Hazard regression modeling for robotic performance prediction

Mingyang Li, Heping Chen, Biao Zhang, Jian Liu, Byoung Uk Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Robotic systems are widely applied in process industry to reduce manufacturing labor costs and increase production productivity. Due to the uncertainties existed in the manufacturing environment, the performance improvement of the assembly process is important yet challenging. This paper proposes a regression-based method to predict the performance of the robotic assembly process. Statistical hazard models are introduced to quantify the influence of possible controllable parameters on the process performance metrics. A real-world case study of an assembly production process is provided to demonstrate the effectiveness of the proposed method.

Original languageEnglish (US)
Title of host publicationIIE Annual Conference and Expo 2014
PublisherInstitute of Industrial Engineers
Number of pages7
ISBN (Electronic)9780983762430
StatePublished - 2014
EventIIE Annual Conference and Expo 2014 - Montreal, Canada
Duration: May 31 2014Jun 3 2014

Publication series

NameIIE Annual Conference and Expo 2014


OtherIIE Annual Conference and Expo 2014


  • Hazard modeling
  • Instantaneous succeeding rate
  • Process improvement
  • Robotic assembly systems

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Control and Systems Engineering


Dive into the research topics of 'Hazard regression modeling for robotic performance prediction'. Together they form a unique fingerprint.

Cite this