For their unique properties, titanium alloys have found wide application in high-tech engineering. But these alloys are difficult to machine and to grind for their high chemical reactivity and poor thermal properties, which aggravate the grinding zone temperature and its detrimental effects. The objective of this article is a comparison of the grindability of Ti-6Al-4V regarding cubic boron nitride (CBN) and diamond brazed type monolayered grinding wheels under the influence of different environments. In grinding this alloy, cryogenic cooling did not help visibly for both CBN and diamond, but the application of oil and also of alkaline coolant significantly gave the best results.

Due to the inhomogeneous material structure, micromachining of fibre reinforced plastics places high requirements on machining processes. Using the hole drilling machining of carbon fibre reinforced plastics as an example, experiments on electro discharge machinability were carried out by means of solid carbide electrodes. Basing on the variation of technological process parameters, the tests were conducted with regard to both workpiece-related and tool-related parameters.The results accentuate the complexity of the influencing parameters affecting surface quality and thermal damage of the mater ial as well as tool wear. However, evidence of a low damage production of small drill holes could be provided

This paper presents a brief introduction to competition-driven digital transformation in the machining sector. On this basis, the creation of a digital twin for machining processes is approached firstly using a basic digital twin structure. The latter is sub-grouped into information and data models, specific calculation and process models, all seen from an application-oriented perspective. Moreover, digital shadow and digital twin are embedded in this framework, being discussed in the context of a state-of-the-art literature review. The main part of this paper addresses models for machine and path inaccuracies, material removal and tool engagement, cutting force, process stability, thermal behavior, workpiece and surface properties. Furthermore, these models are superimposed towards an integral digital twin. In addition, the overall context is expanded towards an integral software architecture of a digital twin providing information system. The information system, in turn, ties in with existing forward-oriented planning from operational practice, leading to a significant expansion of the initially presented basic structure for a digital twin. Consequently, a time-stratified data layer platform is introduced to prepare for the resulting shadow-twin transformation loop. Finally, subtasks are defined to assure functional interfaces, model integrability and feedback measures.

Increasing digitalization enables the use of machine learning (ML) methods for analyzing and optimizing manufacturing processes. A main application of ML is the construction of quality prediction models, which can be used, among other things, for documentation purposes, as assistance systems for process operators, or for adaptive process control. The quality of such ML models typically strongly depends on the amount and the quality of data used for training. In manufacturing, the size of available datasets before start of production (SOP) is often limited. In contrast to data, expert knowledge commonly is available in manufacturing. Therefore, this study introduces a general methodology for building quality prediction models with ML methods on small datasets by integrating shape expert knowledge, that is, prior knowledge about the shape of the input–output relationship to be learned. The proposed methodology is applied to a brushing process with 125 data points for predicting the surface roughness as a function of five process variables. As opposed to conventional ML methods for small datasets, the proposed methodology produces prediction models that strictly comply with all the expert knowledge specified by the involved process specialists. In particular, the direct involvement of process experts in the training of the models leads to a very clear interpretation and, by extension, to a high acceptance of the models. While working out the shape knowledge requires some iterations in general, another clear merit of the proposed methodology is that, in contrast to most conventional ML, it involves no time-consuming and often heuristic hyperparameter tuning or model selection step.