Large-scale cyber-physical systems such as manufacturing lines generate vast amounts of data to guarantee precise control of their machinery. Visions such as the Industrial Internet of Things aim at making this data available also to computation systems outside the lines to increase productivity and product quality. However, rising amounts and complexities of data and control decisions push existing infrastructure for data transmission, storage, and processing to its limits. In this paper, we exemplarily study a fine blanking line which can produce up to 6.2 Gbit/s worth of data to showcase the extreme requirements found in modern manufacturing. We consequently propose integrated data processing which keeps inherently local and small-scale tasks close to the processes while at the same time centralizing tasks relying on more complex decision procedures and remote data sources. Our approach thus allows for both maintaining control of field-level processes and leveraging the benefits of "big data" applications.

Process force determines productivity, quality, and safety in milling. Current approaches of process design often focus on a priori optimization. In order to enable online optimization, the establishment of active force controllers is required. Due to fast-changing engagement conditions of the tool in conjunction with the slower machine dynamics, classic control is not suited. A promising approach is the application of model predictive control (MPC) for force control, which is proposed in this contribution. The model predictive force controller (MPFC) explicitly takes into account a model to predict the immediate future. It consists of a model of the machine tool and a separate model of the process. The process model describes the relation between feed velocity of the tool, force, and geometric properties of the tool, such as the radial deviation, and of the tool/workpiece engagement. The feedback loop of the controller is closed by an online identification of the process model to account changes in the material properties or of the tool wear state. For this identification an ensemble Kalman filter (EnKF) is applied. The MPFC solves an optimization problem on the future behavior in each sampling step to determine the optimal controller output enabling high dynamic control. The proposed control system is validated experimentally and compared with a conventionally designed process with constant feed. It can be shown that the manufacturing time is reduced by 50%. The system enables a paradigm shift in the design of milling processes operating the manufacturing process at its technological limit.

The wear state of the punch in sheet-metal stamping processes cannot be directly observed, necessitating the use of indirect methods to infer its condition. Past research approaches utilized a plethora of machine learning models to infer the punch wear state from suitable process signals, but have been limited by the lack of industrial-grade process setups and sample sizes as well as their insufficient interpretability. This work seeks to address these limitations by proposing the sheared surface of the scrap web as a proxy for the punch wear and modeling its quality from acoustic emission signals. The experimental work was carried out in an industrial-grade fine blanking process setting. Evaluation of the model performances suggests that the utilized regression models are capable of modeling the relationship between acoustic emission signal features and sheared surface quality of the scrap webs. Subsequent model inference suggests adhesive wear on the punch as a root cause for the sheared surface impairment of the scrap webs. This work represents the most extensive modeling effort on indirect punch wear monitoring in sheet-metal stamping both from a model prediction and model inference perspective known to the authors.

Laser technology has a rising demand for high precision Fused Silica components. Precision Glass Moulding (PGM) is a technology that can fulfil the given demands in efficiency and scalability. Due to the elevated process temperatures of almost 1400 °C and the high mechanical load, Glassy Carbon was qualified as an appropriate forming tool material for the moulding of Fused Silica. Former studies revealed that the tools’ surface finishing has an important influence on wear behaviour. This paper deals with investigation and analysis of surface preparation processes of Glassy Carbon moulds. In order to fulfil standards for high precision optics, the finishing results will be characterised by sophisticated surface description parameters used in the optics industry. Later on, the mould performance, in terms of wear resistance, is tested in extended moulding experiments. Correlations between the surface finish of the Glassy Carbon tools and their service lifetime are traced back to fundamental physical circumstances and conclusions for an optimal surface treatment are drawn.

Additive manufacturing (AM) techniques enable new design concepts for performance improvements and functional integration in a wide range of industries. One promising application is in additively manufactured cutting tools for machining, improving process reliability on the one hand and increasing tool life and process productivity on the other hand. Compared to conventional manufacturing processes, AM allows for new and complex geometrical designs, enables the production of individualized parts, and offers new possibilities for alloy composition and material design. This work gives a comprehensive and systematic review of scientific as well as industrial activities, studies, and solutions regarding AM cutting tools and their fields of application. Four different areas are identified, including cooling and coolant supply, damping and vibrational behavior, lightweight design and topology optimization, and functional integration. Thus, the relevant and promising approaches for the industrialization of AM cutting tools are highlighted, and a perspective is given on where further scientific knowledge is needed.

The quantification of the heat flow distribution in the metal cutting process depending on the cut material and the process parameters is a research area with a long history. However, a quantification of the heat flow distribution between chip, tool, and workpiece is still a not fully solved problem and remains a necessary input value for the further modeling of temperature fields and subsequent tool wear and thermal induced surface alterations, which may impair the workpiece functionality. Thus, the following publication shows the results of orthogonal cutting in order to investigate the heat flow distribution between the chip and workpiece. Therefore, the heat partitions in the cutting process were calculated by a thermodynamic methodology. This methodology considers the temperature rise in the workpiece and the chip, measured by thermography and pyrometry, as the effect of the cutting work dissipated into sensible heat. Four metals, Inconel 718, AISI 1045, Ti6Al4V, and AlMgSi0.5, were cut at varying undeformed chip thicknesses and cutting velocities. By formulating a dimensionless number for the cutting process, the Péclet number, the thermal diffusivity was included as an evaluation criterion of heat partitioning between the chip and workpiece across material properties and process settings. In this way, the validity of the Péclet number as an evaluation criterion for heat partitions in cutting and as a valuable heuristic for process design was confirmed. Another goal was to extend the state of the art approach of empirical process analysis by orthogonal cuts with regard to specific cutting forces into the thermal domain in order to provide the basis for further temperature modeling in cutting processes. The usage of the empirical data basis was finally demonstrated for the analytical modeling of temperature fields in the workpiece during milling. Therefore, the specific heat inputs into the workpiece measured in the orthogonal cuts were transferred to the milling process kinematics in order to model the heat flow into the workpiece during milling. This heat flow was used as input for an existing analytical model in order to predict stationary temperature fields in the milling process for the two-dimensional case.