Screw detection for disassembly of electronic waste using reasoning and re-training of a deep learning model
| Autor: | Maurice Pagnucco, Gwendolyn Foo, Sami Kara |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Training set business.product_category Computer science business.industry Deep learning 02 engineering and technology 010501 environmental sciences equipment and supplies 01 natural sciences Electronic waste Fastener Crosshead Reliability engineering Generic knowledge 020901 industrial engineering & automation Hazardous waste General Earth and Planetary Sciences Artificial intelligence business Embodied energy 0105 earth and related environmental sciences General Environmental Science |
| Zdroj: | Procedia CIRP. 98:666-671 |
| ISSN: | 2212-8271 |
| Popis: | Growing populations, increasing standards of living, and reliance on advancing technologies are resulting in an emerging abundance of electronic waste. Automating disassembly of these electronic products is a vital part of improving end-of-life product treatment where hazardous chemicals and materials are present. Furthermore, non-destructive disassembly is ideal to preserve the embodied energy in components from manufacturing. This requires the removal, and hence detection, of fasteners like screws in a disassembly environment. Crosshead screws are a common fastener type used in LCD monitors. This paper proposes a method of screw detection for disassembly and presents results of detection of crosshead screws on components of various models of LCD monitors in a disassembly cell. The system first applies gamma correction to standardize brightness––or luminance–– of images. Generic knowledge about screw features in the form of a deep learning model is then used to visually detects screws. The results are analyzed against common screw location combinations on product components in order to logically reason about possible undetected screws that are also likely to exist. Finally, true negative detections are collected as new training data and the deep learning model is re-trained on these missed detections to improve performance; tailoring and adapting to the environment of the specific disassembly cell. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|