Integrated die-cast components reduce machining/assembly steps and improve mechanical dynamic characteristics, eliminating joint loosening/fracture risks after long-term use. However, the highly variable geometries and random spatial distributions of burrs, flash, parting lines, and risers in castings invalidate pre-programmed or teach-in robotic grinding methods. This paper reviews recent progress and future trends in robotic grinding, analyzing four core aspects: force control stability/adaptability (e.g., adaptive impedance control can reduce average force-tracking error to 0.38 N), trajectory planning/path generation (e.g., error-driven compensation can lower contour error by 34.2–55.1%), process parameter optimization, and challenges of sensing latency/quality evaluation (e.g., deep learning models achieve 97.64% accuracy in identifying abrasive belt wear states). The key enabling technologies are summarized, including active/passive compliant force control, model-/data-driven adaptive trajectory planning, intelligent process parameter optimization integrating physical mechanisms and data-driven approaches, and multi-modal state monitoring with online quality assessment. Representative applications (metal castings, aero-engine blades, thin-walled components, weld seams) are presented, and prospective research directions are proposed. This paper provides a comprehensive reference for theoretical research and engineering practice in this field.
Qiao et al. (Fri,) studied this question.