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18 pages, 4318 KiB

Open AccessArticle

Intelligent Framework Design for Quality Control in Industry 4.0

by Yousaf Ali, Syed Waqar Shah, Arsalan Arif, Mehdi Tlija and Mudasir Raza Siddiqi

Appl. Sci. 2024, 14(17), 7726; https://doi.org/10.3390/app14177726 - 2 Sep 2024

Viewed by 2977

This research aims to develop an intelligent framework for quality control and fault detection in pre-production and post-production systems in Industry 4.0. In the pre-production system, the health of the manufacturing machine is monitored. In this study, we examine the gear system of induction motors used in industries. In post-production, the product is tested for quality using a machine vision system. Gears are fundamental components in countless mechanical systems, ranging from automotive transmissions to industrial machinery, where their reliable operation is vital for overall system efficiency. A faulty gear system in the induction motor directly affects the quality of the manufactured product. Vibration data, collected from the gear system of the induction motor using vibration sensors, are used to predict the motor’s health condition. The gear system is monitored for six different fault conditions. In the second part, the quality of the final product is inspected with the machine vision system. Faults on the surface of manufactured products are detected, and the product is classified as a good or bad product. The quality control system is developed with different deep learning models. Finally, the quality control framework is validated and tested with the evaluation metrics. Full article

(This article belongs to the Special Issue New Technologies in Intelligent Manufacturing and Industrial Engineering)

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12 pages, 4660 KiB

Open AccessArticle

A Real-Time Inspection System for Industrial Helical Gears

by Thomas Idzik, Matthew Veres, Cole Tarry and Medhat Moussa

Sensors 2023, 23(20), 8541; https://doi.org/10.3390/s23208541 - 18 Oct 2023

Cited by 1 | Viewed by 1867

Abstract

Manufacturing is an imperfect process that requires frequent checks and verifications to ensure products are being produced properly. In many cases, such as visual inspection, these checks can be automated to a certain degree. Incorporating advanced inspection techniques (i.e., via deep learning) into real-world inspection pipelines requires different mechanical, machine vision, and process-level considerations. In this work, we present an approach that builds upon prior work at an automotive gear facility located in Guelph, Ontario, which is looking to expand its defect detection capabilities. We outline a set of inspection-cell changes, which has led to full-gear surface scanning and inspection at a rate of every 7.5 s, and which is currently able to detect three common types of surface-level defects. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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11 pages, 3912 KiB

Open AccessArticle

Detecting Teeth Defects on Automotive Gears Using Deep Learning

by Abdelrahman Allam, Medhat Moussa, Cole Tarry and Matthew Veres

Sensors 2021, 21(24), 8480; https://doi.org/10.3390/s21248480 - 19 Dec 2021

Cited by 14 | Viewed by 5733

Abstract

Gears are a vital component in many complex mechanical systems. In automotive systems, and in particular vehicle transmissions, we rely on them to function properly on different types of challenging environments and conditions. However, when a gear is manufactured with a defect, the gear’s integrity can become compromised and lead to catastrophic failure. The current inspection process used by an automotive gear manufacturer in Guelph, Ontario, requires human operators to visually inspect all gear produced. Yet, due to the quantity of gears manufactured, the diverse array of defects that can arise, the time requirements for inspection, and the reliance on the operator’s inspection ability, the system suffers from poor scalability, and defects can be missed during inspection. In this work, we propose a machine vision system for automating the inspection process for gears with damaged teeth defects. The implemented inspection system uses a faster R-CNN network to identify the defects, and combines domain knowledge to reduce the manual inspection of non-defective gears by 66%. Full article

(This article belongs to the Section Sensing and Imaging)

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17 pages, 13846 KiB

Open AccessArticle

A Bevel Gear Quality Inspection System Based on Multi-Camera Vision Technology

by Ruiling Liu, Dexing Zhong, Hongqiang Lyu and Jiuqiang Han

Sensors 2016, 16(9), 1364; https://doi.org/10.3390/s16091364 - 25 Aug 2016

Cited by 13 | Viewed by 11295

Abstract

Surface defect detection and dimension measurement of automotive bevel gears by manual inspection are costly, inefficient, low speed and low accuracy. In order to solve these problems, a synthetic bevel gear quality inspection system based on multi-camera vision technology is developed. The system can detect surface defects and measure gear dimensions simultaneously. Three efficient algorithms named Neighborhood Average Difference (NAD), Circle Approximation Method (CAM) and Fast Rotation-Position (FRP) are proposed. The system can detect knock damage, cracks, scratches, dents, gibbosity or repeated cutting of the spline, etc. The smallest detectable defect is 0.4 mm × 0.4 mm and the precision of dimension measurement is about 40–50 μm. One inspection process takes no more than 1.3 s. Both precision and speed meet the requirements of real-time online inspection in bevel gear production. Full article

(This article belongs to the Special Issue Imaging: Sensors and Technologies)

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