Analysis and optimization in smart manufacturing based on a reusable knowledge base for process performance models

2015 ◽  
Author(s):  
Alexander Brodsky ◽  
Guodong Shao ◽  
Mohan Krishnamoorthy ◽  
Anantha Narayanan ◽  
Daniel Menasce ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Smart Manufacturing ◽  
Process Performance ◽  
Performance Models

scholarly journals Analysis and Optimization based on Reusable Knowledge Base of Process Performance Models

2015 ◽  
Author(s):  
Alexander Brodsky ◽  
Guodong Shao ◽  
Mohan Krishnamoorthy ◽  
Anantha Narayanan ◽  
Daniel Menasc ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Process Performance ◽  
Performance Models

scholarly journals Analysis and optimization based on reusable knowledge base of process performance models

2016 ◽  
Vol 88 (1-4) ◽  
pp. 337-357 ◽  
Author(s):  
Alexander Brodsky ◽  
Guodong Shao ◽  
Mohan Krishnamoorthy ◽  
Anantha Narayanan ◽  
Daniel Menascé ◽  
...  
Keyword(s):  
Knowledge Base ◽  
Process Performance ◽  
Performance Models

Multiproduct batch plants design using linear process performance models

AIChE Journal ◽  
2010 ◽  
Vol 57 (1) ◽  
pp. 122-135 ◽  
Author(s):  
Marta S. Moreno ◽  
Jorge M. Montagna
Keyword(s):  
Linear Process ◽  
Process Performance ◽  
Performance Models ◽  
Batch Plants

scholarly journals Analyse usage of process performance models to predict customer satisfaction

2012 ◽  
Vol 47 (12) ◽  
pp. 35-40 ◽  
Author(s):  
J. Venkatesh ◽  
Priyesh Cherurveettil ◽  
Thenmozhi. S ◽  
Balasubramanie. P
Keyword(s):  
Customer Satisfaction ◽  
Process Performance ◽  
Performance Models

scholarly journals An intelligent approach for improving printed circuit board assembly process performance in smart manufacturing

2020 ◽  
Vol 12 ◽  
pp. 184797902094618
Author(s):  
Vincent WC Fung ◽  
Kam Chuen Yung
Keyword(s):  
Printed Circuit Board ◽  
Circuit Board ◽  
Smart Manufacturing ◽  
Process Performance ◽  
Solder Paste ◽  
Assembly Process ◽  
Printed Circuit Board Assembly ◽  
Printed Circuit ◽  
Circuit Board Assembly ◽  
Solder Paste Printing

The 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.

Developing Predictive Engineering Analytics to Formulate the Closed-Loop Management for Achieving Re-Industrialisation

2021 ◽  
Vol 13 (2) ◽  
pp. 1-22
Author(s):  
Vincent Wah Cheong Fung ◽  
Kam Chuen Yung
Keyword(s):  
Closed Loop ◽  
Printed Circuit Board ◽  
Intelligent Manufacturing ◽  
Circuit Board ◽  
Smart Manufacturing ◽  
Process Performance ◽  
Printed Circuit Board Assembly ◽  
Circuit Board Assembly ◽  
And Performance ◽  
High Level

Regarding the process of printed circuit board assembly (PCBA), existing failure location methods are reactive in nature, while process parameters and performance cannot be predicted to achieve a high level of operational excellence. Designated PCB designs are not customized for specific manufacturing sites, while process performance becomes uncertain to clients and manufacturers. In this paper, an intelligent manufacturing performance predictive framework (IMPPF) is proposed in this paper, which structures the predictive engineering analytics for the smart manufacturing. First, the data collection from the PCBA process is structured by means of multi-responses Taguchi method, which guarantees the data reliability and quality. Second, the artificial neural network is adopted to learn from the existing operational data so as to provide the prediction on machine settings and process performance at the Gerber drawing stage. The contribution of this study is mainly to establish a closed-loop framework to facilitate the predictive engineering analytics for achieving re-industrialization.

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