2812. Machine Learning Based Hierarchical Algorithms: An Application In Plastic Injection Industry
Invited abstract in session TA-28: AI and Machine Learning for Decision Support, stream Decision Support Systems.
Tuesday, 8:30-10:00Room: Maurice Keyworth 1.03
Authors (first author is the speaker)
| 1. | Alara Baysal
|
| Data Science, Ozyegin University | |
| 2. | Cevdet Utku Şafak
|
| Industrial Engineering, Ozyegin University | |
| 3. | Ahmet Eralp Avunduk
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| Data Science, Özyeğin University | |
| 4. | Eyyüb Batuhan Ünal
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| Data Science, Ozyegin University | |
| 5. | Erinc Albey
|
| Industrial Engineering, Ozyegin University |
Abstract
Many plastic injection moulding facilities rely on labor-intensive quality control methods, presenting an opportunity for machine learning algorithms to enhance real-time detection of defective products. This study introduces a hierarchical algorithm that utilizes machine parameters and cavity sensor data to evaluate plastic injection parts' quality and fault types.
We explore the effects of sensor placements, including pressure and temperature sensors near mould gates and cavity edges, and extract features using linear fitting techniques. These features, combined with machine parameters, facilitate the prediction of multiple fault types. The hierarchical approach provides valuable insights into process deviations by capturing interactions between machine settings and sensor data.
The results show the algorithm achieves approximately 90% accuracy in 1/0 fault detection and between 90% and 95% for various common faults in the industry. These findings could help recommend real-time corrective actions, leading to improved equipment efficiency and reduced downtime.
Keywords
- Artificial Intelligence
- Analytics and Data Science
- Decision Support Systems
Status: accepted
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