03/02【SMART UQ原廠線上課程】AI人工智慧與機器學習-應用於汽車應用

Improving automotive design and manufacturing processes requires understanding and accounting for uncertainties. For example, there will be uncertainty in the properties of the materials used and manufacturing process for any component. Even for a perfect process that produced identical components, the performance of each will vary depending on uncertainties associated with its use. For example, the fatigue life of a component could vary based on the vehicle model it is installed in and road conditions. Determining optimal design configurations or manufacturing processes under such uncertainties is difficult and can require substantial time using test data, experiments, and physics-based simulations (e.g. CFD and FEA). Also, it is time consuming to sort through large amounts of manufacturing data to identify the most useful and relevant information. The solution is to first train an AI or machine learning model using data from the design or manufacturing process collected by an intelligent sampling plan. Once trained, the model can rapidly make accurate predictions for all what-if scenarios. With the roadblock of computational cost removed, many otherwise infeasible analyses may be conducted to improve the design or process. Join us for this webinar to learn how AI and machine learning models can be used to enhance automotive design and manufacturing applications.

改進汽車設計和製造流程需要瞭解和考慮不確定性。

例如，任何元件所用材料的特性和製造過程都會存在不確定性。 即使對於生產相同元件的完美工藝，每個元件的性能也會因與其使用相關的不確定性而有所不同。

例如，部件的疲勞壽命可能會根據其安裝的車輛型號和路況而有所不同。

在這種不確定性下確定最佳設計配置或製造過程很困難，並且可能需要大量時間使用測試資料、實驗和基於物理的類比（例如 CFD 和 FEA）。

此外，對大量製造資料進行分類以識別最有用和最相關的資訊也非常耗時。

解決方案是首先使用智慧抽樣計畫收集的設計或製造過程中的資料來訓練人工智慧或機器學習模型。 經過訓練後，該模型可以快速準確地預測所有假設場景。

隨著計算成本的障礙被移除，許多原本不可行的分析可能會被執行以改進設計或過程。 加入我們的網路研討會，瞭解如何使用 AI 和機器學習模型來增強汽車設計和製造應用。