Thesis

A digital twin-driven ultra-precision machining system for future smart manufacturing

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Awarding institution
  • University of Strathclyde
Date of award
  • 2026
Thesis identifier
  • T17978
Person Identifier (Local)
  • 202055497
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Department, School or Faculty
Abstract
  • The UK manufacturing industry is undergoing digital transformation to improve productivityand support the production of high-value components required for sectors such as energy,transport, and healthcare. In ultra-precision manufacturing, productivity is limited by dynamicerrors caused by machine motion, structural compliance, and inertial effects. Many of theseerrors occur outside the machine servo control loop and cannot be measured or corrected usingconventional encoder-based feedback systems, limiting achievable machining accuracy,automation, and production speed.This research investigates a digital twin-driven approach for measuring, predicting, andcompensating for dynamic errors in ultra-precision manufacturing systems. The proposedframework integrates a collaborative robot (COBOT) and a CNC hybrid milling machine,enabling automated workpiece handling and machining within a coordinated digital twinarchitecture.A novel sensing methodology using triaxial accelerometers is developed to measure machinedynamic behaviour outside the control loop. Machine displacement is estimated through signalprocessing techniques, including fast Fourier transform (FFT) filtering and double integrationof acceleration data. Experimental validation using ball bar testing demonstrates that thisapproach improves COBOT tracking accuracy, achieving an average improvement of 41.3%compared with encoder-based measurements.To enable real-time implementation, explainable artificial intelligence (XAI) models areintroduced to predict dynamic error directly from acceleration signal features. Using theQLattice modelling approach, interpretable mathematical expressions are generated to estimatedynamic errors with reduced computational requirements. Experimental evaluation shows thatthe COBOT digital twin reduces placement error in automated pick-and-place tasks by 31.5% for MoveJ motions and 4% for MoveL motions. The Move Joint (MoveJ) commands, wherethe robot follows the most efficient joint path between two points, and Move Linear (MoveL)commands, where the Tool Centre Point (TCP) moves in a straight line between two positions.Application of the approach to the hybrid mill demonstrated the ability to detect machinedynamic behaviour; however, reliable dynamic error correction at micron-level precision waslimited by sensor resolution, accelerometer frequency response, and environmental variation.A proof-of-concept smart manufacturing demonstrator integrating the COBOT and hybrid millwas implemented using a CubeSat end cap component. While full closed-loop correctivemachining was not achieved, the system successfully demonstrated coordinated robotichandling and machining within a digital twin framework.Overall, this research demonstrates that accelerometer-based digital twins combined withexplainable AI provide a viable approach for measuring and compensating dynamic errorsoutside the control loop, particularly for robotic workpiece handling. The work establishes afoundation for future development of predictive digital twin systems capable of improvingprecision, automation, and productivity in ultra-precision manufacturing.
Advisor / supervisor
  • Yang, Erfu
  • Luo, Xichun (Manufacturing teacher)
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DOI
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file details PDF of thesis T17978 2026-04-29 Public Download