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dc.contributorDepartment of Industrial and Systems Engineering-
dc.creatorFung, VWC-
dc.creatorYung, KC-
dc.publisherSAGE Publicationsen_US
dc.rights© The Author(s) 2020en_US
dc.rightsCreative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License ( which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (
dc.rightsThe following publication Fung, V. W. C., & Yung, K. C. (2020). An intelligent approach for improving printed circuit board assembly process performance in smart manufacturing. International Journal of Engineering Business Management, 12, 1-12 is available at
dc.subjectSmart manufacturingen_US
dc.subjectPrinted circuit board assemblyen_US
dc.subjectK-means clusteringen_US
dc.subjectMulti-response Taguchi methoden_US
dc.subjectProcess improvementen_US
dc.titleAn intelligent approach for improving printed circuit board assembly process performance in smart manufacturingen_US
dc.typeJournal/Magazine Articleen_US
dcterms.abstractThe process of printed circuit board assembly (PCBA) involves several machines, such as a stencil printer, placement machine and reflow oven, to solder and assemble electronic components onto printed circuit boards (PCBs). In the production flow, some failure prevention mechanisms are deployed to ensure the designated quality of PCBA, including solder paste inspection (SPI), automated optical inspection (AOI) and in-circuit testing (ICT). However, such methods to locate the failures are reactive in nature, which may create waste and require additional effort to be spent re-manufacturing and inspecting the PCBs. Worse still, the process performance of the assembly process cannot be guaranteed at a high level. Therefore, there is a need to improve the performance of the PCBA process. To address the aforementioned challenges in the PCBA process, an intelligent assembly process improvement system (IAPIS) is proposed, which integrates the k-means clustering method and multi-response Taguchi method to formulate a pro-active approach to investigate and manage the process performance. The critical process parameters are first identified by means of k-means clustering and the selected parameters are then used to formulate a set of experimental studies by using the multi-response Taguchi method to optimize the performance of the assembly process. To validate the proposed system, a case study of an electronics manufacturer in the solder paste printing process was conducted. The contributions of this study are two-fold: (i) pressure, blade angle and speed are identified as the critical factors in the solder paste printing process; and (ii) a significant improvement in the yield performance of PCBA can be achieved as a component in the smart manufacturing.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationInternational journal of engineering business management, 1 Jan. 2020, v. 12, p. 1-12-
dcterms.isPartOfInternational journal of engineering business management-
dc.description.validate202009 bcrc-
dc.description.oaVersion of Recorden_US
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