A Study on the Effect of Defect Shape on Defect Detection in Visual Inspection

Nakajima, Ryosuke; Yamamoto, Riho; Hida, Takuya; Matsumoto, Toshiyuki

doi:10.1016/j.promfg.2020.01.277

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Thus, the goal of tactile inspection is to detect defects such as distortion in shape or unevenness of surfaces by scanning the part using the palm of the hand, fingertips, or even fingernails to detect discontinuities with sizes on the order of a few micrometres [15]. Such minute irregularities are not always visible to the human eye, and their detectability highly depends on the individual visual acuity of each inspector and the size, contrast, colour, illumination, and shape of the defect [7,18,19]. Likewise, tactile inspection depends on individual touch perception, but also on whether gloves are worn during the inspection process and on defect-related characteristics such as size and shape, e.g., sharp versus soft edges [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Visual and Visual–Tactile Inspection of Aircraft Engine Blades

2021

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Background—In aircraft engine maintenance, the majority of parts, including engine blades, are inspected visually for any damage to ensure a safe operation. While this process is called visual inspection, there are other human senses encompassed in this process such as tactile perception. Thus, there is a need to better understand the effect of the tactile component on visual inspection performance and whether this effect is consistent for different defect types and expertise groups. Method—This study comprised three experiments, each designed to test different levels of visual and tactile abilities. In each experiment, six industry practitioners of three expertise groups inspected the same sample of N = 26 blades. A two-week interval was allowed between the experiments. Inspection performance was measured in terms of inspection accuracy, inspection time, and defect classification accuracy. Results—The results showed that unrestrained vision and the addition of tactile perception led to higher inspection accuracies of 76.9% and 84.0%, respectively, compared to screen-based inspection with 70.5% accuracy. An improvement was also noted in classification accuracy, as 39.1%, 67.5%, and 79.4% of defects were correctly classified in screen-based, full vision and visual–tactile inspection, respectively. The shortest inspection time was measured for screen-based inspection (18.134 s) followed by visual–tactile (22.140 s) and full vision (25.064 s). Dents benefited the most from the tactile sense, while the false positive rate remained unchanged across all experiments. Nicks and dents were the most difficult to detect and classify and were often confused by operators. Conclusions—Visual inspection in combination with tactile perception led to better performance in inspecting engine blades than visual inspection alone. This has implications for industrial training programmes for fault detection.

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Section: Literature Reviewmentioning

confidence: 99%

Comparison of Visual and Visual–Tactile Inspection of Aircraft Engine Blades

2021

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“…These Many examples could be found of how optical checks are used in production. To perform high-quality control only visual human inspection is not enough since it relies on the experience of the inspector [13]. Furthermore, automation cannot be reached with such checks.…”

Section: Automatic Quality Controlmentioning

confidence: 99%

Optimization Experiment of Production Processes Using a Dynamic Decision Support Method: A Solution to Complex Problems in Industrial Manufacturing for Small and Medium-Sized Enterprises

Skėrė

Žvironienė

Juzėnas

et al. 2023

Sensors

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In the industrial sector, production processes are continuously evolving, but issues and delays in production are still commonplace. Complex problems often require input from production managers or experts even though Industry 4.0 provides advanced technological solutions. Small and medium-sized enterprises (SMEs) normally rely more on expert opinion since they face difficulties implementing the newest and most advanced Industry 4.0 technologies. This reliance on human expertise can cause delays in the production processes, ultimately, impacting the efficiency and profitability of the enterprise. As SMEs are mostly niche markets and produce small batches, dynamics in production operations and the need for quick responses cannot be avoided. To address these issues, a decision support method for dynamic production planning (DSM DPP) was developed to optimize the production processes. This method involves the use of algorithms and programming in Matlab to create a decision support module that provides solutions to complex problems in real-time. The aim of this method is to combine not only technical but also human factors to efficiently optimize dynamic production planning. It is hardly noticeable in other methods the involvement of human factors such as skills of operations, speed of working, or salary size. The method itself is based on real-time data so examples of the required I 4.0 technologies for production sites are described in this article—Industrial Internet of Things, blockchains, sensors, etc. Each technology is presented with examples of usage and the requirement for it. Moreover, to confirm the effectiveness of this method, tests were made with real data that were acquired from a metal processing company in Lithuania. The method was tested with existing production orders, and found to be universal, making it adaptable to different production settings. This study presents a practical solution to complex problems in industrial settings and demonstrates the potential for DSM DPP to improve production processes while checking the latest data from production sites that are conducted through cloud systems, sensors, IoT, etc. The implementation of this method in SMEs could result in significant improvements in production efficiency, ultimately, leading to increased profitability.

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“…Thus, nondestructive techniques (NDT) are popularly applied during inspections. The following are some of the more frequently used NDT methods 35 In 2019, 36 the researchers studied the different defect shapes on a surface to be inspected and derived the relationship between defect profile and defect detection rate by employing peripheral vision (luminance). The shapes of defects, their locations, and the characteristics of 12 objects were considered as the experimental data.…”

Section: Visual Inspectionmentioning

confidence: 99%

A heuristic approach on predictive maintenance techniques: Limitations and scope

Shukla

Nefti‐Meziani

Davis

2022

Advances in Mechanical Engineering

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In view of the trend towards Industry 4.0, intelligent predictive monitoring and decision-making processes have become a crucial requirement in today’s manufacturing industries to safeguard data exchange and industrial assets from damage that would thus prevent the achievement of overall company goals. For enhanced reliability and safe operation of machines, frequent maintenance of the process equipment and the linked auxiliaries in a plant is highly desirable. Poor maintenance of assets can add to downtime, which can in turn affect the overall cost-effectiveness of the plant. With traditional maintenance strategies and planned or timed-based maintenance, one replaces the faulty systems when they are found to be damaged or broken. However, an early and proactive prediction of machine or equipment fault and failure state enables the industry to take the necessary action to replace the faulty system well before it stops operating entirely. This paper briefly reviews the available predictive maintenance techniques for different applications from the perspective of Industry 4.0. Furthermore, the associated challenges and opportunities are identified and discussed.

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A Study on the Effect of Defect Shape on Defect Detection in Visual Inspection

Cited by 4 publications

References 7 publications

Comparison of Visual and Visual–Tactile Inspection of Aircraft Engine Blades

Comparison of Visual and Visual–Tactile Inspection of Aircraft Engine Blades

Optimization Experiment of Production Processes Using a Dynamic Decision Support Method: A Solution to Complex Problems in Industrial Manufacturing for Small and Medium-Sized Enterprises

A heuristic approach on predictive maintenance techniques: Limitations and scope

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