The main driver of industry 4.0 is data and information for realizing resilient manufacturing systems through data-based decision-making. This requires the data flow from different machinery and its processing. Therefore, a methodical survey on the logical and technical requirements for enabling an efficient data flow can be beneficial since it fastens the process of establishing the data flow and gives production and software engineers a common understanding of the data’s meaning. Hence, data, related metadata like the unit or the frequency of recordings, and technical information, e.g., the machine’s interface or the communication protocol, are necessary. Based on the classical value stream mapping (VSM) as a lean management tool, the first approach for a methodical gathering of data, value stream mapping 4.0, was established in 2016. Since VSM 4.0 provides technical properties for specifying the data flow, some shortcomings were identified, particularly related to technical information. This contribution introduces the VSM 4.0 plus that solved the identified shortcomings by adding new properties. Compared to VSM 4.0, VSM 4.0 plus offers detailed specifications of data points, includes technical information, e.g., used communication protocols, adds information regarding data processing, and enhances the storage and usage dimension by providing more details. Through the added properties, VSM 4.0 plus allows domain and software engineers more efficient collaboration since all required information for establishing the data flow and its processing are united in one methodology. To verify the practicability, the VSM 4.0 plus was applied within several manufacturing-related use cases.

The unit process life cycle inventory is a modeling approach to estimate the energy demand and resource requirements of a unit process. Thus, a model of the unit process life cycle inventory for a specific manufacturing process can be used for quantifying the environmental impacts of specific products manufactured by that process. Within the approach, reusable models are developed for specific manufacturing processes. In this paper, the development and validation of a unit process life cycle inventory model for high-speed laser directed energy deposition is presented. This additive manufacturing process offers great potential for the industry due to its fast process speed. However, high-speed laser directed energy deposition has a high energy consumption and resource demand. Assessing the energy demand for individual manufactured products is a time-intensive process that requires expert knowledge. Thus, the development of an adaptable unit process life cycle inventory model enables more convenient assessment and improvement of its energy and resource efficiencies for producing different products. For the development of the model, the subsystems of a high-speed laser directed energy deposition machine are analyzed separately, e. g. the laser generator, the trajectory system, the powder feeder, and the suction system. Afterward, the energy and material demand of these subsystems are described in mathematical models. Finally, the model is validated by comparing the energy demands of three demonstration parts, measured by experiments, and predicted by the model.

Forgings are produced in several process steps, the so-called forging sequence. The design of efficient forging sequences is a very complex and iterative development process. In order to automate this process and to reduce the development time, a method is presented here, which automatically generates multi-stage forging sequences for different forging geometries on the basis of the component geometry (STL file). The method was developed for closed die forging. The individual modules of this forging sequence design method (FSD method) as well as the functioning of the algorithm for the generation of the intermediate forms are presented. The method is applied to different forgings with different geometrical characteristics. The generated forging sequences are checked with FE simulations for the quality criteria form filling and freedom from folds. The simulation results show that the developed FSD method provides good approximate solutions for an initial design of forging sequences for closed die forging in a short time.

Due to an excellent ratio of high strength to low density, as well as a strong corrosion resistance, the titanium alloy Ti-6Al-4 V is widely used in industrial applications. However, Ti-6Al-4 V is also a difficult-to-cut material because of its low thermal conductivity and high chemical reactivity, especially at elevated temperatures. As a result, machining Ti-6Al-4 V is characterized by high thermal loads and a rapidly progressing thermo-chemical induced tool wear. An adequate cooling strategy is essential to reduce the thermal load and therefore tool wear. Sub-zero metalworking fluids (MWF) which are applied at liquid state but at supply temperatures below the ambient temperature, offer great potential to significantly reduce the thermal load when machining Ti-6Al-4 V. Within the presented research, systematically varied sub-zero cooling strategies are applied when milling Ti-6Al-4 V. The influences of the supply temperature, as well as the volume flow and the outlet velocity are investigated aiming at a reduction of the thermal loads that occur during milling. The milling experiments were recorded using high-speed cameras in order to characterize the impact of the cooling strategies and resolve the behavior of the MWF. Additionally, the novel sub-zero cooling approach is compared to a cryogenic CO2 cooling strategy. The results show that the optimized sub-zero cooling strategy led to a sufficient reduction of the thermal loads and does outperform the cryogenic cooling even at elevated CO2 mass flows.

