Nia Bowers
Yaw Wing Sam presents at the 2026 China Jiangsu Talent Innovation and Entrepreneurship Competition, Western United States Regional Qualifier, in Los Angeles on July 16, 2026. Photo courtesy of Virscend University.
With more than two decades of cross-border experience spanning automotive, consumer electronics, automation equipment, and energy storage, Yaw Wing Sam has built his career at the intersection of manufacturing, quality engineering, and operational excellence. Today, his work increasingly focuses on how artificial intelligence (AI) and manufacturing data can help companies identify quality risks earlier, prevent defects, and improve product reliability.
Drawing on extensive experience in manufacturing operations, supplier development, process improvement, and cross-functional leadership, Sam’s current research explores practical applications of AI in quality-risk prediction, intelligent inspection, and life-cycle quality management. His work reflects a broader shift in manufacturing from reactive inspection toward predictive, data-driven approaches to quality.
Research in AI-Enabled Quality Control
In 2026, Sam published four papers examining the application of artificial intelligence to manufacturing quality and energy storage.
In “An Intelligent Decision Support System for Quality Control in Energy Storage Manufacturing,” published in the Journal of Computer, Signal, and System Research, Sam proposes an intelligent decision-support framework designed to move lithium-ion battery manufacturing from reactive quality inspection toward earlier intervention.
His paper, “Artificial Intelligence-Based Quality Risk Prediction Framework for Energy Storage Manufacturing using ISFLA-Optimized LightGBM,” published in the GBP Proceedings Series as part of BEEPA 2026, presents an AI-based quality-risk prediction framework integrating Industrial Internet of Things (IIoT) data, 5G communication, edge computing, and an optimized LightGBM model. In its reported industrial application, the model achieved 95.72% classification accuracy in identifying quality-risk levels and was associated with a 3.2% increase in production yield.
Another study, “AI-Driven Quality Engineering Framework for Life-Cycle Management of Energy Storage Batteries in Smart Manufacturing Systems,” published in the Journal of Engineering Science & Applications, proposes a three-layer framework covering battery design validation, manufacturing, use, and end-of-life diagnostics. The framework incorporates concepts including physics-informed machine learning and federated learning.
Sam’s fourth 2026 publication, “Data-Driven Defect Reduction in Energy Storage System Manufacturing: A Review of Predictive Quality Control and Intelligent Inspection Technologies,” published in the International Journal of Engineering Advances, reviews predictive quality control, multimodal sensing, and digital-twin technologies for energy storage manufacturing. The paper proposes an integrated conceptual framework for reducing defects through intelligent inspection and data-driven quality management.
Taken together, these publications explore how AI, industrial data, and advanced analytics can enable earlier identification of quality risks, more predictive approaches to manufacturing control, and improved reliability across the energy storage life cycle.
Professional Review and Evaluation
Sam has also contributed his expertise to professional project-review activities focused on manufacturing quality management and AI applications.
For the International Association for Engineering and Technology Development (IAETD), Sam served as an expert reviewer during the 2025 Advanced Manufacturing Quality Management and AI Application Innovation Project Review. His evaluation focused on areas including technological innovation, engineering feasibility, and potential industrial value.
He was also appointed a Review Expert by the Asia Pacific Economic Trade Forum (APETF) for the 2024 Advanced Manufacturing Quality Management and Smart Manufacturing Application Achievement Review. In that role, he evaluated projects based on professional rigor, innovation, practical feasibility, and potential regional industry impact.
Professional Memberships and Entrepreneurship
Sam is a professional member of the American Society of Mechanical Engineers (ASME) and the Institute of Industrial and Systems Engineers (IISE), reflecting his continued engagement with mechanical engineering, manufacturing quality, and industrial systems improvement.
In July 2026, Sam participated in the 2026 China Jiangsu Talent Innovation and Entrepreneurship Competition, Western United States Regional Qualifier, in Los Angeles. He presented an entrepreneurial project focused on advanced manufacturing and AI-enabled quality solutions.
Two Decades of Manufacturing and Quality Leadership
Before turning his focus toward AI-enabled quality engineering, Sam built a career in some of the world’s most demanding manufacturing environments. Over more than 20 years, he has held senior leadership positions, including Quality Director and Senior Quality Manager, across the automotive, consumer electronics, automation equipment, and energy storage industries.
He has led quality organizations of more than 100 professionals across China, Taiwan, and Malaysia and supported global OEM programs involving companies including Apple, Dell, HP, and Microsoft.
Sam’s engineering foundation was established at Ford Motor Company, where he earned Six Sigma Green Belt certification and received Black Belt training. The experience provided a foundation in structured problem-solving, process improvement, and data-driven quality management that would inform his subsequent leadership roles.
At AlphaESS Malaysia, Sam built the company’s Quality Management System (QMS) from the ground up and led the organization to ISO 9001:2015 certification within seven months. The certification followed a successful audit by NQA, a UKAS-accredited certification body based in the United Kingdom.
The QMS subsequently supported TÜV- and IEC-related product certification audits, which were completed with zero nonconformities. These efforts strengthened the company’s quality and compliance infrastructure while supporting its expansion into North American and European markets.
Now pursuing a Master of Business Administration (MBA) at Horizon University, Sam is combining his extensive manufacturing and quality-management experience with research in artificial intelligence. His work aims to develop practical, data-driven approaches to strengthening manufacturing quality, energy-storage reliability, and production consistency in increasingly intelligent manufacturing environments.