Integrating advanced communication protocols in production has accelerated the adoption of data-driven predictive quality methods, notably machine learning (ML) models. However, ML models in image classification often face significant uncertainties arising from model, data, and domain shifts. These uncertainties lead to overconfidence in the classification model's output. To better understand these models, sensitivity analysis can help to analyze the relative influence of input parameters on the output. This work investigates the sensitivity of image classification models used for predictive quality. We propose modeling the distributional domain shifts of inputs with random variables and quantifying their impact on the model's outputs using Sobol indices computed via generalized polynomial chaos (GPC). This approach is validated through a case study involving a welding defect classification problem, utilizing a fine-tuned ResNet18 model and an emblem classification model used in BMW Group production facilities.

翻译：在工业生产中集成先进通信协议，加速了数据驱动的预测质量方法（特别是机器学习模型）的采纳。然而，用于图像分类的机器学习模型常常面临来自模型、数据及领域偏移的重大不确定性。这些不确定性导致分类模型输出呈现过度自信。为更好地理解这些模型，敏感性分析有助于评估输入参数对输出的相对影响。本研究探讨了用于预测质量的图像分类模型的敏感性。我们提出使用随机变量对输入的分布性领域偏移进行建模，并通过广义多项式混沌计算的Sobol指数来量化其对模型输出的影响。该方法通过一个涉及焊接缺陷分类问题的案例研究得到验证，该研究使用了微调的ResNet18模型以及宝马集团生产设施中采用的徽标分类模型。