Stochastic Kinematic Process Model with an Implemented Wear Model for High Feed Dry Grinding

This paper considers heavy duty grinding with resin bonded corundum grinding wheels and without lubrication and cooling. A vertical turning machine redesigned to a grinding machine test bench with a power controlled grinding spindle is used in all of the experiments, allowing high tangential table feed rates up to 480 m/min. This special test-rig emulates the railway grinding usually done by a railway grinding train. The main test-rig components are presented and the resulting kinematics of the experimental set-up is described. A stochastic kinematic grinding model is presented. A wear model that is based on the kinematic description of the grinding process is set up. Grain breakage is identified as the main wear phenomenon, initiated by grain flattening and micro-splintering. The wear model is implemented into the stochastic kinematic modelling. The workpiece material side flow and spring back are considered. The simulation results are validated experimentally. The workpiece surface roughness is compared and a good agreement between simulation and experiment can be found, where the deviation between the experiment and the simulation is less than 15% for single-sided contact between the grinding wheel and the workpiece. Higher deviations between simulation and experiment, up to 24%, for double-sided contact is observed

Physics-informed Bayesian machine learning for probabilistic inference and refinement of milling stability predictions

This paper proposes an industrial-friendly approach based on physics-informed Bayesian machine learning for probabilistic inference and refinement of stability predictions in milling. The knowledge from physics principles and theoretical models is gathered into a substructure-based framework and enables dedicated updating and exchange of models on a substructure level. The Bayesian learning algorithm utilizes this framework as a basis to simultaneously interact with multiple machine and process configurations that share substructures, leading to federated and transfer learning over a network of machine tools on a production site. Moreover, the paper further elaborates on the Bayesian perspective to anticipate the potential usefulness of given experimental data in improving the model accuracy by quantifying information gain for two primary purposes: Firstly, recursive online learning can manage computational resources by processing only the most informative data points that may be collected during arbitrary cuts. Secondly, active learning leverages the information gain to dynamically adapt a sequential data collection, leading to accelerated learning with minimal labeled data. The experimental validations confirm that the proposed approaches lead to effective learning and reliable predictions of milling stability, outperforming traditional deterministic methods.ISSN:1755-5817ISSN:1878-001

Geometric-kinematic model for reinforced concrete core drilling

Since diamond-impregnated tools have a big share on tools for construction, the development of more efficient ones is necessary in the near future. The drilling speed performance is one of the concerns during the development of new diamond-impregnated segments in concrete core drilling. However, observations only based on experiments can be very costly, considering a quite demanding amount of material and workforce. One of the solutions to overcome these issues is the development of a geometric-kinematic model (containing a kinematic process model, material model, and drilling force model) to simulate the tangential and normal forces of diamond-impregnated segments in core drilling process. Such model is regularly used in the literature for the simulation of a bonded-abrasives process. The main purpose of the geometric-kinematic model is to generate simulations for the concrete core drilling for the tool performance estimation. This allows an evaluation of the geometric characteristics' (diamond morphology, orientation, size, and positioning) impact of diamond-impregnated segments and of drilling parameters on the drilling force. The geometric-kinematic model will use detailed segment descriptions with stochastic and deterministic variables. The drilling simulation delivers a consistent drilling forces prediction in Utliberg concrete and ASTM A615 steel at the frequently used core drilling feed window. Besides, the model shows the competence to evaluate the impact of a diamond layer characteristics (diamond size and number of rows), allowing the comparison of distinct diamond arrangements designs between each other.ISSN:0268-3768ISSN:1433-301

Automated evaluation of continuous and segmented chip geometries based on image processing methods and a convolutional neural network

The aim of this work is to present a new methodology for the automated analysis of the cross-sections of experimental chip shapes. It enables, based on image processing methods, the determination of average chip thicknesses, chip curling radii and for segmented chips the extraction of chip segmentation lengths, as well as minimum and maximum chip thicknesses. To automatically decide whether a chip at hand should be evaluated using the proposed methods for continuous or segmented chips, a convolutional neural network is proposed, which is trained using supervised learning with available images from embedded chip cross-sections. Data from manual measurements are used for comparison and validation purposes

Automatic Detection of the Running Surface of Railway Tracks Based on Laser Profilometer Data and Supervised Machine Learning

The measurement of the longitudinal rail profile is relevant to the condition monitoring of the rail infrastructure. The running surface is recognizable as a shiny metallic area on top of the rail head. The detection of the running surface is crucial for vehicle-based rail profile measurements, as well as for defect detection. This paper presents a methodology for the automatic detection of the running surface based on a laser profilometer. The detection of the running surface is performed based on the light reflected from the rail surface. Three rail surfaces with different surface conditions are considered. Supervised machine learning is applied to classify individual surface elements as part of the running surface. Detection by a linear support vector machine is performed with accuracy of >90%. The lateral position of the running surface and its width are calculated. The average deviation from the labeled widths varies between −1.2mm and 5.6mm. The proposed measurement approach could be installed on a train for the future onboard monitoring of the rail network

Vorhersage der Werkstück-Oberflächenrauheit beim Hochleistungs-Trockenschleifen mittels Simulation

Topographie und Eigenschaften der Schleifwerkzeuge beeinflussen das Schleifergebnis massgeblich. Die richtige Werkzeugauswahl stellt in der Praxis oft einen iterativen experimentellen Prozess dar. In diesem Beitrag wird ein geometrisches Simulationstool namens iBRUS, institutseigene Entwicklung, vorgestellt, das fähig ist, die Oberflächenrauheit des Werkstücks für verschiedene Schleifwerkzeuge vorherzusagen und so die Werkzeugauswahl zu beschleunigen und zu verbessern. In einem Hochleistungs Trockenschleifprozess im Seiten-Planschleifen wird unter Berücksichtigung von Schleifwerkzeugen verschiedener Korngrössen und Kornarten die Anwendung illustriert. Die Validierung bezieht die Oberflächentopographie von Werkzeug und Werkstück ein. Bei der Validierung der Simulation mit experimentellen Prozessdaten kann gezeigt werden, dass geometrische Simulationstools die effektive Vorhersage der Werkstück-Oberflächenrauheit ermöglichen und erweitert werden können, um optimale Prozessparameter für die gewünschte Oberflächenqualität zu finden

Single crater dimensions and wire diameter influence on Wire-EDM

In Wire Electrical Discharge Machining (WEDM) the erosion process is based on the superposition of craters. The plasma channel and the craters’ shape have an impact on the process and on the characteristics of the machined surface. This impact goes from the cutting speed to the surface roughness passing through workpiece temperature gradient during the spark. Several models simulate the crater formation through thermal analysis, yielding symmetrical shapes. Commonly non-homogeneous and random aspects in the dielectric and the electrode geometry are neglected. Such features introduce an asymmetric aspect to the crater shape. To investigate the influence of the tool electrode geometry on the craters’ shape, single craters on steel are produced at different pulse energy levels and with three different wire diameters 0.20mm, 0.25mm and 0.30mm. An optical microscope is used to map the single craters’ topography and subsequently extract their dimensions. The single craters’ lengths and aspect ratio are analyzed. In the lower energy range the random aspects dominate the crater shape, the craters are smaller and rounder. Higher energy pulses create consistently bigger and elongated craters. The aspect ratio depends on the wire geometry, thinner wires produce more elongated craters. This behavior reinforces the idea that from a certain energy level on the tool geometry effects overcome the random aspect of the process, generating elongated craters.ISSN:2212-827