In-Process UV-Curing of Pasty Ceramic Composite

Within recent years,a wide range of additive manufacturing processes have been developed. While powder bed based fusion processes like selective laser melting and melting processes like fused layer modelling are being increasingly used in industrial applications, prototyping other processes are in the initial stage. This paper develops a new method for an extrusion-based process of pasty UV-curing ceramic composite material. The method proposes an approach to continuously cure the material while it is deployed to reduce process time and generate complete cured parts. A milling machine has been modified with a syringe and a UV-light source to accommodate the process. Experimental studies have been carried out to examine the influence of the process parameters on the curing process. As a result, a parameter set has been found to make fully dense and cured ceramic composite parts.Mechanical Engineerin

A contribution to the dynamics of the tapping process: analytically estimated and measured instantaneous eigenfrequencies of the tapping tool

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

Hybrid compliance compensation for path accuracy enhancement in robot machining

Robot machining processes with high material removal rates lack of high path accuracy mainly due to the low stiffness of industrial robots. The low stiffness leads to process forces caused deviations of the tool center point (TCP) from the planned position of more than 1 mm in industrial applications. To enhance the path accuracy a novel hybrid compliance compensation is developed. It combines a force sensor and model based online compensation with forces of an offline simulation to instantly react to predictable high force changes e.g. at a milling cutter exit from the work piece. The method is applied to a KUKA KR 300 robot. A compliance model based on a forward kinematic with virtual joints is implemented on an external controller. Cartesian or axis specific compensation values are calculated and transferred to the robot via a control circuit. A compliance measurement method is developed and a force torque sensor is mounted to the flange of the robot. The system is validated in with Cartesian and axis specific compensation values as well as with and without pilot control

Data-based energy performance root cause analysis methodology

The automotive industry aims to achieve continual improvement in energy performance to reduce emissions and costs. High complexity in manufacturing systems makes comprehensive analyses economically infeasible thus a root cause analysis (RCA) methodology is required to identify and quantify subsystems with a high energy saving potential. This allows targeted analyses and measures to be derived. For this purpose, a data-based benchmarking approach for identification and quantification of energy saving potential is developed. The methodology has proven to be suitable to derive significant energy savings in the validation based on a real use case of a globally operating car manufacturer.</p

Quality prediction for milling processes: automated parametrization of an end-to-end machine learning pipeline

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

Automation architecture for harnessing the demand response potential of aqueous parts cleaning machines

To reduce global greenhouse gas emissions, numerous new renewable power plants are installed and integrated in the power grid. Due to the volatile generation of renewable power plants large storage capacity has to be installed and electrical consumer must adapt to periods with more or less electrical generation. Industry, as one of the largest global consumers of electrical energy, can help by adjusting its electricity consumption to renewable production (demand response). Industrial aqueous parts cleaning machines offer a great potential for demand response as they often have inherent energy storage potential and their process can be adapted for energy-flexible operation. Therefore, this paper presents a method for implementing demand response measures to aqueous parts cleaning machines. We first determine the potential for shifting electrical consumption. Then, we adapt the automation program of the machine so that submodules and process steps with high potential can be energy-flexibly controlled. We apply the method to an aqueous parts cleaning machine in batch process at the ETA Research Factory