Torsten Hermanns

Self-optimizing Production Technologies

Customer demands have become more individual and complex, requiring a highly flexible production. In high-wage countries, efficient and robust manufacturing processes are vital to ensure global competitiveness. One approach to solve the conflict between individualized products and high automation is Model-based Self-optimization (MBSO). It uses surrogate models to combine process measures and expert knowledge, enabling the technical system to determine its current operating point and thus optimize it accordingly. The objective is an autonomous and reliable process at its productivity limit. The MBSO concept is implemented in eight demonstrators of different production technologies such as metal cutting, plastics processing, textile processing and inspection. They all have a different focus according to their specific production process, but share in common the use of models for optimization. Different approaches to generate suitable models are developed. With respect to implementation of MBSO, the challenge is the broad range of technologies, materials, scales and optimization variables. The results encourage further examination regarding industry applications.

Determination of process variables in melt-based manufacturing processes

Industrial manufacturing requires continuous production at reliable quality to be competitive. Many manufacturing processes are run close to their technological limits to increase productivity what leads to a significant threat of malfunction in the case of limited control over setting parameters or deviating boundary conditions. This paper discusses the difficulties of determining process variables during manufacturing for two melt-based manufacturing processes, laser cutting and gas metal arc welding. Both manufacturing processes show a highly dynamic and complex behaviour which today prevents a physical description of all interactions of the process variables. On the practical side, even the dominant process variables cannot be measured as they are not directly accessible. The approach that is presented here suggests a combined solution with both modelling and measuring tools that connect through surrogate criteria. It involves a simplified modelling of the manufacturing process that describes the process behaviour well enough and that can be evaluated numerically within a short time frame. The measurement evaluates a property of the process which is well accessible. This ensures robust signal processing and stable information about the surrogate criterion. In combination with the simplified model, the operating point of the process can easily be determined. For laser cutting of metal sheets and gas metal arc welding, it is demonstrated how to acquire information about the process and how to model surrogates. The research is focused on providing tools for fast machine set-up and for components which can be used for self-optimisation.

An integrated approach for the knowledge discovery in computer simulation models with a multi-dimensional parameter space

In production industries, parameter identification, sensitivity analysis and multi-dimensional visualization are vital steps in the planning process for achieving optimal designs and gaining valuable information. Sensitivity analysis and visualization can help in identifying the most-influential parameters and quantify their contribution to the model output, reduce the model complexity, and enhance the understanding of the model behavior. Typically, this requires a large number of simulations, which can be both very expensive and time consuming when the simulation models are numerically complex and the number of parameter inputs increases. There are three main constituent parts in this work. The first part is to substitute the numerical, physical model by an accurate surrogate model, the so-called metamodel. The second part includes a multi-dimensional visualization approach for the visual exploration of metamodels. In the third part, the metamodel is used to provide the two global sensitivity measures: i...

Meta-modelling, visualization and emulation of multi-dimensional data for virtual production intelligence

Decision making for competitive production in high-wage countries is a daily challenge where rational and irrational methods are used. The design of decision making processes is an intriguing, discipline spanning science. However, there are gaps in understanding the impact of the known mathematical and procedural methods on the usage of rational choice theory. Following Benjamin Franklin’s rule for decision making formulated in London 1772, he called “Prudential Algebra” with the meaning of prudential reasons, one of the major ingredients of Meta-Modelling can be identified finally leading to one algebraic value labelling the results (criteria settings) of alternative decisions (parameter settings). This work describes the advances in Meta-Modelling techniques applied to multi-dimensional and multi-criterial optimization by identifying the persistence level of the corresponding Morse-Smale Complex. Implementations for laser cutting and laser drilling are presented, including the generation of fast and fru...

Mathematical modelling and linear stability analysis of laser fusion cutting

A model for laser fusion cutting is presented and investigated by linear stability analysis in order to study the tendency for dynamic behavior and subsequent ripple formation. The result is a so called stability function that describes the correlation of the setting values of the process and the process’ amount of dynamic behavior.

Modelling for self-optimization in laser cutting

Manufacturing of parts by laser cutting is widely accepted in industry. In the market there are systems which are highly focused on special applications like robot based 3D cutting but there are also general purpose machines which cut a variety of metal sheets at different thicknesses. Most of these machines use a melt based cutting process where the material is molten and ejected by an assist gas. Quality of the laser cut part is determined by a dross free cut with minimal roughness on the cut faces after all standardized properties have to be fulfilled. To ensure this level of quality, several process parameters have to be kept at the right values and within defined tolerances. The most important one is the focal position which cannot be detected during processing. In this context, the transient behavior of the focal position due to thermal heating of optical elements is a challenging problem. In this paper, a process model is developed which allows the determination of the focal position by an experimentally accessible surrogate criterion. The theoretical predictions are compared with experimental results and a concept for an active focus control is discussed.

In-situ measurement of the focal position in one and ten micron laser cutting

In laser cutting, quality is perceived as a combination of geometry of the cut face, its roughness and the existence of dross. Several parameters such as the caustic of the laser beam or properties of the gas jet influence this quality [1], [2]. Major impact however has the focal position of the laser beam. An in-situ determination of the focal position can be established by measuring the width of the kerf and translating it to the focal position. In this paper, results from kerf width measurements in CO2 laser cutting are compared to those from one micron fiber laser cutting. The experiments are based on cutting stainless steel with a thickness of 4 mm using nitrogen for melt ejection.In laser cutting, quality is perceived as a combination of geometry of the cut face, its roughness and the existence of dross. Several parameters such as the caustic of the laser beam or properties of the gas jet influence this quality [1], [2]. Major impact however has the focal position of the laser beam. An in-situ determination of the focal position can be established by measuring the width of the kerf and translating it to the focal position. In this paper, results from kerf width measurements in CO2 laser cutting are compared to those from one micron fiber laser cutting. The experiments are based on cutting stainless steel with a thickness of 4 mm using nitrogen for melt ejection.