Human Work Support Technology Utilizing Sensor Data
| Autor: | Shigeyasu Tsubaki, Yasuyuki Mimatsu, Ryota Higashi, Sakurai Yuichi, Kazuaki Suzuki |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science media_common.quotation_subject 02 engineering and technology 010501 environmental sciences 01 natural sciences Reliability engineering Identification (information) 020901 industrial engineering & automation Work (electrical) Factory (object-oriented programming) Quality (business) Product (category theory) Productivity 0105 earth and related environmental sciences media_common |
| Zdroj: | 2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA). |
| DOI: | 10.1109/iciea49774.2020.9102053 |
| Popis: | In response to problems associated with the shortage of factory workers, the demand for efficiency maximization by eliminating wasted work has recently become stronger. Moreover, to ensure compliance with product quality, the demand for reliable work execution is also increasing. Conventionally, productivity has fallen because workers must manually enter a system to start and finish work. To circumvent this, we have developed advanced human work techniques, “automatic switching of work instruction screen” and “real-time work deviation detection”, by utilizing deep learning technology. Compared to conventional approaches, we achieved a 15% reduction in product assembly time and a deviation detection leak of almost zero (more than 95% work identification accuracy). |
| Databáze: | OpenAIRE |
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