| Sponsor |
|
Dedicated Tool for Process Development
Advanced plating capability at Uyemura Tech Center: Process Development, UBM, pilot production. ENIG, ENEPIG, EPIG/EPAG, RAIG immersion gold; all substrates. On-site analysis.
Uyemura
|
|
Machine Learning-based Server Testing for Large-Scale PCB Manufacturing
This paper presents a Decision Tree based Machine Learning to streamline server testing and debugging processes within large-scale PCB manufacturing.
Production Floor
DOWNLOAD
|
Authored By:
Soujanya Nagaraja Rao Malur, Darshil Patel,Daryl Santos
School of Systems Science and Industrial Engineering SUNY at Binghamton
NY, USA
Aditya Shobhawat, Snehal Shindadkar,
Foxconn Industrial Internet
WI, USA
Summary
As the complexity and scale of Printed Circuit Board (PCB) manufacturing continue to increase, the reliability and efficiency of server systems become increasingly critical. This paper presents a novel Decision Tree (DT)-based Machine Learning (ML) framework designed to streamline server testing and debugging processes within large-scale PCB manufacturing environments. The central aim is to improve the efficiency and accuracy of identifying and resolving server-related issues, ultimately minimizing downtime and associated maintenance costs. The framework leverages data from diverse sources, including cloud-based server performance metrics, error logs, and testing outcomes.
After meticulous data preprocessing, the framework analyzes these datasets to uncover relationships among failure symptoms, required actions, implicated server components, and corresponding timeframes. We evaluate various ML algorithms including DT, Random Forest Regression (RFR), K-nearest Neighbors (KNN), with the DT model demonstrating the highest accuracy (94%). A key feature of the framework is a tri-partite graph that connects failure symptoms to recommended actions and the relevant server components. This graph is weighted by both resolution time and likelihood of success, enabling the identification of the most efficient troubleshooting paths.
Implementing this framework across server environments enhances fault detection capabilities and bolsters overall operational stability, leading to reduced downtime. Moreover, it streamlines debugging processes, resulting in cost savings and diminished risks of unexpected failures. By optimizing server edge through improved productivity and reduced operational expenses. In conclusion, this framework signifies a significant advancement in leveraging ML for industrial applications, particularly in the domain of server reliability for PCB manufacturing. The benefits extend beyond operational efficiency to encompass cost reductions, proactive maintenance strategies, and enhanced product quality assurance, ultimately strengthening the industry’s competitive position in a challenging market.
Conclusions
In this study, the Decision Tree (DT) model distinguished itself as the most accurate among the various algorithms explored, achieving an impressive accuracy of 94%. This performance underscores its robustness in analyzing complex datasets, effectively identifying intricate patterns and relationships that might be missed by other models. The transparency of the DT model is a significant advantage, as it not only provides predictions but also actionable insights that are crucial for informed decision-making. The model’s capability to differentiate between value-added and non-value-added items further enhances its utility.
By categorizing and quantifying these elements, the DT model helps organizations assess the impact of different processes and pinpoint areas where improvements can be made. This enables a focus on optimizing value-added activities while addressing and reducing non-value-added elements, leading to more strategic and impactful decision-making. Additionally, the Tripartite graph has proven to be a pivotal tool for visualizing and understanding the relationships between data points. This graphical representation complements the findings from the DT model by offering a clearer view of constraints and dependencies within the data.
It facilitates a comprehensive analysis by highlighting critical areas for intervention, revealing interactions and dependencies that affect overall outcomes, and providing a basis for targeted improvements. Combining the DT model’s insights with the Tripartite graph’s visualization capabilities creates a robust framework for analyzing and improving complex systems. This integrated approach ensures a thorough evaluation of the data, supports the identification of critical areas for improvement, and enhances overall performance and efficiency.
By leveraging both the DT model’s predictive power and the Tripartite graph’s visual clarity, organizations can achieve a more nuanced understanding of their processes, leading to better decision-making and operational excellence. Overall, this combined approach not only offers a comprehensive view of the data but also provides actionable insights that drive performance improvements. The synergy between the DT model and the Tripartite graph ensures that organizations can effectively analyze, optimize and enhance their systems, leading to more informed decisions and greater operational success.
Despite the success of the Decision Tree (DT) model, future research can greatly impact PCB manufacturing and server testing by exploring alternative approaches to decision-making.
Enhancing model accuracy could involve integrating advanced methods to provide deeper insights and more precise predictions. In PCB manufacturing, adopting techniques for data augmentation and advanced processing of unstructured information from maintenance logs can improve the analysis of production data. These methods might lead to a 10-20% increase in accuracy for issue detection and decision- making, potentially boosting overall production efficiency by up to 15%.
For server testing, expanding datasets and applying advanced diagnostic methods can refine failure predictions and system performance forecasts. Approaches for optimizing maintenance and automating model tuning could enhance predictive accuracy by 15- 25% and reduce downtime by approximately 20-30%, leading to more reliable system performance. Regarding visualization, alternatives to the Tripartite graph, such as weighted graphs or network analysis, can use mathematical algorithms like shortest path and centrality measures to reveal data relationships.
Dimensionality reduction methods, such as Multidimensional Scaling (MDS) and t-Distributed Stochastic Neighbor Embedding (t-SNE), can simplify high-dimensional data, potentially improving pattern recognition by up to 25%. Mathematical modeling and simulation can also provide valuable predictive insights based on historical data, supporting scenario analysis and optimization. By developing standardized modules for these advanced techniques, organizations could improve consistency, efficiency, and decision-making capabilities.
Implementing these methods could lead to a 10-20% improvement in decision-making accuracy and operational efficiency in both PCB manufacturing and server testing. Overall, exploring these alternative approaches can lead to more effective decision-making processes, enabling organizations to achieve higher efficiency, better performance, and more reliable outcomes.
Initially Published in the SMTA Proceedings
|
Free Newsletter Subscription
Circuitnet is built for professionals who bear the responsibility of looking ahead, imagining the future, and preparing for it.
Insert Your Email Address
|
| Sponsor |
|
Bring IPC Training to Your Facility
Mobile training center offers IPC certified classes at your company’s facility making solder training effective and convenient for your entire team.
BEST Inc.
|
|
|
