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Data Communication Based on MQTT in a Polymer Extrusion Process

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Blockchain in manufacturing quality control: A computer simulation study.

Pooi-Mun Wong¹, Shreya R K Sinha¹, Chee-Kong Chui¹

¹Department of Mechanical Engineering, National University of Singapore, Singapore, Singapore.

|March 1, 2021

Summary

This summary is machine-generated.

This study introduces Blockchain Quality Controller (BCQC) and Quality Defect Tolerance (QDT) to improve manufacturing quality control. These innovations enhance data security, reduce false defect detections, and speed up the process for mass personalization.

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Area of Science:

Manufacturing Engineering
Computer Science
Quality Management

Background:

Current blockchain applications in manufacturing quality control face challenges with false defect detections and data transparency.
Existing systems lack robust security and efficient during-production quality monitoring.

Purpose of the Study:

To propose a novel framework, Blockchain Quality Controller (BCQC), for enhanced manufacturing quality control.
To introduce a new consensus algorithm, Quality Defect Tolerance (QDT), for blockchain-based during-production quality management.
To address limitations in data security, transparency, and defect detection accuracy.

Main Methods:

Development of the Blockchain Quality Controller (BCQC) framework integrating blockchain and Internet-of-Things (IoT).
Proposal and implementation of the Quality Defect Tolerance (QDT) consensus algorithm for real-time quality control.
Simulation analysis to evaluate the performance of BCQC and QDT.

Main Results:

BCQC framework significantly enhances data security and improves the accuracy of defect detections.
The QDT algorithm effectively consolidates multiple inspections, accelerating the overall quality control process despite increased node count.
Simulations demonstrate improved performance in identifying and managing defects during production.

Conclusions:

The BCQC framework and QDT algorithm offer a robust solution for during-production quality control in manufacturing.
These advancements are crucial for improving part quality in mass personalization manufacturing environments.
The proposed system addresses key limitations of current blockchain implementations in quality management.