An Automatic Detection and Online Quality Inspection Method for Workpiece Surface Cracks based on Machine Vision

Abstract

The wide application of intelligent manufacturing technologies imposes higher requirements for the quality inspection of industrial products; however, the existing industrial product quality inspection methods generally have a few shortcomings such as requiring many inspectors, too complicated methods, difficulty in realizing standardized monitoring, and the low inspection efficiency, etc. Targeting at these problems, this paper proposed an automatic detection and online quality inspection method for workpiece surface cracks based on the machine vision technology. At first, it proposed a vision-field environment calibration method, gave the specific method for workpiece shape feature recognition and size measurement based on machine vision, and achieved the on-line monitoring of workpiece quality problems such as feature defects and size deviations. Then, this study integrated the multi-scale attention module and the up-sampling module that can restore the locations of image pixels based on the high-level and low-level hybrid feature maps, built a workpiece crack extraction network, and realized workpiece crack feature extraction, crack type classification, and damage degree division. At last, experimental results verified the effectiveness of the proposed method, and this paper provided a reference for the application of machine vision technology in other fields.

A production interface to enable legacy factories for industry 4.0

“…The utilization of computer vision (CV) technologies in manufacturing systems is widespread and serves various purposes, such as enhancing quality control, automating manufacturing tasks, and enabling closed-loop control systems, among others [20,21]. For instance, CV algorithms can be used to detect defects in products and components, such as surface cracks [22], weld defects [23], and misalignments [24]. Monitoring and analyzing sensor data from the production line can enhance quality control and defect prevention [25].…”

“…IoT Real-time monitoring and control, optimization of production schedules [12] Integration challenges with existing manufacturing systems, compatibility issues with legacy equipment and software [15] Predictive maintenance, reducing downtime and saving costs [13] Limited computational resources [15], cybersecurity risks [16] Logistics management, improving productivity and delivery efficiency [14] CV Quality control, defect detection in products and components [22][23][24] Complex deployment, infrastructure requirements [20] Automation of manufacturing tasks, improving efficiency and accuracy [26] Calibration and accuracy validation requirements, limitations in handling complex variations or low-contrast features Human activity observation, facilitating human-machine interaction, ergonomics assessment [27,29] Camera positioning and angle requirements for accurate observation Integration with emerging technologies, enhancing functionality [31][32][33] Additional hardware and software integration requirements, limitations in real-time synchronization and data transfer Machine learning integration, improving accuracy and quality [34,35] Requirements for sufficient training data and computational resources…”