Vision-based automated waste audits: a use case from the window manufacturing industry

Martinez Rodriguez, Pablo, Mohsen, Osama, Al-Hussein, Mohamed and Ahmad, Rafiq (2022) Vision-based automated waste audits: a use case from the window manufacturing industry. International Journal of Advanced Manufacturing Technology, 119 (11-12). pp. 7735-7749. ISSN 0268-3768

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Official URL: https://doi.org/10.1007/s00170-022-08730-2

Abstract

Waste auditing is one of the tools used to quantify waste generation in construction processes, especially in industrialized building construction facilities that aim to reduce waste. These audits are organized following a regular schedule to monitor manufacturing activities with respect to the waste generated. However, the identification and quantification of waste through occasional audits of activities at any particular workstation remains a biased, manual, error-prone, and monotonous task. This paper proposes the automation of waste auditing in industrialized construction facilities, using as a case study a cutting station on a window manufacturing line. The waste generated during the cutting process is quantified using contour-based image processing algorithms, and the identification of the material is determined by optimized deep learning classification models. This approach allows the continuous acquisition of waste generation data at the workstation level and enables data-driven waste management decision-making that has the potential to support the reduction of waste in industrialized building construction facilities.

Item Type: Article
Additional Information: Funding information: The authors gratefully acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada (File No. IRCPJ 419145–15).
Uncontrolled Keywords: Window manufacturing, Waste management, Deep learning, Machine vision, Construction waste
Subjects: H100 General Engineering
H300 Mechanical Engineering
H900 Others in Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: Rachel Branson
Date Deposited: 01 Feb 2022 09:55
Last Modified: 08 Apr 2022 14:45
URI: http://nrl.northumbria.ac.uk/id/eprint/48310

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