A5059257840- Advanced machining processes and optimization
- Iterative Learning Control Systems
- Advanced Control Systems Optimization
- Manufacturing Process and Optimization
- Injection Molding Process and Properties
- Flexible and Reconfigurable Manufacturing Systems
- Advanced Surface Polishing Techniques
- Physics and Engineering Research Articles
- Cardiovascular Function and Risk Factors
- Fault Detection and Control Systems
- Advanced Measurement and Metrology Techniques
- Mechanical Circulatory Support Devices
- Metallurgy and Material Forming
- Robot Manipulation and Learning
- Hydraulic and Pneumatic Systems
- Seismic Performance and Analysis
- Process Optimization and Integration
- Refrigeration and Air Conditioning Technologies
- Musculoskeletal pain and rehabilitation
- Elevator Systems and Control
- Tunneling and Rock Mechanics
- Assembly Line Balancing Optimization
- Textile materials and evaluations
- Product Development and Customization
- Structural Engineering and Vibration Analysis
RWTH Aachen University
2014-2023
FH Aachen
2017
Today's manufacturers are facing numerous challenges such as highly entangled and interconnected supply chains, shortening product lifecycles growing complexity. They thus feel the need to adjust adapt faster on all levels of value creation. Self-optimization a basic principle appears promising approach handle complexity unforeseen disturbances within machines processes. Therefore it will improve resilience competitiveness manufacturing companies. This paper gives an introduction concept...
Abstract Process force determines productivity, quality, and safety in milling. Current approaches of process design often focus on a priori optimization. In order to enable online optimization, the establishment active controllers is required. Due fast-changing engagement conditions tool conjunction with slower machine dynamics, classic control not suited. A promising approach application model predictive (MPC) for control, which proposed this contribution. The controller (MPFC) explicitly...
The consideration of the pvT-behaviour (pressure, specific volume and temperature) plastic material in combination with closed-loop cavity pressure control allows for compensation variable boundary conditions injection moulding process.By suitably implementing control, repeatability product quality processes can be improved. However, there are still obstacles industrial application. As process behaviour is greatly dependent on mould – which interchangeable typically designed manufactured...
Milling is one of the most flexible and productive manufacturing processes for machining metals. In case rough milling as much material possible should be removed in little time possible. Therefore, a high cutting force desirable. The maximum thus suitable control variable to reduce time. related feed velocity. relationship can described by models. They turn used determine velocity given force. This reference, which shall not exceeded. Hence, Model-based Predictive Controller (MPC)...
Deep process and machine knowledge is necessary for setting up conventional manufacturing systems. Hence, the degree of automation shall be increased to ensure high productivity flexibility. An essential element this goal systematic establishment additional control loops, e. g. in form machine-oriented loops or higher loops. In following a method presented decouple from For Model-based Predictive Controller (MPC) used predict future behavior. Based on prediction MPC adapts reference with...
Today's manufacturing systems are either optimized for flexible or individualized manufacturing. The machine operator determines the optimal setup variables that accurately implemented by controllers. However, overall objective is productivity under restriction of product quality, where a model-based predictive controller used to rather control process than settings. This approach requires an accurate model dynamic behavior tool. Therefore, Support Vector Machines algorithm applied identify...
Manufacturing complex products, process monitoring and control systems can increase productivity. Today, improvements in simulation technique enable computer based set up optimization reducing expensive trials. The performance of offline strongly depends on the selected model does not include uncertainties such as to tool wear or deviating material properties. Sensors help reduce thus stability. Milling is one most flexible metal cutting processes. force a very important evaluating...
Plastic injection molding is characterized by high design flexibility of the manufactured parts. Consequently, it one most important processes for mass production plastic The setup manufacturing process very complex due to numerous impact factors. In addition, material fluctuations or changing ambient conditions require adaption during guarantee a constant product quality. order reduce effort and control quality, concept model-based self-optimization applied molding. Therefore, Norm-Optimal...
Due to the lack of donor organs, importance left ventricular assist devices (LVADs) increases. State art is operate pumps with a constant speed (CS) leading effects such as underpumping, suction or backflow blood from aorta in ventricle. The end-diastolic volume (EDV) influenced by venous return, well diastolic function and systolic pressure development. Thus it good medical indicator load. In this paper norm-optimal iterative learning control (NOILC) algorithm designed shape EDV...
The injection molding of thermoplastics is one the most efficient manufacturing processes for autonomous plastic parts. process commonly divided into two main phases: phase and packing phase. During velocity conventionally controlled. In contrast, pressure Due to different control objectives within these phases strategy changed regarding a switch-over point, which leads issues in control. Therefore, cross-phase required avoids order optimize strategy. this contribution, model-based...
Milling is a manufacturing process for machining metals, where milling tool cuts metal from workpiece. Especially during rough the productivity significantly affected by force that acts on tool. The depends feed velocity and engagement conditions. latter are defined path takes through metal. Consequently, of cutter suitable manipulated variable. A model describes relation between force, conditions velocity. separate machine behavior axis its numerical controller. Based these two models...
Mechanistic force models are popular to describe the in cutting technology. Process simulation, process optimization, and control rely on accuracy of these models. Standard identification techniques not capable identifying a mechanistic model on-line hard real-time. However, it is necessary adjust increasing tool wear, e.g. predictive controller for milling. This work introduces ensemble Kalman filter field technology - enabling first time continuous parametrization The approach shows high...
The extensive effort in manual labour for mould manufacturing is the main reason outsourcing resources to low-wage countries. Furthermore, international and die sector faces a lack of skilled workers due unattractive working conditions, example stressing, monotonous work partly hazardous atmosphere (e.g. nickel dust). Especially, finishing process grinding, lapping, polishing) freeform surfaces still less automated metal processing. Therefore, many efforts are initiated order fully automate...
A semi-active tuned liquid column damper (S-TLCD) is proposed, which can tune both the natural frequency and damping ratio autonomously. The S-TLCD reaches a robust high-level performance by adapting its parameters to changing loading structural conditions. Degradation effects, soil-structure interaction environmental variations cause such changes. This paper summarizes results of numerical experimental investigations on damper. For analyses, seismically excited 20-story benchmark building...
Kurzfassung Die Zerspankraft liefert in spanenden Fertigungsverfahren eine wichtige Information über den Prozesszustand. Trotz optimal ausgelegter Prozesse wird selten konstante erreicht, da Unsicherheiten, wie z. B. Werkzeugverschleiß oder Materialkenngrößen, diese beeinflussen. Aus Schutz vor Überlast, werden die Prozessparameter so gewählt, dass vom Werkzeug ertragbare Last auch zum Standzeitende nicht überschritten wird. Damit einher geht ein Produktivitätsverlust bei schneidscharfem...
Support Vector Machines (SVM) is a machine learning algorithm with inherent generalization ability and convex optimization problem. This paper studies the application of SVM method for online identification nonlinear dynamic behavior feed velocity in CNC machining center. Both blackbox greybox modeling approaches are tested this purpose. Within German Cluster Excellence "Integrative Production Technology High-Wage Countries", modelbased predictive control (MPC) strategy linear state-space...