A powerful AI model unlocks the secrets of semi-solid die casting, transforming defect detection and paving the way for precision manufacturing breakthroughs.
Research: Quality prediction of semi-solid die casting of aluminum alloy in terms of machine learning. Image Credit: Javier Ruiz / Shutterstock
Researchers in China have developed a machine-learning model to identify defective products by detecting injection pressure during the semi-solid die-casting process. This provides a foundation for monitoring and further optimizing the manufacturing process. The study also emphasizes how these techniques can improve quality control for high-performance components used in the automotive and telecommunications industries. The research paper "Quality prediction of semi-solid die casting of aluminum alloy in terms of machine learning" was published in the journal Advanced Manufacturing.
Compared to traditional die casting with full liquid melt, semi-solid die casting applies a higher viscous slurry. It fills the die cavity with laminar flow, thus avoiding entrapment defects and enhancing the mechanical properties. However, this technology has not yet achieved the widespread commercial application envisioned in its early stage. One of the most critical challenges is that the process window is narrower, and the process stability is poorer compared to traditional die casting. Addressing this challenge, Professor Qiang Zhu from Southern University of Science and Technology and Associate Professor Xiaogang Hu from Sun Yat-Sen University, along with their co-authors Zhiyuan Wang, Gan Li, Zhen Xu, and Hongxing Lu, have developed a machine learning model to identify defective products through the detection of injection pressure during the semi-solid die-casting process, thereby providing a foundation for monitoring and further optimizing the manufacturing process.
"Semi-solid slurry provides the ability for smooth filling of the mold cavity during die casting, but it also results in extreme sensitivity of the process to production conditions, including mold temperature and ambient temperature," explains Dr. Xiaogang Hu. "Fluctuations in these process conditions are unavoidable in actual production, leading to poor quality stability of semi-solid die castings."
"We try to establish a connection between process data and product quality using machine learning methods." Dr. Hu says, "The main challenge lies in selecting appropriate indicators to describe these process fluctuations. Based on our findings, the V-P transition point and filling pressure were identified as critical parameters affecting defect formation."
The authors introduced data slicing and curve node extraction approaches based on domain knowledge in the data preparation phase. The results show that training with the filtered data yields significantly better outcomes than using the raw data directly. This indicates that the data preprocessing and feature selection methods are effective, considerably enhancing the model's predictive performance. The accuracy score for internal defect prediction improved by 9.6%, while external defect prediction saw an increase of 53%.
The authors have compared various machine learning algorithms, and the results illustrate that the multi-layer perceptron (MLP) model achieved the highest accuracy in predicting the quality of semi-solid die castings. For example, the MLP model achieved an accuracy score of 0.9238 during ten k-fold cross-validations. The characteristics of the filling pressure curve can predict the probability and type of defect formation. More importantly, this model has helped reveal the mechanisms behind the formation of surface and internal defects in semi-solid die castings.
"The predictive model tells us that during the filling stage, it is not necessarily the case that the higher the solid fraction of the slurry, the smoother the filling will be," says Dr. Hu, "there is an optimal solid fraction, higher or lower than that can cause turbulence."
Professor Qiang Zhu, the leader of this research project, emphasizes that machine learning methods offer an opportunity to handle the complex nonlinear relationships among high-dimensional physical data and have been widely applied in intelligent manufacturing in recent years. The authors highlight that their model's applicability extends to other alloys, demonstrating generalizability to different semi-solid processes. Due to its poor process stability, semi-solid die-casting technology has not yet achieved large-scale engineering application. Utilizing the quality prediction model based on filling pressure can help us better monitor process fluctuations and provide a foundation for subsequent process interventions.
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