MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641105078 A) filed by Vinodh S; M. Ajin; N. S. Vijaykumar; Dr. R. Kirubagharan; Selvaparvathi S; and Dr. Jeevakarunya C on September 01, 2026, for Ai-Optimized Friction Surfacing Method For Fabricating Tic–zr Reinforced Aluminium Metal Matrix Composite Coatings.
Inventors include Vinodh S; M. Ajin; N. S. Vijaykumar; Dr. R. Kirubagharan; Selvaparvathi S; and Dr. Jeevakarunya C.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: AI-Optimized Friction Surfacing Method for Fabricating TiC–Zr Reinforced Aluminium Metal Matrix Composite Coatings Abstract The present invention relates to an AI-optimized friction surfacing method for fabricating titanium carbidezirconium (TiC–Zr) reinforced aluminium metal matrix composite (MMC) coatings on metallic substrates. Unlike conventional friction surfacing processes, which rely on fixed process parameters determined through trial-and-error and offer no in-process quality assurance, the proposed method integrates real-time multi-sensor process monitoring with a machine-learning-based predictive model to continuously optimize the friction surfacing operation. A consumable rod formed from an aluminium alloy matrix incorporating titanium carbide particles (5–15 wt.%) and zirconium particles (1–5 wt.%), fabricated by solid-state compaction followed by extrusion or machining, is rotated and traversed against a prepared substrate under a controlled axial force to deposit a dense, metallurgically bonded composite coating. During deposition, axial force, torque, temperature, tool displacement and rotational speed are continuously acquired through dedicated sensors and a multichannel, high-speed data acquisition system. The acquired signals are processed by an artificial intelligence model, selected from an artificial neural network, random forest, support vector machine, or gradient boosting machine, which recognizes process-property patterns and predicts coating hardness, porosity, defect density, reinforcement distribution uniformity, and metallurgical bond strength. These predictions drive an adaptive PID/fuzzy-logic feedback controller that updates the axial force, rotational speed, traverse speed, tool tilt angle and preheat temperature set-points in real time, forming a closed-loop optimization architecture. The resulting TiC–Zr/aluminium metal matrix composite coating exhibits a dense, defect-free structure with strong metallurgical bonding and uniform reinforcement distribution, as verified through microstructural examination, microhardness testing, wear testing, X-ray diffraction (XRD) analysis, and energy-dispersive spectroscopy (EDS) mapping. Compared to conventional coatings produced by openloop friction surfacing, the invention provides improved hardness, wear and corrosion resistance, and reduced porosity, while substantially reducing dependence on empirical trial runs. The invention is suited to components used in aerospace, automotive, marine, general engineering, power generation, and defence applications that demand wear- and corrosion-resistant surfaces manufactured with consistent, qualityassured properties.
Disclaimer: Curated by HT Syndication.
