Data-Driven Classification of Screwdriving Operations
- 2.6k Downloads
Consumer electronic devices are made by the millions, and automating their production is a key manufacturing challenge. Fastening machine screws is among the most difficult components of this challenge. To accomplish this task with sufficient robustness for industry, detecting and recovering from failure is essential. We have built a robotic screwdriving system to collect data on this process. Using it, we collected data on 1862 screwdriving runs, each consisting of force, torque, motor current and speed, and video. Each run is also hand-labeled with the stages of screwdriving and the result of the run. We identify several distinct stages through which the system transitions and relate sequences of stages to characteristic failure modes. In addition, we explore several techniques for automatic result classification, including standard maximum angle/torque methods and machine learning time series techniques.
KeywordsScrewdriving Automation Assembly Manufacturing
The authors gratefully acknowledge the financial support from Foxconn.
- 1.Jia, Z., Bhatia, A., Aronson, R., Bourne, D., Mason, M.T.: A survey of automated threaded fastening. In preparationGoogle Scholar
- 2.Nicolson, E.J.: Grasp stiffness solutions for threaded insertion. Master’s thesis, University of California, Berkeley (1990)Google Scholar
- 3.Whitney, D.E., Mechanical Assemblies: Their Design, Manufacture, and Role in Product Development, vol. 1. Oxford University Press, New York (2004)Google Scholar
- 4.Bickford, J.H.: Handbook of Bolts and Bolted Joints. CRC Press, Boca Raton (1998)Google Scholar
- 5.Bickford, J.H.: Introduction to the Design, Behavior of Bolted Joints: Non-gasketed Joints. CRC Press, New York (2007)Google Scholar
- 6.ISO: ISO 5393: Rotary tools for threaded fasteners - performance test method. ISO, Technical Report (2013)Google Scholar
- 8.Matsuno, T., Huang, J., Fukuda, T.: Fault detection algorithm for external thread fastening by robotic manipulator using linear support vector machine classifier. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 3443–3450. IEEE (2013)Google Scholar
- 11.Giannoccaro, N.I., Klingajay, M.: Online identification for the automated threaded fastening using GUI format. In: Lazinica, A., Merdan, M. (eds.) Cutting Edge Robotics, vol. Kordic, pp. 727–745. Pro Literatur Verlag, Germany (2005)Google Scholar
- 12.MicroTorque-ToolsTalk MT User Guide, Atlas CopcoGoogle Scholar
- 13.Guillame-Bert, M.: Official home of the Honey programming language (2016). http://framework.mathieu.guillame-bert.com/. Accessed 06 June 2016
- 14.Guillame-Bert, M., Dubrawski, A.: Classification of time sequences using graphs of temporal constraints. J. Mach. Learn. Res. (2016, in review)Google Scholar