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    Automated Evaluation and Rating of Product Repairability Using Artificial Intelligence-Based Approaches

    Source: Journal of Manufacturing Science and Engineering:;2023:;volume( 146 ):;issue: 002::page 20901-1
    Author:
    Liao, Hao-Yu
    ,
    Esmaeilian, Behzad
    ,
    Behdad, Sara
    DOI: 10.1115/1.4063561
    Publisher: 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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      Automated Evaluation and Rating of Product Repairability Using Artificial Intelligence-Based Approaches

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4295605
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    contributor authorLiao, Hao-Yu
    contributor authorEsmaeilian, Behzad
    contributor authorBehdad, Sara
    date accessioned2024-04-24T22:38:48Z
    date available2024-04-24T22:38:48Z
    date copyright11/1/2023 12:00:00 AM
    date issued2023
    identifier issn1087-1357
    identifier othermanu_146_2_020901.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4295605
    description abstractDespite 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.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleAutomated Evaluation and Rating of Product Repairability Using Artificial Intelligence-Based Approaches
    typeJournal Paper
    journal volume146
    journal issue2
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.4063561
    journal fristpage20901-1
    journal lastpage20901-10
    page10
    treeJournal of Manufacturing Science and Engineering:;2023:;volume( 146 ):;issue: 002
    contenttypeFulltext
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