Investigation of failures in rotational moulding using historical production dataset and machine learning

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Machine learning model predicts failure probabilities in batch production, suggesting optimal parameters for product quality.

Machine learning model predicted failure probabilities with an accuracy of 97.17%, aiding in defect reduction.
Analysis focused on historical production data, identifying critical parameters like heating temperature and speed.
Predictive modeling enhances rotational moulding efficiency without needing extensive sensorization.
Findings emphasize the importance of maintaining product quality through optimal production parameters.

Ördek et al. (2025) studied this question.

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View Full Paper

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Authors

Machine learning model predicts failure probabilities in batch production, suggesting optimal parameters for product quality.

Machine learning model predicted failure probabilities with an accuracy of 97.17%, aiding in defect reduction.
Analysis focused on historical production data, identifying critical parameters like heating temperature and speed.
Predictive modeling enhances rotational moulding efficiency without needing extensive sensorization.
Findings emphasize the importance of maintaining product quality through optimal production parameters.

Ördek et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark