Assessment of milling condition by image processing of the produced surfaces

The digital industrial revolution calls for smart manufacturing plants, i.e. plants that include sensors and vision systems accompanied with artificial intelligence and advanced data analytics in order to meet the required accuracy, reliability and productivity levels. In this paper, we introduce a surface analysis and classification approach based on a deep learning algorithm. The approach is intended to let machining centres recognise the adequacy of process parameters adopted for the milling operation performed, based on the phenomenological effects left on the machined surface. Indeed, the operator will be able to understand how to change process parameters to improve workpiece quality of subsequent parts by a reverse engineering procedure that reconstructs the process parameters that generated the analysed surface. A shallow convolutional neural network was proposed to work on surface image patches based on a limited training dataset of optimal and undesired cutting conditions. The architecture consists of a series of 3 stacked convolutional blocks. The performance of the proposed solution was validated through 5-fold cross-validation, measuring the mean and standard deviation of the f1-score metric. The algorithm arrived at outperformed the best state-of-the-art approach by 4.8% when considering average classification performance.