Singularity issue in dimensional variation diagnosis of multi-station assembly processes

Yu Ding*, Abhishek Gupta, Daniel Apley

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review


This paper presents a new method of diagnosing variation components of process error sources in a manufacturing system. Quite often in a complex multi-station assembly system only limited numbers of sensors are present due to which complete information of fixture errors is unavailable. This makes the system of variance components singular and not solvable by using regular least-squares estimation. The method suggests reformulation of the original error propagation model into a variation relation by using a matrix transformation. With the development of a new variation estimator and its diagnosability condition, some singular systems that are not diagnosable using traditional least-squares methods become diagnosable. Difference between the new approach and the traditional approaches has been elaborated. Modified procedures of the new estimator are also presented to enhance its estimation performance. The idea is presented in the specific context of panel assembly processes, but the application of the idea should not be limited therein. Conclusions can be extended to general discrete-part manufacturing processes where fixtures are extensively used to ensure dimensional accuracy of the final product.

Original languageEnglish (US)
Number of pages12
StatePublished - Jan 1 2003
Event2003 ASME International Mechanical Engineering Congress - Washington, DC, United States
Duration: Nov 15 2003Nov 21 2003


Other2003 ASME International Mechanical Engineering Congress
Country/TerritoryUnited States
CityWashington, DC


  • Fault diagnosis
  • Least squares estimation
  • Multi-station assembly processes
  • System singularity

ASJC Scopus subject areas

  • Engineering(all)


Dive into the research topics of 'Singularity issue in dimensional variation diagnosis of multi-station assembly processes'. Together they form a unique fingerprint.

Cite this