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View article: Model-Based Controlling Approaches for Manufacturing Processes
Model-Based Controlling Approaches for Manufacturing Processes Open
The main objectives in production technology are quality assurance, cost reduction, and guaranteed process safety and stability. Digital shadows enable a more comprehensive understanding and monitoring of processes on shop floor level. Thu…
View article: Model-Based Controlling Approaches for Manufacturing Processes
Model-Based Controlling Approaches for Manufacturing Processes Open
The main objectives in production technology are quality assurance, cost reduction, and guaranteed process safety and stability. Digital shadows enable a more comprehensive understanding and monitoring of processes on shop floor level. Thu…
View article: Model-Based Controlling Approaches for Manufacturing Processes
Model-Based Controlling Approaches for Manufacturing Processes Open
The main objectives in production technology are quality assurance, cost reduction, and guaranteed process safety and stability. Digital shadows enable a more comprehensive understanding and monitoring of processes on shop floor level. Thu…
View article: Model predictive force control in milling based on an ensemble Kalman filter
Model predictive force control in milling based on an ensemble Kalman filter Open
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 of active force controllers is requ…
View article: Robust Parametrization of a Model Predictive Controller for a CNC\n Machining Center Using Bayesian Optimization
Robust Parametrization of a Model Predictive Controller for a CNC\n Machining Center Using Bayesian Optimization Open
Control algorithms such as model predictive control (MPC) and state\nestimators rely on a number of different parameters. The performance of the\nclosed loop usually depends on the correct setting of these parameters. Tuning\nis often done…
View article: Identifying trending coefficients with an ensemble Kalman filter
Identifying trending coefficients with an ensemble Kalman filter Open
This paper extends the ensemble Kalman filter (EnKF) for inverse problems to identify trending model coefficients. This is done by repeatedly inflating the ensemble while maintaining the mean of the particles. As a benchmark serves a class…
View article: Quality Control in Injection Molding based on Norm-optimal Iterative Learning Cavity Pressure Control
Quality Control in Injection Molding based on Norm-optimal Iterative Learning Cavity Pressure Control Open
Plastic injection molding is characterized by high design flexibility of the manufactured parts. Consequently, it is one of the most important processes for mass production of plastic parts. The setup of the manufacturing process is very c…
View article: Robust Parametrization of a Model Predictive Controller for a CNC Machining Center Using Bayesian Optimization
Robust Parametrization of a Model Predictive Controller for a CNC Machining Center Using Bayesian Optimization Open
Control algorithms such as model predictive control (MPC) and state estimators rely on a number of different parameters. The performance of the closed loop usually depends on the correct setting of these parameters. Tuning is often done ma…
View article: Identifying trending model coefficients with an ensemble Kalman filter – a demonstration on a force model for milling
Identifying trending model coefficients with an ensemble Kalman filter – a demonstration on a force model for milling Open
This paper extends the ensemble Kalman filter (EnKF) for inverse problems to identify trending model coefficients. This is done by repeatedly inflating the ensemble while maintaining the mean of the particles. As a benchmark serves a class…
View article: Ensemble Kalman filtering for force model identification in milling
Ensemble Kalman filtering for force model identification in milling Open
Mechanistic force models are popular to describe the force in cutting technology. Process simulation, process optimization, and process control rely on the accuracy of these models. Standard identification techniques are not capable of ide…
View article: Model Predictive Control in Milling based on Support Vector Machines
Model Predictive Control in Milling based on Support Vector Machines Open
Today's manufacturing systems are either optimized for flexible or individualized manufacturing. The machine operator determines the optimal setup for the machine variables that are accurately implemented by the machine controllers. Howeve…
View article: Continuous identification for mechanistic force models in milling
Continuous identification for mechanistic force models in milling Open
Force determines the product quality, the productivity and the safety of a milling process. Mechanistic force models are the key to understand, optimize or control the cutting process. They combine the undeformed chip parameter with empiri…
View article: Kernel Selection for Support Vector Machines for System Identification of a CNC Machining Center
Kernel Selection for Support Vector Machines for System Identification of a CNC Machining Center Open
Advanced learning methods enable the model-based control of systems with complex unknown dependencies. Within the German cluster of excellence "Internet of Production", a configuration for an interconnected data-base is proposed, where dat…