About this Research Topic

Background

Machine learning, a fascinating field of study, has indeed revolutionized the way computers learn from data patterns, enabling them to perform classification and regression tasks. Its practical applications are incredibly diverse, ranging from self-driving vehicles and image recognition to language processing and financial management. However, a particularly exciting area where machine learning has made significant strides in recent years is in forming processes.

With the advent of big data and ever-improving computer performance, machine learning has found its way into various aspects of forming processes. The applications are boundless! Consider robust and sensitivity analysis, which can help us understand how different factors impact the outcome of forming processes. Machine learning lends itself to the design, optimization, control, and maintenance of these processes, making them more efficient and reliable.

But that's not all! Machine learning has also proven effective in identifying and modelling material behavior, which is crucial in forming processes. By analyzing vast amounts of data, these models can capture the intricate relationships between various material properties and their behavior during the forming process. This understanding can lead to better control and decision-making, ultimately improving the quality of the formed materials.

Additionally, machine learning has shown promise in the detection and prediction of forming defects. By analyzing patterns in the data, these algorithms can identify potential defects early on, allowing for timely intervention and prevention. This can save both time and resources, as defects can be costly and lead to suboptimal outcomes.

The goal of this Special Issue is to bring together research works that focus specifically on the applications of machine learning in forming processes. We encourage original contributions that delve into the following topics, among others:

1. Material modelling and mechanical testing: Explore how machine learning can enhance our understanding of material behavior during forming processes. This includes the development of models and techniques that leverage machine learning to improve material characterization and mechanical testing.

2. Sensitivity and uncertainty analysis: Utilize machine learning algorithms to assess the sensitivity and uncertainty of various parameters in forming processes. By understanding the impact of different factors, we can optimize processes for improved performance and stability.

3. Classification and prediction of defects: Investigate how machine learning can aid in the classification and prediction of defects in forming processes. This includes developing models and algorithms that can detect, classify, and predict potential defects early on, allowing for proactive measures to be taken.

4. Design, optimization, and control of forming processes: Leverage machine learning techniques to optimize the design and control of forming processes. This involves exploring novel algorithms and methodologies that can help achieve desired outcomes with enhanced efficiency and accuracy.

We encourage researchers to submit original works that contribute to our understanding and utilization of machine learning in forming processes. The aim is to advance our knowledge in this exciting area and pave the way for further innovations and improvements.

Keywords: Machine Learning, Forming Processes, Material Modelling, Forming Defects, Mechanical Testing

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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