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REVIEW ARTICLE

Hybrid multimodal artificial intelligence, vision sensory, and robotic cyber-physical systems for plastic inspection and sorting in digital circular remanufacturing: A review

Syed Ali Hassan1* ,  Michail J. Beliatis1*
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1 Department of Business Development and Technology, School of Business and Social Sciences, Aarhus University, Herning, Central Denmark Region, Denmark
IJAMD, 025340030 https://doi.org/10.36922/IJAMD025340030
Received: 18 August 2025 | Revised: 14 January 2026 | Accepted: 23 January 2026 | Published online: 27 March 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Plastic inspection has emerged as an important component of industrial manufacturing processes, quality control, and recycling, driven by a growing emphasis on sustainable, circular, and efficient modes of production. This systematic narrative review focuses on three key areas: (i) a review on the imaging techniques used in the plastic industry for creating training datasets for artificial intelligence (AI) models; (ii) an evaluation of various AI approaches, including support vector machines (SVMs), deep reinforcement learning (DRL), convolutional neural networks (CNNs), and hybrid/multimodal techniques; and (iii) the integration of these techniques within robotic cyber-physical systems (CPS) for the automation of plastic defect identification, material classification, and sorting for recycling/remanufacturing, supplemented by circular business aspects. CNNs demonstrate exceptional performance in feature extraction and in detecting surface defects, such as scratches, cracks, and inconsistencies in plastic materials. SVMs, with their robustness to small, noisy datasets, provide accurate classification and quality control, making them a valuable complement to CNNs. Hybrid approaches that combine CNNs and SVMs leverage the strengths of both methods for complex tasks, thereby maximizing the advantages of each. DRL enhances the inspection and sorting capabilities of robotic CPS when integrated together. Despite these advancements, challenges remain, including high resource costs, data-intensive requirements, and constraints on real-time implementation. Potential solutions include adopting efficient architectures and lightweight frameworks. A pilot application of these AI strategies within a robotic CPS demonstrates their transformative potential to automate large-scale remanufacturing and recycling systems efficiently, accurately, and in an eco-friendly manner, supporting circular economy principles and sustainability.

Graphical abstract
Keywords
Plastic inspection and sorting
Deep reinforcement learning
Hybrid artificial intelligence models
Convolutional neural networks
Multimodal machine vision sensors
Circular remanufacturing operations
Quantum hybrid intelligence
Robot process automation
Funding
The authors gratefully acknowledge the support of the EIT RawMaterials EPICENTER project (Project Agreement No. 23006) and the Interreg Baltic Sea Region Project: CompositeCircle (Index No. PIFC3.2079, Project No.C072), which partially funded the development of this study.
Conflict of interest
Michail J. Beliatis is one of the Associate Editors of the journal but was not involved in the editorial and peer review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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