Manual, Hybrid and Automated Inspection Literature and Current Research

Comparative Analysis of Human Operators and Advanced Technologies in the Visual Inspection of Aero Engine Blades

Background—Aircraft inspection is crucial for safe flight operations and is predominantly performed by human operators, who are unreliable, inconsistent, subjective, and prone to err. Thus, advanced technologies offer the potential to overcome those limitations and improve inspection quality. Method—This paper compares the performance of human operators with image processing, artificial intelligence software and 3D scanning for different types of inspection. The results were statistically analysed in terms of inspection accuracy, consistency and time. Additionally, other factors relevant to operations were assessed using a SWOT and weighted factor analysis. Results—The results show that operators’ performance in screen-based inspection tasks was superior to inspection software due to their strong cognitive abilities, decision-making capabilities, versatility and adaptability to changing conditions. In part-based inspection however, 3D scanning outperformed the operator while being significantly slower. Overall, the strength of technological systems lies in their consistency, availability and unbiasedness. Conclusions—The performance of inspection software should improve to be reliably used in blade inspection. While 3D scanning showed the best results, it is not always technically feasible (e.g., in a borescope inspection) nor economically viable. This work provides a list of evaluation criteria beyond solely inspection performance that could be considered when comparing different inspection systems.

Photometric Stereo-Based Defect Detection System for Steel Components Manufacturing Using a Deep Segmentation Network

This paper presents an automatic system for the quality control of metallic components using a photometric stereo-based sensor and a customized semantic segmentation network. This system is designed based on interoperable modules, and allows capturing the knowledge of the operators to apply it later in automatic defect detection. A salient contribution is the compact representation of the surface information achieved by combining photometric stereo images into a RGB image that is fed to a convolutional segmentation network trained for surface defect detection. We demonstrate the advantage of this compact surface imaging representation over the use of each photometric imaging source of information in isolation. An empirical analysis of the performance of the segmentation network on imaging samples of materials with diverse surface reflectance properties is carried out, achieving Dice performance index values above 0.83 in all cases. The results support the potential of photometric stereo in conjunction with our semantic segmentation network.

Detecting cognitive hacking in visual inspection with physiological measurements

Cyber threats are targeting vulnerabilities of human workers performing tasks in manufacturing processes, including visual inspection to bias their decision-making, thereby sabotaging product quality. This article examines the use of priming as a form of "cognitive hacking" to adversely affect quality inspection decisions in manufacturing, and investigates physiological measurements as means to detect such intrusion. In a within-subject design experiment, twenty participants inspected surface roughness of a manufactured component with and without exposure to priming on the display of an inspection logging system. The results show that the presence of primes impacted accuracy on surface roughness, cortical activities at parietal lobe P4, and eye gaze for inspecting components. The experiment provides supporting evidence that basic hacking of a worker display can be an effective method to alter decision making in inspection. The findings also illustrate that cortical activities and eye gaze can be useful indicators of cognitive hacking. A major implication of the study results is that physiological indicators can be effective at revealing unconscious cognitive influence in visual inspection.