Intelligent support in manufacturing process selection based on artificial neural networks, fuzzy logic, and genetic algorithms: Current state and future perspectives

Technological advances, dynamic customer needs, growing uncertainty, and the imperative for sustainable development pressure manufacturing entities to enhance productivity and competitiveness. In this challenging landscape, decision-making in manufacturing process selection is critical. Adopting intelligent support is essential for balancing performance and costs through optimal process selection. Through a comprehensive review of 93 studies published between 2013 and 2023, this paper aims to provide a profound understanding of intelligent support in manufacturing process selection. The findings, which indicate significant interest in intelligent methodologies for manufacturing process selection, are of great importance. Fuzzy logic is prevalent in additive manufacturing due to its ability to handle complex and imprecise data. At the same time, artificial neural networks are favored in conventional manufacturing for leveraging extensive historical data. Genetic algorithms are primarily used for optimization challenges. As manufacturing evolves with new technologies and complex materials, this paper advocates adopting a generalized matrix learning vector quantization neural network for efficient and intelligent process selection in additive and conventional approaches due to its capacity to leverage historical data and handle complex and high dimensional data.