Simulation du procédé d'assemblage par clinchage : de la mise en forme à la résistance

Ce papier présente une simulation par éléments finis d'essais de résistance d'un point de clinch en traction pure –postérieur à la simulation numérique du processus de mise en forme du point-. Les simulations sont validées par comparaison avec des résultats expérimentaux. Les influences sur les prédictions numériques de différents paramètres numériques et paramètres procédé ont été évaluées

A workflow for synthetic data generation and predictive maintenance for vibration data

Digital twins, virtual representations of real-life physical objects or processes, are becoming widely used in many different industrial sectors. One of the main uses of digital twins is predictive maintenance, and these technologies are being adapted to various new applications and datatypes in many industrial processes. The aim of this study was to propose a methodology to generate synthetic vibration data using a digital twin model and a predictive maintenance workflow, consisting of preprocessing, feature engineering, and classification model training, to classify faulty and healthy vibration data for state estimation. To assess the success of the proposed workflow, the mentioned steps were applied to a publicly available vibration dataset and the synthetic data from the digital twin, using five different state-of-the-art classification algorithms. For several of the classification algorithms, the accuracy result for the classification of healthy and faulty data achieved on the public dataset reached approximately 86%, and on the synthetic data, approximately 98%. These results showed the great potential for the proposed methodology, and future work in the area.publishedVersio

Assessment of feature engineering and long short-term memory for structure loss identification from process data in monocrystalline silicon growth by the Czochralski method

Structure loss impairs the quality of monocrystalline ingots and represents a major productivity loss for crystal growers. If structure loss could be predicted in advance it could help reduce the lost production time or preventive measures could be initiated. For this reason, feature engineering and machine learning by long short-term memory (LSTM) is used to assess if structure loss could be predicted from sensor data collected during growth of ingots. The method is not able to predict structure loss in advance, and the predictions may likely be based on the length of the signal, which is shortened for structure loss ingots as their growth is interrupted, and not actual features in the sensor readings

A workflow for synthetic data generation and predictive maintenance for vibration data

Digital twins, virtual representations of real-life physical objects or processes, are becoming widely used in many different industrial sectors. One of the main uses of digital twins is predictive maintenance, and these technologies are being adapted to various new applications and datatypes in many industrial processes. The aim of this study was to propose a methodology to generate synthetic vibration data using a digital twin model and a predictive maintenance workflow, consisting of preprocessing, feature engineering, and classification model training, to classify faulty and healthy vibration data for state estimation. To assess the success of the proposed workflow, the mentioned steps were applied to a publicly available vibration dataset and the synthetic data from the digital twin, using five different state-of-the-art classification algorithms. For several of the classification algorithms, the accuracy result for the classification of healthy and faulty data achieved on the public dataset reached approximately 86%, and on the synthetic data, approximately 98%. These results showed the great potential for the proposed methodology, and future work in the area

Statistical Analysis of Structure Loss in Czochralski Silicon Growth

In Czochralski monocrystalline silicon growth, structure loss (SL) is the loss of the mono-crystalline structure. It represents a significant loss of productivity. In this work, this phenomenon is investigated by statistical analysis of production data of roughly 14000 ingots produced over a year of time at NorSun factory in Årdal, Norway. It is found that ingots with structure loss typically have lower heater power and temperature fluctuations than ingots without structure loss after four hours of body (ca. 240 mm). Particularly, ingots without manual adjustment by furnace operator have significantly higher frequency of structure loss than ingots for which the operator has increased the temperature one or more times. Most ingots with structure loss are also found to have a higher pull speed on average than ingots without structure loss, and that there is a threshold below which no ingots had structure loss. A binary logistic regression was used for classification of ingots with and without structure loss and 30% of the data was used to comparison of predictions of the model. Using only the standard deviation of the temperature fluctuations around a moving average provided a prediction accuracy of 99.6%, for ingots that have passed six hours of body (ca. 360 mm).publishedVersio

Statistical analysis of structure loss in Czochralski silicon growth

In Czochralski monocrystalline silicon growth, structure loss (SL) is the loss of the mono-crystalline structure. It represents a significant loss of productivity. In this work, this phenomenon is investigated by statistical analysis of production data of roughly 14000 ingots produced over a year of time at NorSun factory in Årdal, Norway. It is found that ingots with structure loss typically have lower heater power and temperature fluctuations than ingots without structure loss after four hours of body (ca. 240 mm). Particularly, ingots without manual adjustment by furnace operator have significantly higher frequency of structure loss than ingots for which the operator has increased the temperature one or more times. Most ingots with structure loss are also found to have a higher pull speed on average than ingots without structure loss, and that there is a threshold below which no ingots had structure loss. A binary logistic regression was used for classification of ingots with and without structure loss and 30% of the data was used to comparison of predictions of the model. Using only the standard deviation of the temperature fluctuations around a moving average provided a prediction accuracy of 99.6%, for ingots that have passed six hours of body (ca. 360 mm).publishedVersio

Statistical Analysis of Structure Loss in Czochralski Silicon Growth

In Czochralski monocrystalline silicon growth, structure loss (SL) is the loss of the mono-crystalline structure. It represents a significant loss of productivity. In this work, this phenomenon is investigated by statistical analysis of production data of roughly 14000 ingots produced over a year of time at NorSun factory in Årdal, Norway. It is found that ingots with structure loss typically have lower heater power and temperature fluctuations than ingots without structure loss after four hours of body (ca. 240 mm). Particularly, ingots without manual adjustment by furnace operator have significantly higher frequency of structure loss than ingots for which the operator has increased the temperature one or more times. Most ingots with structure loss are also found to have a higher pull speed on average than ingots without structure loss, and that there is a threshold below which no ingots had structure loss. A binary logistic regression was used for classification of ingots with and without structure loss and 30% of the data was used to comparison of predictions of the model. Using only the standard deviation of the temperature fluctuations around a moving average provided a prediction accuracy of 99.6%, for ingots that have passed six hours of body (ca. 360 mm)

Statistical analysis of structure loss in Czochralski silicon growth

In Czochralski monocrystalline silicon growth, structure loss (SL) is the loss of the mono-crystalline structure. It represents a significant loss of productivity. In this work, this phenomenon is investigated by statistical analysis of production data of roughly 14000 ingots produced over a year of time at NorSun factory in Årdal, Norway. It is found that ingots with structure loss typically have lower heater power and temperature fluctuations than ingots without structure loss after four hours of body (ca. 240 mm). Particularly, ingots without manual adjustment by furnace operator have significantly higher frequency of structure loss than ingots for which the operator has increased the temperature one or more times. Most ingots with structure loss are also found to have a higher pull speed on average than ingots without structure loss, and that there is a threshold below which no ingots had structure loss. A binary logistic regression was used for classification of ingots with and without structure loss and 30% of the data was used to comparison of predictions of the model. Using only the standard deviation of the temperature fluctuations around a moving average provided a prediction accuracy of 99.6%, for ingots that have passed six hours of body (ca. 360 mm)

Impact of thermal history on defects formation in the last solid fraction of Cz silicon ingots

publishedVersio