Explainable artificial intelligence for manufacturing process modelling and optimisation: an integrated ANN and LIME approach
将可解释人工智能融入实验设计，用人工神经网络构建高精度模型，再用LIME方法解释模型并识别重要实验因素，帮助工程师理解优化过程。
Design of experiments (DOE) is a crucial tool for improving product or process quality. Accurately modelling the relationship between process variables and quality characteristics is essential. However, traditional DOE methods have limitations when dealing with complex data and cannot effectively fit the data. Data-driven methods can handle complex data and construct high-precision empirical models, but they often lack interpretability. Therefore, in this article, we incorporate explainable artificial intelligence (XAI) into DOE for the purpose of constructing high-precision models, improving interpretability, and identifying important experimental factors. First, we use artificial neural networks (ANN) to model DOE data and construct a high-precision empirical model. Then, since engineers usually focus only on the optimal solution and its nearby local space, we use the local interpretable model-agnostic explanation (LIME) method to interpret the ANN model and enhance the local interpretability of the model. Finally, we design an experiment and obtain the response using the ANN model to identify the important experimental factors. At the end of the article, we validate the accuracy of the ANN model through a simulation study, and illustrate the proposed method using two datasets from designed experiments.