented in metal melting processes or the metal melting aspect, alongside explaining additive manufacturing as a competitor to the current melting processes and its advances in metal melting and AI implementations.
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