.png){kind=icon}
.png){kind=icon}
Return
The process-oriented multivariate capability index
.png){kind=icon}
PRE
AI
DOI：10.1080/00207540412331530158.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recent literature has proposed multivariate capability indices, but does not suggest a method for measuring quality characteristics in a way that links production irregularities directly to their causes. Our objective is to present a new approach to multivariate capability indices that uses process-oriented basis representation (POBREP) which allows the computing of cause-related index values. The proposed method focuses on independent process-oriented multivariate data by employing regression coefficients as data. These coefficients measure the amount of the characteristic patterns induced by particular problems or incidents that can occur in the system. Two examples from the electronics industry ( the chip capacitor process and solder paste process) use simulated data and Monte Carlo integration to demonstrate the new process-oriented capability method. A reduction of estimation error was realized when using process-oriented capability. For the chip capacitor problem, capability error is 24 - 54% when using ordinary multivariate data. However, when using process-oriented data the error is less than 3%. Capability is difficult to compute from sample data in the solder paste example without the process-oriented approach. Future research should propose a multivariate capability measure for dependent process-oriented data.
Keywords:
capability
process-oriented
multivariate
indices
Monte Carlo simulation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.3
Papers:
1.1W
Citations:
3.7W
Organization
No organization information available