ENGINEERING DECISION SYSTEMS FOR METAL FABRICATION: VYACHESLAV SHARGAEV'S APPLIED AI APPROACH

Abstract

Closed-loop artificial intelligence is increasingly claimed for metal-fabrication systems that combine reinforcement-learning-based process control with vision-based quality inspection. This review organizes the reinforcement-learning, computer-vision, and production-economics literature bearing on metal cutting around three conditions such claims can be checked against: unification of the controller's reward or state signal with the vision classifier's output, validation under production-representative process variability, and calibration of the reward function against a bounded economic objective. Shargaev's three publications, a monograph, a patent application, and a journal article, are the only body of work in this literature that specifies both architectures in comparable technical detail, which makes his research program the natural case for tracing where each condition currently stands and where it remains a stated design intention rather than a demonstrated result. The review closes by specifying the architecture, validation protocol, and reward calibration a demonstrated implementation would require, giving a concrete target against which future claims of integrated, quality-aware cutting control can be measured.