| 1. |
- Ashourpour, M., et al.
(author)
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Real-Time Defect and Object Detection in Assembly Line : A Case for In-Line Quality Inspection
- 2023
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In: Flexible Automation and Intelligent Manufacturing. - : Springer. - 9783031382406 - 9783031382413 ; , s. 99-106
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Conference paper (peer-reviewed)abstract
- Identification of flawed assemblies and defective parts or products as early as possible is a daily struggle for manufacturing companies. With the ever-increasing complexity of assembly operations and manufacturing processes alongside the need for shorter cycle times and higher flexibility of productions, companies cannot afford to check for quality issues only at the end of the line. In-line quality inspection needs to be considered as a vital part of the process. This paper explores use of a real-time automated solution for detection of assembly defects through YOLOv8 (You Only Look Once) deep learning algorithm which is a class of convolutional neural networks (CNN). The use cases of the algorithm can be extended into detection of multiple objects within a single image to account for not only defects and missing parts in an assembly operation, but also quality assurance of the process both in manual and automatic cells. An analysis of YOLOv8 algorithm over an industrial case study for object detection shows the mean average precision (mAP) of the model on the test dataset and consequently its overall performance is extremely high. An implementation of this model would facilitate in-line quality inspection and streamline quality control tasks in complex assembly operations.
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| 2. |
- Massouh, Bassam, et al.
(author)
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Online Hazard Detection in Reconfigurable Plug & Produce Systems
- 2024
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In: Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems.. - : Springer Nature. - 9783031382406 - 9783031382413 ; , s. 889-897
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Conference paper (peer-reviewed)abstract
- Plug & Produce is a modern automation concept in smart manufacturing for modular, quick, and easy reconfigurable production. The system’s flexibility allows for the configuration of production with abstraction, meaning that the production resources participating in a specific production plan are only known in the online phase. The safety assurance process of such a system is complex and challenging. This work aims to assist the safety assurance when utilizing a highly flexible Plug & Produce concept that accepts instant logical and physical reconfiguration. In this work, we propose a concept for online hazard identification of Plug & Produce systems, the proposed concept, allows for the detection of hazards in the online phase and assists the safety assurance as it provides the hazard list of all possible executable alternatives of the abstract goals automatically. Further, it combines the safety-related information with the control logic allowing for safe planning of operations. The concept was validated with a manufacturing scenario that demonstrates the effectiveness of the proposed concept.
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| 3. |
- Mosa, Waddah, et al.
(author)
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Software-supported Hazards Identification for Plug & Produce Systems
- 2024
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In: Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems. - : Springer Nature. - 9783031382406 - 9783031382413 ; , s. 603-610
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Conference paper (peer-reviewed)abstract
- This paper presents a model-based safety software that performs Hazard Identification (HI) for Plug&Produce (P&P) systems automatically. P&P systems, inspired by Plug&Play in computers, aim to integrate devices and tools into the manufacturing system with minimum integration efforts and costs. When plugging a new resource, it will exchange all the required information with the manufacturing system and be ready to operate within minutes rather than days or weeks. One of the challenges that face this concept is performing proper risk assessment after each change in the system. Therefore, the risk assessment needs to be automated as much as possible. This paper is about automating one risk assessment step: Hazard Identification. A new safety model is designed to identify hazards. The presented software analyses this model by implementing a novel algorithm that uses lookup tables to cover various possible hazards when resources work together. This software will support the risk reduction team by drastically reducing the time needed for HI and being ready for the next steps in risk analyses. Automating identifying hazards is an essential step towards automating the entire risk assessment process and achieving safe P&P systems.
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