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Relevant bibliographies by topics / Classification Scrap Management

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Academic literature on the topic 'Classification Scrap Management'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Classification Scrap Management"

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Prof., S. P. Veer Harshal Linge Aditya Zade Parikshit Urkande Mrunali Nimbulkar Nutan Sayankar Payal Khedekar. "Machine Learning Techniques in Scrap Management System." International Journal of Advanced Innovative Technology in Engineering 10, no. 2 (2025): 100–107. https://doi.org/10.5281/zenodo.15401161.

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The application of machine learning methods on scrap management systems is discussed in this literature review. ML methods have been used in recycling process optimisation, predictive maintenance, and sorting scrap.  This research looks at several machines learning methodologies, including computer vision, clustering, and classification.  It also discusses practical applications and challenges in implementing machine learning in this domain. The results prove that machine learning is able to enhance sorting accuracy, reduce operational costs, and enable more eco-friendly recycling pr

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Li, Yifeng, Xunpeng Qin, Zhenyuan Zhang, and Huanyu Dong. "A robust identification method for nonferrous metal scraps based on deep learning and superpixel optimization." Waste Management & Research: The Journal for a Sustainable Circular Economy 39, no. 4 (2021): 573–83. http://dx.doi.org/10.1177/0734242x20987884.

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End-of-life vehicles (ELVs) provide a particularly potent source of supply for metals. Hence, the recycling and sorting techniques for ferrous and nonferrous metal scraps from ELVs significantly increase metal resource utilization. However, different kinds of nonferrous metal scraps, such as aluminium (Al) and copper (Cu), are not further automatically classified due to the lack of proper techniques. The purpose of this study is to propose an identification method for different nonferrous metal scraps, facilitate the further separation of nonferrous metal scraps, achieve better management of r

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Ipin, Saiful Arifin, Tukino, and Elfina Novalia. "The Use of Support Vector Machine in Classifying Scrap Metal Goods Based on Physical Characteristics." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 3 (2025): 2283–88. https://doi.org/10.59934/jaiea.v4i3.1154.

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The problem of metal waste management, especially scrap metal, is increasingly complex with the increase in industrial and construction activities that produce various types of waste materials. Scrap metal is one type of waste that still has high economic value if it can be sorted and classified appropriately based on its type and quality. However, manual classification methods are still predominantly used, subjectivity and human error. To overcome these challenges, this study proposes an artificial intelligence-based approach by implementing Support Vector Machine (SVM) as an automatic classi

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Bernat, Łukasz, Tomasz Jurtsch, Grzegorz Moryson, Jan Moryson, and Grzegorz Wiczyński. "Effect of Surface Condition on the Results of Chemical Composition Measurements of Scrap Copper Alloys." Recycling 9, no. 1 (2024): 14. http://dx.doi.org/10.3390/recycling9010014.

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The processing of copper-bearing scrap makes it possible to reduce the costs and energy consumption of obtaining copper alloy products compared to producing them from primary raw materials. To achieve this, it is necessary to quickly and accurately determine the content of alloying elements in individual scrap elements. However, the copper-bearing scrap obtained at secondary raw materials collection points consists of elements with various surface conditions (due to contamination, shape, paint coatings, roughness, etc.). The paper contains research results on the influence of surface roughness

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Alexopoulos, Kosmas, Paolo Catti, Giannis Kanellopoulos, et al. "Deep Learning for Estimating the Fill-Level of Industrial Waste Containers of Metal Scrap: A Case Study of a Copper Tube Plant." Applied Sciences 13, no. 4 (2023): 2575. http://dx.doi.org/10.3390/app13042575.

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Advanced digital solutions are increasingly introduced into manufacturing systems to make them more intelligent. Intelligent Waste Management Systems in industries allow for data collection and analysis to make better-informed decisions, monitor and manage processes remotely, and improve waste management. In many industries, scrap is collected in large waste containers located on the factory floor, usually close to its source. In most cases, monitoring of waste containers’ fill levels is either manually performed by visual inspection by the operators working in close proximity or by employing

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Díaz-Romero, Dillam Jossue, Simon Van den Eynde, Wouter Sterkens, et al. "Simultaneous mass estimation and class classification of scrap metals using deep learning." Resources, Conservation and Recycling 181 (June 2022): 106272. http://dx.doi.org/10.1016/j.resconrec.2022.106272.

