A COMPUTATIONALLY-STRUCTURED FRAMEWORK FOR GENERALIZABLE AND EXPLAINABLE AI IN INJECTION MOLDING: INTEGRATING DATA STANDARDIZATION, TRANSFER LEARNING, AND EDGE DEPLOYMENT

The emergence of Artificial Intelligence (AI) and Machine Learning (ML) is now the key element of modern manufacturing, but the majority of the models applied in injection molding are domain-specific and cannot be generalized to other machines, materials, or process conditions. The given paper deals with this computing problem by developing a structured literature review devoted to the issue of the generalization and transferability of industrial AI systems. Based on this discussion, a conceptual framework of 5 steps is put forward to formalize the way in which cross-domain robustness may be attained based on fundamental computing systems like data normalization, feature relevance control, regularization, domain adaptation, and explainable modeling. The stages are all based on the latest developments in AI, the introduction of digital-twin, and the integration of hybrid edge-cloud architecture. The framework suggested opens up a hypothetical basis of constructing domain resistant and intelligible artificial pipelines, that links machine particular learning with scalable and transferable intelligence. This effort to coordinate the data of industrial processes with the principles of reproducibility and generalization in computing leads to the further development of both AI methodology and practical use of smart manufacturing in the direction of the reliable and cross-domain automatization.