An Improved Intelligent Cloud-based Structure for Automated Product Quality Control - 论文摘要

Sep 13, 2026

Regardless of product type, quality is the central concern for manufacturers, and quality management relies on procedures and instruments to improve factory-wide performance. With today’s fast-changing business environment and intense market competition, continuous improvement and quality tools are critical for long-term success. This study investigates automating quality assurance using cloud computing, leveraging cloud agility, power, and cost advantages in uncertain manufacturing conditions. Using machine learning and cloud services, it provides a visual quality assurance proposal and evaluates defect detection accuracy and response time.

LanguageEnglish

CategoryPresentations

FormatPDF

Pages9

Reading Time3 min

AccessFree to download

I. INTRODUCTION

Manufacturing quality assurance and its importance

Challenges in production control

II. QUALITY ASSURANCE

Definition and role of QA

Benefits of Quality Assurance

Cost savings and efficiency

An Improved Intelligent Cloud-based Structure for Automated Product Quality Control Vineeta Shrivastava 1 , Vaibhav Udgir2 1Lnct University, BHOPAL 2Think Future Technologies, BHOPAL Abstract: Regardless of the products, quality is the most crucial concern for all manufacturers in any manufacturing system. Quality management is a collection of procedures and instruments that can improve the overall system performance of a manufacturing facility. The dynamic nature of today's business climate and intense rivalry in the market have made quality tools and continuous improvement (CI) essential for long-term success and overcoming obstacles. The methodology of automating quality assurance with cloud computing is also explored in this study. Given how popular cloud computing has become lately. An increasing number of manufacturers are thinking about switching to a cloud-based quality system for their quality management. Because of this, the cloud's power, agility, and cost advantages are now essential for surviving in today's dynamic and uncertain manufacturing environment. Thus, utilizing machine learning methods and cloud computing, this work has provided a service-based proposal system for visual quality assurance. The model's accuracy in detecting defects in manufactured parts and response time are assessed. The model's average accuracy was estimated to be approximately 93%, while the average reaction delay was estimated to be approximately 8 seconds. Keywords: Manufacturing, Quality Assurance, Customer Satisfaction, Visual Inspection, Cloud Computing I. INTRODUCTION In the context of manufacturing, quality assurance, or QA, refers to the methods that manufacturers use as part of a quality management system to maintain consistently, expected quality levels on the things they create. Quality assurance, which is frequently associated with quality control, or QC, is a component of the ecosystem that guarantees customers receive quality products free of unanticipated flaws. Quality assurance is crucial in any manufacturing environment, but it is especially important for types of manufacturers, such as those in the automobile sector and high-precision parts suppliers, because failure to maintain a high standard of quality might result in harm or death [1] . A combination of increasing customer expectations and technical improvements has increased the complexity of industrial systems. Businesses also encounter stochastic disruptions and cyclical demand, which results in an unequal utilization of industrial capacity. Effectively and efficiently planning and managing production has thus become a significant competitive advantage [2][3] . Production control deals with recurring tasks in the production process, such as order release or tool allocation, as well as short-term rescheduling because of unanticipated machine failures or departures from the schedule [4][5] . However when it relates to single-part and small-series production, the division of labor planning and production control is overly rigid and unsuccessful for the following reasons: 􀁸 Formulation of unworkable work plans without accounting for the current volume of income (e.g., machine utilization, tool condition) 􀁸 The decision-making space for production control is constrained due to the small number of process choices. 􀁸 The route possibilities were not systematically evaluated due to the manual rescheduling by the production planner. 􀁸 Put a focus on individual optimization criteria in production planning and control. II. QUALITY ASSURANCE Quality assurance, also known as QA, is the term used to describe the processes manufacturers use as part ofa quality management system to maintain consistently, expected quality levels on the items they produce. A part of the ecosystem that ensures customers receive high-quality products free of unintended faults is quality assurance, which is commonly used in conjunction with quality control, or QC. Man

FAQs

What does quality assurance (QA) mean in a manufacturing environment?

QA refers to the methods manufacturers use within a quality management system to maintain expected quality levels for the items they produce.

Why is quality assurance especially important for high-precision manufacturers and automotive parts?

Because failing to maintain a high standard of quality could result in serious harm or even death.

How does the proposed approach automate visual quality assurance?

It uses machine learning together with cloud computing in a service-based proposal system for visual quality assurance, then measures defect detection accuracy and response time.

Sep 13, 2026

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