Assessment of the Effect of Cleanliness on the Visual Inspection of Aircraft Engine Blades: An Eye Tracking Study

Background—The visual inspection of aircraft parts such as engine blades is crucial to ensure safe aircraft operation. There is a need to understand the reliability of such inspections and the factors that affect the results. In this study, the factor ‘cleanliness’ was analysed among other factors. Method—Fifty industry practitioners of three expertise levels inspected 24 images of parts with a variety of defects in clean and dirty conditions, resulting in a total of N = 1200 observations. The data were analysed statistically to evaluate the relationships between cleanliness and inspection performance. Eye tracking was applied to understand the search strategies of different levels of expertise for various part conditions. Results—The results show an inspection accuracy of 86.8% and 66.8% for clean and dirty blades, respectively. The statistical analysis showed that cleanliness and defect type influenced the inspection accuracy, while expertise was surprisingly not a significant factor. In contrast, inspection time was affected by expertise along with other factors, including cleanliness, defect type and visual acuity. Eye tracking revealed that inspectors (experts) apply a more structured and systematic search with less fixations and revisits compared to other groups. Conclusions—Cleaning prior to inspection leads to better results. Eye tracking revealed that inspectors used an underlying search strategy characterised by edge detection and differentiation between surface deposits and other types of damage, which contributed to better performance.

PoreAnalyzer—An Open-Source Framework for the Analysis and Classification of Defects in Additive Manufacturing

Additive manufacturing processes offer high geometric flexibility and allow the use of new alloy concepts due to high cooling rates. For each new material, parameter studies have to be performed to find process parameters that minimize microstructural defects such as pores or cracks. In this paper, we present a system developed in Python for accelerated image analysis of optical microscopy images. Batch processing can be used to quickly analyze large image sets with respect to pore size distribution, defect type, contribution of defect type to total porosity, and shape accuracy of printed samples. The open-source software is independent of the microscope used and is freely available for use. This framework allows us to perform such an analysis on a circular area with a diameter of 5 mm within 10 s, allowing detailed process maps to be obtained for new materials within minutes after preparation.

Research into Power Transformer Health Assessment Technology Based on Uncertainty of Information and Deep Architecture Design

The uncertainty of the evaluation information is likely to affect the accuracy of the evaluation, when conducting a health evaluation of a power transformer. A multilevel health assessment method for power transformers is proposed in view of the three aspects of indicator criterion uncertainty, weight uncertainty, and fusion uncertainty. Firstly, indicator selection is conducted through the transformer guidelines and engineering experience to establish a multilevel model of transformers that can reflect the defect type and defect location. Then, a Gaussian cloud model is used to solve the uncertainty of the indicator criterion boundary. Based on association rules, AHP, and variable weights, the processed weights are calculated from the update module to obtain comprehensive weights, which overcomes the uncertainty of the weights. Improved DSmT theory is used for multiple evidence fusion to solve the high conflict and uncertainty problems in the fusion process. Finally, through actual case analysis, the defect type, defect location, and overall state of the transformer of the device are obtained. By comparing with many defect cases in a case-study library, the evaluation accuracy rate is found to reach 96.21%, which verifies the practicability and efficiency of the method.

Behavior of Defective Cast in Place Piles

This paper deals with testing defected model piles in the soil in order to study their behavior. In this respect, the results of model pile tests are discussed either geotechnically or structurally according to the type of failure. Two parameters were studied in order to evaluate the general behavior of defective piles. These parameters include the defect location and the defect type for floating and end bearing pile. The results of the experimental work indicated that the critical case for floating pile is seen to be when the defect of (5%) at the first third of the pile length at which the decrease in the bearing capacity is about (21%), while the decrease in the bearing capacity is found to be (14%) and (10%), when the defect is at the middle and the lower third of the pile length, respectively. The decrease in the bearing capacity for floating pile is found to be (31%) and (21%) for void and neck defect, respectively, while the decrease in the bearing capacity for end bearing pile is found to be (43%) and (52%) for void and neck defect, respectively.

Syngas molecules as probes for defects in 2D hexagonal boron nitride: their adsorption and vibrations

Through insights from first-principles calculations, we predict defect-laden h-BN, particularly with N vacancy, to have good propensity toward CO and CO2 hydrogenation. Vibrational frequencies of CO and CO2 are proposed as identifiers of defect type.

Visualizing defect energetics

Stability of any point defect type and the corresponding compound can be visualized within the same convex-hull framework. Defect formation enthalpy is determined using intercepts at the compositions of the pure elements involved in defect formation.