แนวคิดหลัก
The author explores the potential of deep learning in advancing additive manufacturing, highlighting its ability to address complex challenges and improve the AM process.
บทคัดย่อ
The content discusses the intersection of additive manufacturing and deep learning, emphasizing the role of DL in overcoming challenges like process interactions. It reviews recent studies applying DL in AM for enhanced processes and quality control. The paper also outlines future opportunities for research in this domain.
Additive manufacturing (AM) is gaining popularity due to customization demands, but faces challenges like part quality inconsistency. Traditional methods are time-consuming, leading to a shift towards data-driven machine learning (ML) techniques. Deep learning (DL) shows promise in addressing AM complexities by automatically capturing relationships from high-dimensional data.
Research has focused on using DL for various aspects of AM, including design optimization, thermal profile modeling, and process monitoring. DL techniques like CNNs and RNNs have been applied to predict thermal fields, optimize tool paths, and monitor defects. The integration of physics-informed DL models enhances accuracy in predicting material properties.
The content highlights the importance of input data quality for successful DL implementation in AM. Various types of data such as images, point clouds, and spectral emissions are used for monitoring processes and detecting anomalies. Multi-sensor approaches combining AE signals, thermal images, and other sensor data offer comprehensive monitoring solutions.
Overall, the paper provides a comprehensive overview of how DL is revolutionizing additive manufacturing processes by addressing challenges and enhancing efficiency through advanced monitoring techniques.
สถิติ
The popularity of additive manufacturing (AM) market size increased from USD 20,670 million in 2023 to USD 98,310 million in 2032.
Researchers use CNN-based models for image-based deficiencies monitoring for quality assessment.
Transfer learning is utilized from effective networks to address AM defect detection issues.
Acoustic emissions (AE) are used for defect detection analysis in AM processes.
Spectral emissions are employed for signal-based monitoring in AM processes.
Multi-sensor signal-based monitoring integrates various sensors like infrared cameras for defect detection.
Point clouds are utilized as 3D representations to capture geometric variations in AM processes.
คำพูด
"DL can automatically capture complex relationships from high-dimensional data without hand-crafted feature extraction."
"The recent emergence of deep learning (DL) is showing great promise in overcoming many challenges faced by additive manufacturing."
"Researchers are continuously analyzing AM process from different perspectives using machine learning techniques."