MUMBAI, India, July 30 -- Intellectual Property India has published a patent application (202641088157 A) filed by G Ashwin Prabhu; Dr. Sharbani Kaushik; Mr. Venkatesh K C; Mr. R. M. Saravana Kumar; Mr. S. Balaji; Mr. S. Sivakumar; Dr. Aravindan M; Dr. S. Saravanan; and Dr. G. Ashwin Prabhu on July 20, 2026, for Ai-Driven Optimization Of Composite Machining For Aerospace Applications.
Inventors include Dr. Sharbani Kaushik; Mr. Venkatesh K C; Mr. R. M. Saravana Kumar; Mr. S. Balaji; Mr. S. Sivakumar; Dr. Aravindan M; Dr. S. Saravanan; and Dr. G. Ashwin Prabhu.
The application for the patent was published on July 24, 2026, under issue no. 30/2026.
Abstract: The invention discloses an AI-driven, closed-loop machining system for aerospace-grade carbon-fibre-reinforced polymer, glass-fibre-reinforced polymer, and fibre- metal laminate components. The system integrates a multi-axis machine tool, spindle-current sensor, three-component force sensor, acoustic-emission sensor, infrared temperature sensor, machine-vision module, and an edge-computing controller. During drilling, trimming, milling, or countersinking, sensor data are acquired at 10-50 kHz and converted into features representing thrust force, vibration, cutting temperature, fibre pull-out, delamination, tool wear, and surface roughness. A trained hybrid model comprising a physics-informed neural network and constrained Bayesian optimizer predicts machining quality and selects spindle speed between 3,000 and 18,000 rpm, feed rate between 50 and 1,200 mm/min, depth of cut between 0.1 and 3.0 mm, and coolant or minimum-quantity-lubrication flow between 20 and 150 mL/h. The controller updates operating commands at intervals not exceeding 100 ms while maintaining predetermined limits, including thrust force below 120 N, laminate temperature below 180°C, delamination factor not exceeding 1.15, and arithmetic surface roughness below 2.0 µm. For a representative 6-12 mm composite panel machined using 6-10 mm carbide or diamond-coated tools, the optimization objective jointly minimizes defect probability, energy consumption, tool degradation, and cycle time. Relative to fixed-parameter machining, the disclosed configuration is designed to achieve 20-35% lower delamination, 15-25% longer tool life, 10-18% lower specific energy consumption, and 18-30% shorter machining time, subject to validation using statistically significant comparative trials. The technical advancement arises from real-time sensor fusion and automatic physical adjustment of machining parameters, rather than offline software-only recommendation. This produces repeatable hole and edge quality despite laminate variability, fibre orientation, tool ageing, and thermal sensitivity, thereby reducing rejection, rework, and production cost in aircraft structural-component manufacturing. The system stores traceable process signatures for each component and triggers tool replacement when predicted remaining useful life falls below 10%, supporting aerospace quality assurance and maintenance planning.
Disclaimer: Curated by HT Syndication.