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Hagen, Markus, Josef Quan Ngyuen, David Hui Shyan Lau, Eran Nave, and Jens Tübke. "Automated Data Gathering, Classification, Standardization and Evaluation Connected with AI in Smart Li-ion Battery Cell Production." ECS Meeting Abstracts MA2022-01, no. 2 (2022): 450. http://dx.doi.org/10.1149/ma2022-012450mtgabs.

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The production of lithium-ion batteries is characterized by high scrap rates. With material costs of 70-80% of the cell price, these are a significant cost factor. By using AI, correlations of process data and cell properties can be determined and particularly quality-relevant process steps can be identified. However, the prerequisite for the use of AI is a clean collection, description and allocation of data which is very time-consuming. In the BMBF-funded projects “KontElPro”, for solvent-free electrode production and “AgiloBat2”, for the agile production of format-flexible cells, software i

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Choi, Jung-Wan, and Kwang-Sup Shin. "A Study on the Risk Identification and Management of Global Supply Chain Risks : Focusing on the Domestic Steel Industries." Korean Logistics Research Association 35, no. 1 (2025): 71–84. https://doi.org/10.17825/klr.2025.35.1.71.

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Recently, as uncertainty and vulnerability in the global supply chain have increased, it is necessary to identify risks and establish countermeasures considering the structure of the global supply chain. In particular, it is important to apply a risk management system and establish a response strategy for the steel industries among the various industries exposed to changes in the supply and demand of raw materials and the market environment. This study divided the domestic steel industry into four stages from the SCM perspective: procurement management, production management, inventory managem

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Díaz-Romero, Dillam, Simon Van den Eynde, Isiah Zaplana, et al. "Classification of aluminum scrap by laser induced breakdown spectroscopy (LIBS) and RGB + D image fusion using deep learning approaches." Resources, Conservation and Recycling 190 (March 2023): 106865. http://dx.doi.org/10.1016/j.resconrec.2023.106865.

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Pandey, Aavash Raj, Mahdi Seify, Udoka Okonta, and Amin Hosseinian-Far. "Advanced Sentiment Analysis for Managing and Improving Patient Experience: Application for General Practitioner (GP) Classification in Northamptonshire." International Journal of Environmental Research and Public Health 20, no. 12 (2023): 6119. http://dx.doi.org/10.3390/ijerph20126119.

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This paper presents a novel analytical approach for improving patients’ experience in healthcare settings. The analytical tool uses a classifier and a recommend management approach to facilitate decision making in a timely manner. The designed methodology comprises of 4 key stages, which include developing a bot to scrap web data while performing sentiment analysis and extracting keywords from National Health Service (NHS) rate and review webpages, building a classifier with Waikato Environment for Knowledge Analysis (WEKA), analyzing speech with Python, and using Microsoft Excel for analysis.

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Conference papers on the topic "Classification Scrap Management"

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Bacchetti, L., and L. Argiolas. "Q-SYM² and Q-ASC: ABS Scrap Yard Management, Assisted Scrap Classification and Digital Integration Into the Steel Process." In AISTech 2020. AIST, 2020. http://dx.doi.org/10.33313/380/059.

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Kempken, Jens. "IMPROVEMENT OF SCRAP MANAGEMENT IN EAF AND BOR BY CLASSIFICATION USING COMPUTER VISION ALGORITHMS." In 52º Seminário de Aciaria, Fundição e Metalurgia de Não-Ferrosos. Editora Blucher, 2023. http://dx.doi.org/10.5151/2594-5300-40234.

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Lopez Martinez, Luis. "Fatigue Life Extension of Offshore Structures by Ultrasonic Peening." In ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2011. http://dx.doi.org/10.1115/omae2011-49935.

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The service life of offshore installations is limited by its structural integrity. Furthermore the structural integrity is mainly governed by the fatigue resistance of critical welded details. In a FPSO installation these details are among others pallet stools weld joints to deck structure and bulkheads/web frames weld connections to longitudinal in ballast tanks. ultrasonic peening can improve the fatigue resistance of welded joints. Fatigue test results shows an increase of four times for high stress ranges and up to ten times for high cycle fatigue. For specimens which have already consumed

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