The application of modern edge computing solutions within machine tools increasingly empowers the recording and further processing of internal data streams. The datasets derived by contextualized data acquisition form the basis for the development of novel data-driven approaches for quality monitoring. Nevertheless, for the desired data-driven modeling and data handling, heavily specialized human resources are required. Additionally, domain experts are indispensable for adequate data preparation. To reduce the manual effort regarding data analysis and modeling this paper presents a new approach for an automated parametrization of an end-to-end machine learning pipeline (MLPL) to develop and select the best-performing quality prediction models for usage in machining production. This supports domain experts with a lack of specific knowledge of data science to develop well-performing models for machine learning-based quality prediction of milled workpieces. The results show that the presented algorithm enables the automated generation of data-driven models at high prediction performances to use for quality monitoring systems. The algorithm’s performance is tested and evaluated on four real-world datasets to ensure transferability.

The Automated Fibre Placement process is commonly used in aerospace for the manufacturing of structural components, but requires a subsequent inspection to meet the corresponding safety requirements. In order to improve this mostly manual inspection step, machine learning methods for the interpretation of 2D surface images are being increasingly utilised in research. Depending on the manufacturing process, a very long time can elapse between the appearance of a manufacturing defect and its recognition. Hence, in this paper Convolutional and Recurrent Neural Network techniques are presented that allow a line-by-line analysis of the incoming height profile scans of a Laser Line Scan Sensor as a 1D signal, which enables a direct reaction to a defect, even if only one or a few individual height profiles of the defect have been recorded. The combination of Convolutional and Recurrent Neural Network structures is particularly beneficial for this application. The investigations in this paper are especially interesting for developers of automated inspection systems in composite engineering.

TiAl6V4 allows significant performance improvements in industrial applications. However, the machining is a considerable challenge due to low thermal conductivity, Young’s modulus and strong adhesion tendency. This leads to high thermal and mechanical loads on the cutting edge resulting in early tool failure. Today, uncoated cemented carbide tools are commonly used. However, temperature active physical vapor deposition (PVD) coatings like CrAlVN and CrAlMoN provide a promising approach to increase tool life. For this, the coating’s ability to form lubricating oxide phases is vital. In the present study, CrAlVN and CrAlMoN coatings were investigated to determine the more suitable version for increasing the economic efficiency of machining TiAl6V4. The coatings were deposited by hybrid direct current magnetron sputtering/high power pulsed magnetron sputtering (dcMS/HPPMS) processes. Morphology, thickness, chemical composition, indentation hardness, indentation modulus and oxide phase composition were analyzed. Moreover, friction and wear behavior of the coated tools were determined using a pin on disc (PoD) tribometer at various temperatures. Additionally, tool life and deformation behavior were analyzed after turning TiAl6V4. The CrAlVN and CrAlMoN coatings show a dense morphology and a smooth surface topography. Both variants have a good adhesion to the cemented carbide tools. For increased temperatures, the tribological analyses showed a reduction in the coefficient of friction. In case of CrAlMoN coated samples, a friction reduction compared to CrAlVN was observed at lower temperatures. Due to this, an increased tool life was achieved for CrAlMoN coated cutting inserts during turning of TiAl6V4.

In this contribution an analytical approach for estimating the tapping tool’s instantaneous eigenfrequencies of flexural modes is derived. A sensor-integrated tap holder with a close-to-tool vibration sensor attached on the tapping tool is introduced and verified by means of frequency response analysis. The close-to-tool vibration data measured during thread cutting experiments is analyzed in time and frequency domain. The instantaneous eigenfrequencies observed in the spectrogram of the power spectral density are compared with the analytical estimation results. It could be shown that considering for the analytical estimation approach the tapping tool-workpiece contact as clamped boundary condition shows close accordance to the experimental data.

Driven by an increasing demand for individualized products and shorter product life-cycles, companies continuously extend their product portfolio. Simultaneously, companies expand into new markets to reach customers and to exploit varying location factors to reduce costs. Global production networks (GPNs) have to be adapted constantly to react to new circumstances and changes in the demand of products. To remain competitive, product allocation and production network configuration are essential. At the same time, companies face an increasing complexity while handling these tasks. This poses a challenge particularly for small and medium sized companies, which have limited planning capacities and management resources. Current literature describes optimization-based approaches for the integrated product allocation and network configuration of production networks. Yet, multi-objective models lack transparency of results and user friendliness. Therefore, this paper presents a multi-objective optimization model that incorporates flexibility and reconfiguration aspects to determine an optimal product allocation and network configuration of a GPN over a given planning horizon. The preemptive goal programming approach is used to identify Pareto-optimal solutions and to increase user friendliness. The subsequent verification, validation and post-optimality analysis combined in a structured process enables a wide range of companies to apply the approach. The model is successfully applied in the GPN of a special machine manufacturer, which produces high precision metrology machines. Due to its transparent approach for complex planning problems, the developed method provides a solid base for well-founded, objective decisions. Hence, the risk of costly errors in the planning phase is reduced.