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Uncontrolled environmental conditions often impact manufacturing processes and lead to product quality fluctuations. For machine tools, thermal influences are a major limitation to the volumetric performance. Climate controls for the shop floor, and machines, or thermally stable structural designs are economically not feasible, promoting control-based compensation as a possible solution. Since the relationship between disturbing quantities and effects are complex and specific to each machine, appropriate modelling is a critical requirement. The authors describe an approach for developing hybrid models, superposing white-box model knowledge, and machine learning. The overall effort can be optimized by combining and balancing different modelling methods, like designing the physical model part and training intelligent algorithms. A general model structure allows a continuous integration of different white-box and black-box model components. The authors integrate self-developed smart sensors into a demonstrator machine tool to test and validate the performance of the approach.

Industry 4.0 requires flexible and fast solutions to automatically identify and handle different parts during the production process. For such a multiclassification (MCC) task, usually feature detectors or machine learning approaches are used. However, increasing product variety and consequently rising number of classes impel the expansion of data set sizes, a mandatory procedure for accurate object identification. The acquisition of a sufficiently large number of sample images is a costly and time-consuming issue. This work presents an iterative reference data set creation method of small size image data sets for feature descriptor-based MCC. The novel method compares the shape of an object in a candidate image with the shapes from a growing reference image set consisting of all previously accepted candidates. An image is assigned to or rejected from the reference data set depending on the found shape similarity. Rejected images form a grid search set that is later used to optimize the feature descriptors’ hyperparameters and enable the addition of new classes. The benefits of this method are the small number of images to be acquired for MCC, the possibility of adding new parts without re-training, the little overhead for new applications, and its compatibility with most commonly used feature descriptors. When compared with a small control data set provided by an inexperienced user with an accuracy of 49% for a five classes MCC, the reference data set built by the novel method gains 20% on the accuracy (69%, 21 images in total) and can be performed by the same inexperienced user.

Electric motors are subjected to many different quality control tests during their manufacture. Some of these tests are typically performed by human operators. It is well known in the literature that these operators are not reliable for repetitive inspections due to factors such as subjectivity and fatigue. Vision systems come as an alternative to perform visual tests for quality control. The authors have already proposed a vision system based on edge-detection tools to identify defects in electric motors characterized by one or more coil segments of the winding that are not properly fastened to the other coils and are placed in the projection of the orifice where the rotor is inserted. In this paper, a comparison between an improved version of this first algorithm and a convolutional neural network is done. Data augmentation is used to enhance the image data set, improving the reliability of network training. This dataset was also extrapolated to emulate the results of a manufacturing line. For a test dataset, neural networks presented better results than the edge detection algorithm, but their performance was similar for extrapolated images. For large production volumes, it is recommended the use of neural networks with proper training, but for small datasets the edge detection algorithm with proper parametrization is still the best choice.