Fringe projection profilometry in combination with other optical measuring technologies has established itself over the last decades as an essential complement to conventional, tactile measuring devices. The non-contact, holistic reconstruction of complex geometries within fractions of a second in conjunction with the lightweight and transportable sensor design open up many fields of application in production metrology. Furthermore, triangulation-based measuring principles feature good scalability, which has led to 3D scanners for various scale ranges. Innovative and modern production processes, such as sheet-bulk metal forming, thus, utilize fringe projection profilometry in many respects to monitor the process, quantify possible wear and improve production technology. Therefore, it is essential to identify the appropriate 3D scanner for each application and to properly evaluate the acquired data. Through precise knowledge of the measurement volume and the relative uncertainty with respect to the specimen and scanner position, adapted measurement strategies and integrated production concepts can be realized. Although there are extensive industrial standards and guidelines for the quantification of sensor performance, evaluation and tolerancing is mainly global and can, therefore, neither provide assistance in the correct, application-specific positioning and alignment of the sensor nor reflect the local characteristics within the measuring volume. Therefore, this article compares fringe projection systems across various scale ranges by positioning and scanning a calibrated sphere in a high resolution grid.

In recent years, an increasing number of machine learning applications in tribology and coating design have been reported. Motivated by this, this contribution highlights the use of Gaussian processes for the prediction of the resulting coating characteristics to enhance the design of amorphous carbon coatings. In this regard, by using Gaussian process regression (GPR) models, a visualization of the process map of available coating design is created. The training of the GPR models is based on the experimental results of a centrally composed full factorial 2³ experimental design for the deposition of a-C:H coatings on medical UHMWPE. In addition, different supervised machine learning (ML) models, such as Polynomial Regression (PR), Support Vector Machines (SVM) and Neural Networks (NN) are trained. All models are then used to predict the resulting indentation hardness of a complete statistical experimental design using the Box–Behnken design. The results are finally compared, with the GPR being of superior performance. The performance of the overall approach, in terms of quality and quantity of predictions as well as in terms of usage in visualization, is demonstrated using an initial dataset of 10 characterized amorphous carbon coatings on UHMWPE.

This work aims to evaluate the current state of research on the use of artificial intelligence, deep learning, digitalization, and Data Mining in product development, mainly in the mechanical and mechatronic domain. These methods, collectively referred to as “digital engineering”, have the potential to disrupt the way products are developed and improve the efficiency of the product development process. However, their integration into current product development processes is not yet widespread. This work presents a novel consolidated view of the current state of research on digital engineering in product development by a literature review. This includes discussing the methods of digital engineering, introducing a product development process, and presenting results classified by their individual area of application. The work concludes with an evaluation of the literature analysis results and a discussion of future research potentials.

Within the Transregional Collaborative Research Centre 73, a self-learning engineering workbench is being developed. It assists product developers in designing sheet-bulk metal formed (SBMF) parts by computing the effects of given product and process characteristics on the product properties. This contribution presents a novel approach to using deep learning methods for the properties prediction. By making use of a parameter study of 20 SBMF part designs, a metamodel is trained and used to predict the total equivalent plastic strain on local level as an indicator for part manufacturability.

Substantial efforts have been made to integrate manufacturing- and design-relevant knowledge into product development processes. A common approach is to provide the relevant knowledge to the design engineers using a knowledge-based system (KBS) that, in turn, becomes the engineering assistance system. Keeping the knowledge up to date is a critical issue, making knowledge acquisition a bottleneck of developing and maintaining KBS. This article presents a robust metamodel optimization and performance estimation architecture for developing and maintaining a KBS useful for design-for-manufacturing from the context of sheet-bulk metal forming. It is shown that the presented KBS or engineering assistance system helps achieve performing design-for-manufacturing, integrating both design and manufacturing knowledge. Using the presented approach helps overcome the bottleneck of knowledge acquisition and knowledge update through its self-learning component based on data mining and knowledge discovery.