The effect of visual search strategy and overlays on visual inspection of castings

Watts, Kristopher Patrick

doi:10.31274/etd-180810-1770

Cited by 5 publications

(7 citation statements)

References 43 publications

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“…A systematic search strategy such as this represents the most accurate and efficient strategy for most inspection tasks (Drury, 1990;Wang & Lin, 1993). In fact, training inspectors to use a systematic search strategy has been shown to significantly improve performance in terms of increased perceptual sensitivity for defects, fewer false alarms, and greater coverage of each item to be inspected (Watts, 2011). …”

Section: Search Componentmentioning

confidence: 99%

Visual inspection : a review of the literature.

See¹

2012

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This report provides a review of 212 documents published in the visual inspection literature from the 1950s to the present. The inspection task is defined and characterized and trends in inspection research are reviewed. The prevailing twocomponent model of inspection and various techniques to measure inspection performance are discussed. The majority of the report reviews the parameters that may impact inspection performance and describes the relevant findings from the literature. The report concludes with a re-iteration of the value of inspection research and a summary of the major recommendations for improving inspection performance.4

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Section: Search Componentmentioning

confidence: 99%

Visual inspection : a review of the literature.

See¹

2012

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“…More robust training and judgment types (raster and relative) decrease the probability of false alarms (Type I error) and increase the probability of misses (Type II error). This result is from the studies [41][42] as depicted in Tables 2 and 3 in which raster training results in more Type II errors than basic training and relative judgment results in more Type II errors than absolute judgment.…”

Section: A Resultsmentioning

confidence: 75%

“…The results of this study are found in Table 3; however, it is noted Type II error in raster training was about 16% more variable than for basic training. The subjects in this study had no prior experience with inspecting castings, which allowed for an unbiased result in the analyzing the overall effectiveness in training [40][41][42]. training.…”

Section: Influence Diagram For Visual Inspectionmentioning

confidence: 99%

A probabilistic model to estimate visual inspection error for metalcastings given different training and judgment types, environmental and human factors, and percent of defects

Stallard

MacKenzie

Peters

2018

Journal of Manufacturing Systems

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Current methods for visual inspection of cast metal surfaces are variable in both terms of repeatability and reproducibility. Because of this variation in the inspection methods, extra finishing operations are often prescribed; much of this is over processing in attempt to avoid rework or customer rejection. Additionally, defective castings may pass inspection and be delivered to the customer. Given the importance of ensuring that customers receive high-quality castings, this article analyzes and quantifies the probability of Type I and II errors, where a Type I error is a false alarm, and a Type II error misses a present defect. A probabilistic model frequently used in risk analysis, called an influence diagram, is developed to incorporate different factors impacting the chances of Type I and II errors. These factors include: training for inspectors, the type of judgment used during the inspection process, the percentage of defective castings, environmental conditions, and the inspectors' capabilities. The model is populated with inputs based on prior experimentation and the authors' expertise. The influence diagram calculates the probability of a Type I error at 0.35 and the probability of a Type II error at 0.40. These results are compared to a naïve Bayes model. A manufacturer can use this analysis to identify factors in its foundry that could reduce the probability of errors. Even under the best-case scenario, the probability of Type I error is 0.18 and the probability of Type II error is 0.30 for visual inspection. This indicates improvements to the inspection process for cast metal surfaces is required.

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“…The work atmosphere can also affect the inspector's likelihood to locate defects. In some workplaces, workers are rewarded for doing their job well while others are disciplined Table 3.4-Training effects on Type I and II errors and percent of part viewed [32] if quality is subpar. In some corporations, inspectors are required to re-inspect parts, either from his previous inspections or from another inspector.…”

Section: Environmental Factorsmentioning

confidence: 99%

“…This is an interesting observation since it appears more robust training and judgement types (raster and relative) decrease the probability of false alarms and increase the probability of misses. This is most likely a result from the high variability in Type II error from raster training used in the assessment of probabilities [32]. Comparator C9 is used in some surface roughness inspection processes.…”

Section: ) Tomentioning

confidence: 99%

Quantitative surface inspection methods for metal castings

Voelker¹

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The effect of visual search strategy and overlays on visual inspection of castings

Cited by 5 publications

References 43 publications

Visual inspection : a review of the literature.

Visual inspection : a review of the literature.

A probabilistic model to estimate visual inspection error for metalcastings given different training and judgment types, environmental and human factors, and percent of defects

Quantitative surface inspection methods for metal castings

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