A5090885489- Advanced machining processes and optimization
- Advanced Surface Polishing Techniques
- Manufacturing Process and Optimization
- Engineering Technology and Methodologies
- Tunneling and Rock Mechanics
- Additive Manufacturing and 3D Printing Technologies
- Advanced Machining and Optimization Techniques
- Additive Manufacturing Materials and Processes
- Advanced Measurement and Metrology Techniques
- Flexible and Reconfigurable Manufacturing Systems
- Injection Molding Process and Properties
- Tribology and Lubrication Engineering
- Industrial Vision Systems and Defect Detection
- Iterative Learning Control Systems
- Engineering and Materials Science Studies
- Welding Techniques and Residual Stresses
- Metal Alloys Wear and Properties
- Metal Forming Simulation Techniques
- Drilling and Well Engineering
- Innovations in Concrete and Construction Materials
- Structural Health Monitoring Techniques
- Civil and Structural Engineering Research
- Augmented Reality Applications
- Hydraulic and Pneumatic Systems
- Digital Transformation in Industry
University of Stuttgart
2017-2025
Fraunhofer Institute for Machine Tools and Forming Technology
2020-2022
Otto-von-Guericke University Magdeburg
2012-2022
University of Kaiserslautern
2022
Facing Our Risk of Cancer Empowered
2020
Walter de Gruyter (Germany)
2020
Leibniz University Hannover
2007-2013
Inspired by the natural intelligence of humans and bio-evolution, Artificial Intelligence (AI) has seen accelerated growth since beginning 21st century. Successful AI applications have been broadly reported, with Industry 4.0 providing a thematic platform for AI-related research development in manufacturing. This paper highlights manufacturing, ranging from production system design planning to process modeling, optimization, quality assurance, maintenance, automated assembly disassembly. In...
The tool lifetime and the material removal rate significantly influence production costs of a machined component. In order to maintain process reliability, tools are often changed too early in industrial practice, which is waste resources results increased extended setup times. Tool condition monitoring systems based on machine learning have shown great potential detect predict wear, thereby reducing risk failure optimising change intervals. Nevertheless, common supervised methods for wear...
Fixtures are an essential element of the machining system, being part precision path and force flux between process machine tool. Intelligent fixtures enable identification critical conditions, a compensation error influences minimization defective parts. At first, this contribution presents study in which influence clamping setup workpiece characteristics at various steps is analyzed. Experimental theoretical results regarding dynamic behavior reveal relevance these with respect to...
The Johnson–Cook constitutive equation is very widely used for simulating cutting processes. Different methods are applied establishing parameters of the equation. Based on analysed in this study, two algorithms were worked out to determine prevailing conditions during In first algorithm, all established simultaneously with standardized test methods. second separately accordance machining developed methodology was verified AISI 1045 heat-treatable steel and Ti10V2Fe3Al (Ti-1023) titanium...
The thermomechanical interaction of the tool with chip in most loaded secondary cutting zone depends on contact length rake face chip. Experimental studies dependency speed, undeformed thickness, and angle, performed by optical method, are used for comparison obtained FE modeling orthogonal process. To determine parameters constitutive Johnson-Cook equation, which serves as a material model that has predominant influence length, software-implemented algorithm was developed. This is based...
Cutting simulations via the Finite Element Method (FEM) have recently gained more significance due to ever increasing computational performance and thus better resulting accuracy. However, these are still time consuming therefore cannot be deployed for an in situ evaluation of machining processes industrial environment. This is high non-linear nature FEM processes, which require considerable resources. On other hand, machine learning methods known capture complex behaviors. One most widely...
Abstract In the manufacturing industries, noise is one of most common health hazards at workplaces. wood machining, for instance, circular sawing processes in particular produce high emissions that often exceed permitted limits. The main source rotating saw blade, whose aeroacoustic behavior influenced by air turbulence on tool contour. So far, no numerical approach to study and optimize from blades has been investigated. This paper addresses this deficit presents a methodology modeling...
The increasing volatility of the markets and growing demand for customized products are challenges future production to ensure maximum flexibility adaptability. This paper introduces software-defined value stream process systems (SVPSs), a novel approach that extends concept manufacturing into autonomous, reconfigurable systems. SVPSs establish cyber–physical chain links product features requirements, enabling their fulfillment through modular machine hardware. A construction kit...
Abstract In this study, AI-supported anomaly detection methods in the milling of inhomogeneous sample materials are investigated. To simplify data generation, targeted boreholes were introduced into homogeneous material samples. Process collected by means acceleration measurements on both workpiece and tool sides force side. Implementing feature extraction applying feature-based machine learning algorithms achieved precise classification reliable differentiation between drilled undrilled...