Publications

• Online tool condition monitoring for ultrasonic metal welding via sensor fusion and machine learning
  Qasim Nazir, Chenhui Shao
  Journal of Manufacturing Processes, 2020, paper
  Abstract: In ultrasonic metal welding (UMW), tool wear significantly affects the weld quality and tool maintenance constitutes a substantial part of production cost. Thus, tool condition monitoring (TCM) is crucial for UMW. Despite extensive literature focusing on TCM for other manufacturing processes, limited studies are available on TCM for UMW. Existing TCM methods for UMW require offline high-resolution measurement of tool surface profiles, which leads to undesirable production downtime and delayed decision-making. This paper proposes a completely online TCM system for UMW using sensor fusion and machine learning (ML) techniques. A data acquisition (DAQ) system is designed and implemented to obtain in-situ sensing signals during welding processes. A large feature pool is then extracted from the sensing signals. A subset of features are selected and subsequently used by ML-based classification models. A variety of classification models are trained, validated, and tested using experimental data. The best-performing classification models can achieve close to 100% classification accuracy for both training and test datasets. The proposed TCM system not only provides real-time TCM for UMW but also can support optimal decision-making in tool maintenance. The TCM system can be extended to predict remaining useful life (RUL) of tools and integrated with a controller to adjust welding parameters accordingly.
  
  

• Accuracy Analysis of 3-RSS Delta Parallel Manipulator
  Mansoor Ghazi, Qasim Nazir, Sajid Ullah Butt, Aamer Ahmed Baqai
  Procedia Manufacturing, 2018, paper
  Abstract: Accuracy analysis of parallel manipulators is the first step in selecting an appropriate error model for further design. In this paper, kinematic accuracy of a Delta parallel manipulator is evaluated by Jacobian and geometric error models. The Jacobian (or condition number) based error models have been widely used for analysis and optimal design of parallel manipulators. However, as it is highlighted in this study, these models are dependent on the choice of particular matrix norm and do not capture the directional nature of accuracy. The geometric error model, derived for the Delta parallel manipulator, computes the exact value of positioning errors in task space that arise due to errors in joint space. The proposed model is used to compute overall as well as individual positioning errors along each DOF. It is revealed that; kinematic accuracy exhibits a highly directional nature over the reachable workspace. Moreover, individual errors along each DOF should be analyzed for complete evaluation of accuracy of the Delta parallel manipulator.