Automated Evaluation and Rating of Product Repairability Using Artificial Intelligence-Based ApproachesSource: Journal of Manufacturing Science and Engineering:;2023:;volume( 146 ):;issue: 002::page 20901-1DOI: 10.1115/1.4063561Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Despite the importance of product repairability, current methods for assessing and grading repairability are limited, which hampers the efforts of designers, remanufacturers, original equipment manufacturers (OEMs), and repair shops. To improve the efficiency of assessing product repairability, this study introduces two artificial intelligence (AI) based approaches. The first approach is a supervised learning framework that utilizes object detection on product teardown images to measure repairability. Transfer learning is employed with machine learning architectures such as ConvNeXt, GoogLeNet, ResNet50, and VGG16 to evaluate repairability scores. The second approach is an unsupervised learning framework that combines feature extraction and cluster learning to identify product design features and group devices with similar designs. It utilizes an oriented FAST and rotated BRIEF feature extractor (ORB) along with k-means clustering to extract features from teardown images and categorize products with similar designs. To demonstrate the application of these assessment approaches, smartphones are used as a case study. The results highlight the potential of artificial intelligence in developing an automated system for assessing and rating product repairability.
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contributor author | Liao, Hao-Yu | |
contributor author | Esmaeilian, Behzad | |
contributor author | Behdad, Sara | |
date accessioned | 2024-04-24T22:38:48Z | |
date available | 2024-04-24T22:38:48Z | |
date copyright | 11/1/2023 12:00:00 AM | |
date issued | 2023 | |
identifier issn | 1087-1357 | |
identifier other | manu_146_2_020901.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4295605 | |
description abstract | Despite the importance of product repairability, current methods for assessing and grading repairability are limited, which hampers the efforts of designers, remanufacturers, original equipment manufacturers (OEMs), and repair shops. To improve the efficiency of assessing product repairability, this study introduces two artificial intelligence (AI) based approaches. The first approach is a supervised learning framework that utilizes object detection on product teardown images to measure repairability. Transfer learning is employed with machine learning architectures such as ConvNeXt, GoogLeNet, ResNet50, and VGG16 to evaluate repairability scores. The second approach is an unsupervised learning framework that combines feature extraction and cluster learning to identify product design features and group devices with similar designs. It utilizes an oriented FAST and rotated BRIEF feature extractor (ORB) along with k-means clustering to extract features from teardown images and categorize products with similar designs. To demonstrate the application of these assessment approaches, smartphones are used as a case study. The results highlight the potential of artificial intelligence in developing an automated system for assessing and rating product repairability. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Automated Evaluation and Rating of Product Repairability Using Artificial Intelligence-Based Approaches | |
type | Journal Paper | |
journal volume | 146 | |
journal issue | 2 | |
journal title | Journal of Manufacturing Science and Engineering | |
identifier doi | 10.1115/1.4063561 | |
journal fristpage | 20901-1 | |
journal lastpage | 20901-10 | |
page | 10 | |
tree | Journal of Manufacturing Science and Engineering:;2023:;volume( 146 ):;issue: 002 | |
contenttype | Fulltext |