Fatigue crack growth characterization of re-treaded tire rubber

Gudsoorkar, Uday; Bindu, Rupa

doi:10.1016/j.matpr.2021.01.065

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Depending on factors of interest, such as the material and hardness of the subject, various types of images, such as CT images or X-ray radiographic images, can be used to analyze the subject. Various studies, such as medicine, agriculture, and manufacturing, are actively being conducted [25][26][27][28][29][30][31]. These images have the advantage of seeing changes in parameters of interest (e.g., materials constituting the object, hardness, and height) from a photograph of the object at a glance [32].…”

Section: Depth Imagementioning

confidence: 99%

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

Kang

Kim

et al. 2021

Applied Sciences

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This paper introduces and implements an efficient training method for deep learning–based anomaly area detection in the depth image of a tire. A depth image of 16 bit integer size is used in various fields, such as manufacturing, industry, and medicine. In addition, the advent of the 4th Industrial Revolution and the development of deep learning require deep learning–based problem solving in various fields. Accordingly, various research efforts use deep learning technology to detect errors, such as product defects and diseases, in depth images. However, a depth image expressed in grayscale has limited information, compared with a three-channel image with potential colors, shapes, and brightness. In addition, in the case of tires, despite the same defect, they often have different sizes and shapes, making it difficult to train deep learning. Therefore, in this paper, the four-step process of (1) image input, (2) highlight image generation, (3) image stacking, and (4) image training is applied to a deep learning segmentation model that can detect atypical defect data. Defect detection aims to detect vent spews that occur during tire manufacturing. We compare the training results of applying the process proposed in this paper and the general training result for experiment and evaluation. For evaluation, we use intersection of union (IoU), which compares the pixel area where the actual error is located in the depth image and the pixel area of the error inferred by the deep learning network. The results of the experiment confirmed that the proposed methodology improved the mean IoU by more than 7% and the IoU for the vent spew error by more than 10%, compared to the general method. In addition, the time it takes for the mean IoU to remain stable at 60% is reduced by 80%. The experiments and results prove that the methodology proposed in this paper can train efficiently without losing the information of the original depth data.

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Section: Depth Imagementioning

confidence: 99%

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

Kang

Kim

et al. 2021

Applied Sciences

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Tyre remanufacturing: A brief review

Gaidhane

Ullah

Khalatkar

2022

Materials Today: Proceedings

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Diagnostics in Tire Retreading Based on Classification with Fuzzy Inference System

et al. 2022

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Currently, there are tire retreading companies whose evaluations are not wholly accurate; due to various factors, sometimes customers are forced to agree to what is decided, and this means that the customer can sometimes pay for services that do not necessarily guarantee the correct operation of the tire or, failing that, shorten its life. This work aims to develop a tire diagnostic system that allows for evaluating a tire’s faults and can thus be more precise when determining if it needs retreading or a change process. The diagnostic system is focused on demonstrating that fuzzy logic can be applied in diagnosing the condition of tires. The methodology consisted of determining the variables to be considered in the evaluation of tires, such as blowing out, flange breakage, band failure, and patching, then applying fuzzy logic. Subsequently, the execution tests of the built diagnostic software were carried out for its validation in a case study of a tire retreading company. The result was a margin of error of 1.6% accuracy versus 5.6% from the operator experience. The conclusion was that fuzzy logic could be applied correctly in the field of tire retreading, providing substantial savings in time and resources for related companies, as well as giving customers confidence since, by using more accurate results, the diagnostic system will make the tire evaluation efficient.

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Fatigue crack growth characterization of re-treaded tire rubber

Cited by 3 publications

References 18 publications

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

Tyre remanufacturing: A brief review

Diagnostics in Tire Retreading Based on Classification with Fuzzy Inference System

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