The Role of Visual Inspection in the 21<sup>st</sup> Century

See, Judi E.; Drury, Colin G.; Speed, Ann; Williams, Allison; Khalandi, Negar

doi:10.1177/1541931213601548

Cited by 52 publications

(30 citation statements)

References 25 publications

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“…It has been acknowledged that errors cannot be entirely eliminated because they are considered to be a normal consequence of human variability [41]. Thus, during manual assembly, inspection is required to verify that a product is free of defects before it is transferred to the next level of assembly or shipped to the customer [42]. In this way, inspection allows the system to recover from human error.…”

Section: Literature Review 21 Some Considerations About Manual Assementioning

confidence: 99%

“…For this reason, automated visual inspection (AVI) has long been used in the manufacturing industry for quality control and monitoring [45,46]. As gains in computational power yield enhanced image acquisition, processing and analysis capabilities, automation replaces human visual inspection more and more often [42,47]. For example, a robot can take pictures of the final product to detect deviations and nonconformities.…”

Section: Literature Review 21 Some Considerations About Manual Assementioning

confidence: 99%

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Classification and Quantification of Human Error in Manufacturing: A Case Study in Complex Manual Assembly

2021

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Manual assembly operations are sensitive to human errors that can diminish the quality of final products. The paper shows an application of human reliability analysis in a realistic manufacturing context to identify where and why manual assembly errors occur. The techniques SHERPA and HEART were used to perform the analysis of human reliability. Three critical tasks were selected for analysis based on quality records: (1) installation of three types of brackets using fasteners, (2) fixation of a data cable to the assembly structure using cushioned loop clamps and (3) installation of cap covers to protect inlets. The identified error modes with SHERPA were: 36 action errors, nine selection errors, eight information retrieval errors and six checking errors. According to HEART, the highest human error probabilities were associated with assembly parts sensitive to geometry-related errors (brackets and cushioned loop clamps). The study showed that perceptually engaging assembly instructions seem to offer the highest potential for error reduction and performance improvement. Other identified areas of action were the improvement of the inspection process and workers’ provision with better tracking and better feedback. Implementation of assembly guidance systems could potentially benefit worker’s performance and decrease assembly errors.

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Section: Literature Review 21 Some Considerations About Manual Assementioning

confidence: 99%

Section: Literature Review 21 Some Considerations About Manual Assementioning

confidence: 99%

Classification and Quantification of Human Error in Manufacturing: A Case Study in Complex Manual Assembly

2021

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“…The manual process of VI involves five principal steps: Set up, Present, Search, Decide and Respond (Drury and Watson, 2002;Drury and Dempsey, 2012). Of these five steps, the 'search' and 'decide' activities appear to have received the most interest in previous investigations, probably because they are most cognitively complex and error-prone (Rao, et al, 2006;Drury and Dempsey, 2012;See et al, 2017). The 'search' step of VI has been found particularly time-consuming and prone to error, particularly in relation to omissions where a defect is missed rather than 'commissive errors' or false alarms (See, 2012).…”

Section: The Process Of Visual Inspectionmentioning

confidence: 99%

How and why we need to capture tacit knowledge in manufacturing: Case studies of visual inspection

et al. 2019

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Human visual inspection skills remain superior for ensuring product quality and conformance to standards in the manufacturing industry. However, at present these skills cannot be formally shared with other workers or used to develop and implement new solutions or assistive technologies because they involve a high level of tacit knowledge which only exists in skilled operators' internal cognitions. Industry needs reliable methods for the capture and analysis of this tacit knowledge so that it can be shared and not lost but also so that it can be best utilised in the transfer of manual work to automated systems and introduction of new technologies and processes. This paper describes two UK manufacturing case studies that applied systematic task analysis methods to capture and scrutinise the tacit knowledge and skills being applied in the visual inspection of aerospace components. Results reveal that the method was effective in eliciting tacit knowledge, and showed that tacit skills are particularly needed when visual inspection standards lack specification or the task requires greater subjective interpretation. The implications of these findings for future research and for developments in the manufacturing industry are discussed.

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“…[3][4][5][6] In parallel efforts, significant focus has been on exploring innovative solutions for automating visual inspection via autonomous drones, mobile robots, mounted cameras, and so on. [7][8][9][10] While human factors have been identified to lie at the heart of most of the limitations of visual inspection, [11][12][13] human beings possess distinct capabilities like flexibility, imagination, creativity, and so on, that prove indispensable especially during the inspection of safety-critical components. As such, the key to alleviating the major limitations of visual inspection lies in supplementing human capabilities in a systematic manner (through an appropriate level of automation).…”

Section: Introductionmentioning

confidence: 99%

Augmented reality for enhanced visual inspection through knowledge-based deep learning

Wang

Zargar

Fang

2020

Structural Health Monitoring

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A two-stage knowledge-based deep learning algorithm is presented for enabling automated damage detection in real-time using the augmented reality smart glasses. The first stage of the algorithm entails the identification of damage prone zones within the region of interest. This requires domain knowledge about the damage as well as the structure being inspected. In the second stage, automated damage detection is performed independently within each of the identified zones starting with the one that is the most damage prone. For real-time visual inspection enhancement using the augmented reality smart glasses, this two-stage approach not only ensures computational feasibility and efficiency but also significantly improves the probability of detection when dealing with structures with complex geometric features. A pilot study is conducted using hands-free Epson BT-300 smart glasses during which two distinct tasks are performed: First, using a single deep learning model deployed on the augmented reality smart glasses, automatic detection and classification of corrosion/fatigue, which is the most common cause of failure in high-strength materials, is performed. Then, in order to highlight the efficacy of the proposed two-stage approach, the more challenging task of defect detection in a multi-joint bolted region is addressed. The pilot study is conducted without any artificial control of external conditions like acquisition angles, lighting, and so on. While automating the visual inspection process is not a new concept for large-scale structures, in most cases, assessment of the collected data is performed offline. The algorithms/techniques used therein cannot be implemented directly on computationally limited devices such as the hands-free augmented reality glasses which could then be used by inspectors in the field for real-time assistance. The proposed approach serves to overcome this bottleneck.

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The Role of Visual Inspection in the 21^st Century

Cited by 52 publications

References 25 publications

Classification and Quantification of Human Error in Manufacturing: A Case Study in Complex Manual Assembly

Classification and Quantification of Human Error in Manufacturing: A Case Study in Complex Manual Assembly

How and why we need to capture tacit knowledge in manufacturing: Case studies of visual inspection

Augmented reality for enhanced visual inspection through knowledge-based deep learning

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The Role of Visual Inspection in the 21st Century

Cited by 52 publications

References 25 publications

Classification and Quantification of Human Error in Manufacturing: A Case Study in Complex Manual Assembly

Classification and Quantification of Human Error in Manufacturing: A Case Study in Complex Manual Assembly

How and why we need to capture tacit knowledge in manufacturing: Case studies of visual inspection

Augmented reality for enhanced visual inspection through knowledge-based deep learning

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Product

Resources

About

The Role of Visual Inspection in the 21^st Century