Machining using industrial robots is currently limited to applications with low geometrical accuracies and soft materials. This paper analyzes the sources of errors in robotic machining and characterizes them in amplitude and frequency. Experiments under different conditions represent a typical set of industrial applications and allow a qualified evaluation. Based on this analysis, a modular approach is proposed to overcome these obstacles, applied both during program generation (offline) and execution (online). Predictive offline compensation of machining errors is achieved by means of an innovative programming system, based on kinematic and dynamic robot models. Real-time adaptive machining error compensation is also provided by sensing the real robot positions with an innovative tracking system and corrective feedback to both the robot and an additional high-dynamic compensation mechanism on piezo-actuator basis.


    Access

    Download


    Export, share and cite



    Title :

    Improving robotic machining accuracy through experimental error investigation and modular compensation



    Publication date :

    2016-01-01



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    Enhanced machining center accuracy through real time error compensation

    Lau,H. / Ma,Q. / Lau,K.C. et al. | Automotive engineering | 1996


    Enhanced Machining Center Accuracy Through Real Time Error Compensation

    Tata, Bhavani / Seth, Brij B. / Tonello, E. T. et al. | SAE Technical Papers | 1996


    Improving Robotic Accuracy through Iterative Teaching

    Sawyer, Daniela / Tinkler, Lloyd / Roberts, Nathan et al. | British Library Conference Proceedings | 2020


    Improving Robotic Accuracy through Iterative Teaching

    Sawyer, Daniela / Tinkler, Lloyd / Roberts, Nathan et al. | British Library Conference Proceedings | 2020


    Improving Robotic Accuracy through Iterative Teaching

    Sawyer, Daniela / Tinkler, Lloyd / Roberts, Nathan et al. | SAE Technical Papers | 2020