Combining business process and failure modelling to increase yield in electronics manufacturing

Abstract

The prediction and capturing of defects in low-volume assembly of electronics is a technical challenge that is a prerequisite for design for manufacturing (DfM) and business process improvement (BPI) to increase first-time yields and reduce production costs. Failures at the component-level (component defects) and system-level (such as defects in design and manufacturing) have not been incorporated in combined prediction models. BPI efforts should have predictive capability while supporting flexible production and changes in business models. This research was aimed at the integration of enterprise modelling (EM) and failure models (FM) to support business decision making by predicting system-level defects. An enhanced business modelling approach which provides a set of accessible failure models at a given business process level is presented in this article. This model-driven approach allows the evaluation of product and process performance and hence feedback to design and manufacturing activities hence improving first-time yield and product quality. A case in low-volume, high-complexity electronics assembly industry shows how the approach leverages standard modelling techniques and facilitates the understanding of the causes of poor manufacturing performance using a set of surface mount technology (SMT) process failure models. A prototype application tool was developed and tested in a collaborator site to evaluate the integration of business process models with the execution entities, such as software tools, business database, and simulation engines. The proposed concept was tested for the defect data collection and prediction in the described case study.

Integrating the Taguchi method and the multiattribute decision-making method to optimize the surface mount technology solder paste printing thickness process

“…The experimental results of this study can provide a reference to the SMT industry for improving the product quality. 1,2,11,12,16,18,21,24–26…”