An Artificial Intelligence-Based Pick-and-Place Process Control for Quality Enhancement in Surface Mount Technology

He, Jingxi; Cen, Yuqiao; Alelaumi, Shrouq; Won, Daehan

doi:10.1109/tcpmt.2022.3215109

Cited by 9 publications

(9 citation statements)

References 27 publications

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“…Machine learning (ML) in the form of data-driven predictive modeling in manufacturing environments is gaining a lot of research interest in recent times [18][19][20]. Regression or regression analysis or regression learning is one of the widely accepted approaches for predictive modeling in many contexts, including manufacturing [21].…”

Section: Relevant Related Workmentioning

confidence: 99%

“…Therefore, it is observed that currently, the available literature does not offer an indepth investigative study where several machine learning algorithms or techniques are utilized in the same context to make a strong case for their adoption in predictive modeling in manufacturing environments. For example in [20], only five methods are investigated to make a case for optimal pick-and-place process controls that enhance the efficiency and quality of surface mount technology. The research investigations presented in this paper are devoted to this end, i.e., the investigation of multiple machine learning methods to make a case for their potential adoption in typical manufacturing processes and/or environments.…”

Section: Relevant Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Presently, contemporary manufacturing technologies now support the embedding of predictive modeling and other AI-based paradigms into many manufacturing and production processes [18][19][20]. Some of these manufacturing processes include, but are not limited to, maintenance planning, quality control, monitoring and regulation, and robust automation of shop floor operations [18][19][20]. Hence, there is a growing need to have a generic framework that supports the holistic and granular use of data sets from manufacturing processes to support data-driven innovations and inventions in manufacturing environments.…”

Section: Introductionmentioning

confidence: 99%

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A Generalized Framework for Adopting Regression-Based Predictive Modeling in Manufacturing Environments

Akinsolu

Zribi

2023

Inventions

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In this paper, the growing significance of data analysis in manufacturing environments is exemplified through a review of relevant literature and a generic framework to aid the ease of adoption of regression-based supervised learning in manufacturing environments. To validate the practicality of the framework, several regression learning techniques are applied to an open-source multi-stage continuous-flow manufacturing process data set to typify inference-driven decision-making that informs the selection of regression learning methods for adoption in real-world manufacturing environments. The investigated regression learning techniques are evaluated in terms of their training time, prediction speed, predictive accuracy (R-squared value), and mean squared error. In terms of training time (TT), k-NN20 (k-Nearest Neighbour with 20 neighbors) ranks first with average and median values of 4.8 ms and 4.9 ms, and 4.2 ms and 4.3 ms, respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of prediction speed (PS), DTR (decision tree regressor) ranks first with average and median values of 5.6784×106 observations per second (ob/s) and 4.8691×106 observations per second (ob/s), and 4.9929×106 observations per second (ob/s) and 5.8806×106 observations per second (ob/s), respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of R-squared value (R2), BR (bagging regressor) ranks first with average and median values of 0.728 and 0.728, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 0.746 and 0.746, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. In terms of mean squared error (MSE), BR (bagging regressor) ranks first with average and median values of 2.7 and 2.7, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 3.5 and 3.5, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. All methods are further ranked inferentially using the statistics of their performance metrics to identify the best method(s) for the first and second stages of the predictive modeling of the multi-stage continuous-flow manufacturing process. A Wilcoxon rank sum test is then used to statistically verify the inference-based rankings. DTR and k-NN20 have been identified as the most suitable regression learning techniques given the multi-stage continuous-flow manufacturing process data used for experimentation.

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Section: Relevant Related Workmentioning

confidence: 99%

Section: Relevant Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Generalized Framework for Adopting Regression-Based Predictive Modeling in Manufacturing Environments

Akinsolu

Zribi

2023

Inventions

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“…Statistics approaches are also applied such as failure mode and effects analysis for quality control improvement [4] and statistical process control for monitoring the quality of the manufacturing process and for obtaining quality of the end product [5]. Artificial intelligence is increasingly entering for supporting conductance of different tasks such as optimization of the components position [6], as well as machine learning for detecting patterns or anomalies on PCB [7] and deep learning for defects classification in PCB production [8].…”

Section: Introductionmentioning

confidence: 99%

Printed circuit board and printed circuit board assembly methods for testing and visual inspection: a review

Petkov,

Ivanova

2024

Bulletin EEI

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Testing and visual inspection of printed circuit boards (PCBs) and printed circuit board assemblies (PCBAs) are important procedures in the manufacturing process of electronic modules and devices related to locating and identifying possible defects and failures. Earlier defects detection leads to decreasing expenses, time and used resources to produce high quality electronics. In this paper an exploration and analysis about the current research regarding methods for PCB and PCBA testing, techniques for defects detection and vusial inspection is performed. The impact of machine and deep learning for testing and visual inspection procedures is also investigated. The used methodology comprises bibliometric approach and content analysis of papers, indexed in scientific database Scopus, considering the queries: “PCB and testing” and “PCB and testing”, “printed circuit board assembly and testing” and “PCBA and testing”, “PCB defect detection” and “PCBA defect detection”, “PCB and visual inspection”, and “PCBA and visual inspection”. The findings are presented in the form of a framework, which summarizes the contemporary landscape of methods for PCBs and PCBAs testing and visual inspection.

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Statistical-Based Pick-and-Place Control

Kim,

Won,

Yoon

2023

Lecture Notes in Mechanical Engineering

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An Artificial Intelligence-Based Pick-and-Place Process Control for Quality Enhancement in Surface Mount Technology

Cited by 9 publications

References 27 publications

A Generalized Framework for Adopting Regression-Based Predictive Modeling in Manufacturing Environments

A Generalized Framework for Adopting Regression-Based Predictive Modeling in Manufacturing Environments

Printed circuit board and printed circuit board assembly methods for testing and visual inspection: a review

Statistical-Based Pick-and-Place Control

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