Arboretti Rosa, Ceccato Riccardo, Pegoraro Luca, Salmaso Luigi, Housmekerides Chris, Spadoni Luca, Pierangelo Elisabetta, Quaggia Sara, Tveit Catherine, Vianello Sebastiano
Department of Civil, Environmental and Architectural Engineering, Università degli Studi di Padova, Padua, Italy.
Department of Management and Engineering, Università degli Studi di Padova, Vicenza, Italy.
J Appl Stat. 2021 Mar 26;49(10):2674-2699. doi: 10.1080/02664763.2021.1907840. eCollection 2022.
Industrial statistics plays a major role in the areas of both quality management and innovation. However, existing methodologies must be integrated with the latest tools from the field of Artificial Intelligence. To this end, a background on the joint application of Design of Experiments (DOE) and Machine Learning (ML) methodologies in industrial settings is presented here, along with a case study from the chemical industry. A DOE study is used to collect data, and two ML models are applied to predict responses which performance show an advantage over the traditional modeling approach. Emphasis is placed on causal investigation and quantification of prediction uncertainty, as these are crucial for an assessment of the goodness and robustness of the models developed. Within the scope of the case study, the models learned can be implemented in a semi-automatic system that can assist practitioners who are inexperienced in data analysis in the process of new product development.
工业统计学在质量管理和创新领域都发挥着重要作用。然而，现有的方法必须与人工智能领域的最新工具相结合。为此，本文介绍了实验设计（DOE）和机器学习（ML）方法在工业环境中的联合应用背景，并给出了一个来自化学工业的案例研究。通过DOE研究来收集数据，并应用两个ML模型来预测响应，其性能优于传统建模方法。重点在于因果调查和预测不确定性的量化，因为这些对于评估所开发模型的优劣和稳健性至关重要。在案例研究范围内，所学习到的模型可以在半自动系统中实现，该系统可以在新产品开发过程中协助缺乏数据分析经验的从业者。
