Abstract:Monitoring of the relative deviation between commanded and actual tool tip position, which limits the volumetric performance of the machine tool, enables the use of contemporary methods of compensation to reduce tolerance mismatch and the uncertainties of on-machine measurements. The development of a primarily optical sensor setup capable of being integrated into the machine structure without limiting its operating range is presented. The use of a frequencymodulating interferometer and photosensitive arrays in combination with a Gaussian laser beam allows for fast and automated online measurements of the axes' motion errors and thermal conditions with comparable accuracy, lower cost, and smaller dimensions as compared to state-of-the-art optical measuring instruments for offline machine tool calibration. The development is tested through simulation of the sensor setup based on raytracing and Monte-Carlo techniques.
Production of low-quality or faulty products is costly for manufacturing companies since it wastes a lot of resources, human effort, and time. Avoiding such waste requires the correct set of process control parameters, which depends on the dynamic situation in the production processes. Research so far mainly focused on optimizing specific processes using traditional optimization algorithms, mainly evolutionary algorithms. To develop a framework that enables real-time optimization based on a predictive model for an arbitrary production process, this paper explores the application of reinforcement learning (RL) in the field of process parameter optimization. Inspired by the literature review on both, production process parameter optimization, and RL, a model based on maximum a posteriori policy optimization that can handle both numerical and categorical parameters is proposed. A validation study conducted on data sets from production fields compares the trained model to state–of–the–art traditional optimization algorithms and shows that RL can find optima of similar quality while requiring significantly less time.
Geometriemessungen auf Werkzeugmaschinen sind immer häufiger gefragt. Insbesondere bei der Großbauteileproduktion bieten sie ein hohes Potential, um Kosten und Zeit zu sparen. Anders als Koordinatenmessgeräte sind Werkzeugmaschinen rauen Umgebungsbedingungen von Produktionshallen ausgesetzt, welche die Messungen deutlich beeinflussen können. Der Fachbericht befasst sich mit einem Vorgehen, um umweltbedingte Störeinflüsse und die damit induzierten Messunsicherheiten zu bestimmen und zu reduzieren.
Geometric measurements on machine tools are getting more and more important for the production of large parts due to the big cost and time saving potential. Unlike coordinate measuring machines, machine tools are exposed to rough shop floor conditions generating large measurement uncertainties. The article describes a procedure to define and reduce these disturbances and the specific measurement uncertainties.
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