Integrity Assessment of an Aircraft Cylinder Assembly With a Crack

Vučetić, Nikola; Jovičić, Gordana; Antunović, Ranko; Sovilj-Nikic, Sandra; Košarac, Aleksandar; Jeremić, Dejan

doi:10.17222/mit.2022.430

Cited by 2 publications

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“…Several other researchers have undertaken simulation studies to estimate the extent of damage. Through these simulations, they were able to draw conclusions indicating that areas of damage and high stress levels exhibited remarkable similarity in their findings (Zhao et al, 2021;Hou et al, 2022;Vučetić et al, 2022;Suresh Kumar and Kumaraswamidhas, 2021).…”

Section: Introductionmentioning

confidence: 91%

Comparison of finite element analysis results with strain gauge measurements of a front axle housing

Gür,

Cen

2024

Mech. Sci.

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Abstract. The strength of the front axle of tractors used on rough terrain is crucial in countries in which agriculture is widespread. Rough agricultural fields, rugged village roads, and ground irregularities cause unexpected reaction forces on the axles. Thus, it is important to analyze the front axle of a tractor with respect to stress, which eventually leads to cracks and premature failure. In this study, ANSYS 13.0 finite element analysis (FEA) software (ANSYS, 2010) was used to predict the strength of a design under loading conditions. ANSYS 13.0 allows products to be tested in a virtual environment and helps to prevent problems that may arise later and accordingly improve them. This study aims to investigate the stresses that occur on the housing of the front axle of a tractor. The reaction forces acting on the front axle housing can cause cracks near the middle of the housing. The study applies a static load of 30 000 N to both hubs at the end of the front axle housing and uses the FEA method to predict and evaluate the maximum stress areas on the housing. Strain gauges are bonded to these locations to measure the real-life stresses on the axle housing in these areas. The results of the FEA and strain gauge measurements were compared, and a correlation was found with 98 % accuracy.

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Section: Introductionmentioning

confidence: 91%

Comparison of finite element analysis results with strain gauge measurements of a front axle housing

Gür,

Cen

2024

Mech. Sci.

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“…If the assembly defects are severe, not only will the product not be able to function correctly, but it may even cause loss of life or property for the user. Therefore, the assembly quality of the product needs to be strictly controlled [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Deep Learning for Defect Inspection in Hand Tool Assembly

Lin,

Jheng,

Lin

et al. 2024

Sensors

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The integrity of product assembly in the precision assembly industry significantly influences the quality of the final products. During the assembly process, products may acquire assembly defects due to personnel oversight. A severe assembly defect could impair the product’s normal function and potentially cause loss of life or property for the user. For workpiece defect inspection, there is limited discussion on the simultaneous detection of the primary kinds of assembly anomaly (missing parts, misplaced parts, foreign objects, and extra parts). However, these assembly anomalies account for most customer complaints in the traditional hand tool industry. This is because no equipment can comprehensively inspect major assembly defects, and inspections rely solely on professionals using simple tools and their own experience. Thus, this study proposes an automated visual inspection system to achieve defect inspection in hand tool assembly. This study samples the work-in-process from three assembly stations in the ratchet wrench assembly process; an investigation of 28 common assembly defect types is presented, covering the 4 kinds of assembly anomaly in the assembly operation; also, this study captures sample images of various assembly defects for the experiments. First, the captured images are filtered to eliminate surface reflection noise from the workpiece; then, a circular mask is given at the assembly position to extract the ROI area; next, the filtered ROI images are used to create a defect-type label set using manual annotation; after this, the R-CNN series network models are applied to object feature extraction and classification; finally, they are compared with other object detection models to identify which inspection model has the better performance. The experimental results show that, if each station uses the best model for defect inspection, it can effectively detect and classify defects. The average defect detection rate (1-β) of each station is 92.64%, the average misjudgment rate (α) is 6.68%, and the average correct classification rate (CR) is 88.03%.

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Integrity Assessment of an Aircraft Cylinder Assembly With a Crack

Cited by 2 publications

References 0 publications

Comparison of finite element analysis results with strain gauge measurements of a front axle housing

Comparison of finite element analysis results with strain gauge measurements of a front axle housing

Utilizing Deep Learning for Defect Inspection in Hand Tool Assembly

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