دورية أكاديمية
X-ray transmission imaging of waste printed circuit boards for value estimation in recycling using machine learning.
| العنوان: | X-ray transmission imaging of waste printed circuit boards for value estimation in recycling using machine learning. |
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| المؤلفون: | Firsching M; Division Development Center X-Ray Technology (EZRT), Fraunhofer Institute for Integrated Circuits IIS, Fürth, Germany., Ottenweller M; Division Development Center X-Ray Technology (EZRT), Fraunhofer Institute for Integrated Circuits IIS, Fürth, Germany., Leisner J; Division Development Center X-Ray Technology (EZRT), Fraunhofer Institute for Integrated Circuits IIS, Fürth, Germany., Rüger S; Division Development Center X-Ray Technology (EZRT), Fraunhofer Institute for Integrated Circuits IIS, Fürth, Germany. |
| المصدر: | Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA [Waste Manag Res] 2024 Sep; Vol. 42 (9), pp. 759-766. Date of Electronic Publication: 2024 Jun 20. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Sage Publications Country of Publication: England NLM ID: 9881064 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1096-3669 (Electronic) NLM ISO Abbreviation: Waste Manag Res Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Original Publication: London ; New York : Academic Press, c1983- |
| مواضيع طبية MeSH: | Recycling*/methods , Electronic Waste* , Machine Learning*, X-Rays |
| مستخلص: | The growing amount of electronic waste is a global challenge: on one hand, it poses a threat to the environment as it may contain toxic or hazardous substances, on the other hand it is a valuable 'urban mine' containing metals like gold and copper. Thus, recycling of electronic waste is not only a measure to reduce environmental pollution but also economically reasonable as prices for raw materials are rising. Within electronic waste, printed circuit boards (PCBs) occupy a prominent position, as they contain most of the valuable material. One important step in the overall recycling process is the evaluation and the value estimation for further treatment of the waste PCBs (WPCBs). In this article, we introduce a method for value estimation of entire WPCBs based on component detection. The value of the WPCB is then predicted by the value of the detected components. This approach allows a flexible application to different situations. In the first step, we created a dataset and labelled the components of 104 WPCBs using different component classes. The component detection is performed on dual energy X-ray images by the deep neural object detection network 'YOLO v5'. The dataset is split into a training, validation and test subset and standard performance measures as precision, recall and F Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. |
| References: | IEEE Trans Pattern Anal Mach Intell. 2015 Sep;37(9):1904-16. (PMID: 26353135) |
| فهرسة مساهمة: | Keywords: PCB; X-ray imaging; XRT; deep neural networks; machine learning; object detection; recycling; sorting |
| تواريخ الأحداث: | Date Created: 20240621 Date Completed: 20240901 Latest Revision: 20240905 |
| رمز التحديث: | 20240905 |
| مُعرف محوري في PubMed: | PMC11370203 |
| DOI: | 10.1177/0734242X241257084 |
| PMID: | 38902936 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1096-3669 |
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| DOI: | 10.1177/0734242X241257084 |