Nils Doede

Graduation:
M.Eng.
Function:
Project engineer
Practice Areas:
process design, process automation, bulk forging
Phone:
+49 (0)511 279 76-339
E-Mail:
doede@iph-hannover.de
vCard:
vCard
Xing:
https://www.xing.com/profile/Nils_Doede/cv
LinkedIn:
https://www.linkedin.com/in/nils-doede-6a21661b8

Publications

Process monitoring and the resulting increase in quality through AI are attracting increasing attention in large parts of the manufacturing industry. The possibilities of inline process monitoring of cross-wedge rolling are being investigated as part of the research of the Collaborative Research Center 1153. The aim is to develop a monitoring system that enables inline process control in order to compensate process deviations that occur during the forming process. Therefore, an algorithm is developed that can detect and classify process deviations within a few seconds and while the process is still running. An AI-based image recognition algorithm was applied as part of this research work. The process data was collected as part of a sensitivity study of the process parameters. A parameter study was used to determine optimized hyperparameters for AI modeling that enable a high prediction accuracy. The challenge of the necessary speed of the prediction was tested and validated. The evaluation of the algorithm including the generation of a picture requires 270 ms on average and is therefore fast enough to be used as preparation for process control. The investigations revealed a possibility for data augmentation that significantly increases the predictive accuracy of the models. Leave-One-Out Cross-Validation (LOOCV) was used to conclude the overall performance of the model.

Cross-wedge rolling, Hybrid components, Process monitoring, AI-based image recognition

Artificial intelligence (AI) can be used to reliably recognise errors, reduce defective parts and increase component quality. In the 'AutoPress' project, researchers have developed a system of sensors and AI that recognises 95 to 98 per cent of all process deviations.

Artificial intelligence, AI, process monitoring, error detection

A major advantage of flashless precision forging is the saving of resources by avoiding excess material. Especially materials with good flow properties, such as aluminum, sometimes lead to the formation of thin flash. Seals have been developed and experimentally tested to prevent thin flash, i.e. the flow of material into tool gaps. The study presented shows the effectiveness of different sealing materials and the ideal sealing geometry.

thin flash, Sealing concept, precision forging, forming technology, process optimization

Increasing the service life and process reliability of systems plays an important role in terms of sustainable and economical production. Especially in the field of energy-intensive bulk forming, low scrap rates and long tool lifetimes are business critical. This article describes a modular method for AI-supported process monitoring during hot forming within a screw press. With this method, the following deviations can be detected in an integrated process: the height of the semi-finished product, the positions of the die and the position of the semi-finished product. The method was developed using the CRISP-DM standard. A modular sensor concept was developed that can be used for different screw presses and dies. Subsequently a hot forming-optimized test plan was developed to examine individual and overlapping process deviations. By applying various methods of artificial intelligence, a method for process-integrated detection of process deviations was developed. The results of the investigation show the potential of the developed method and offer starting points for the investigation of further process parameters.

Process monitoring, Wear, Hot forming, Predictive maintenance, Quality management

In the research project “AutoPress”, the IPH – Institut für Integrierte Produktion Hannover gGmbH and Jobotec GmbH are jointly striving to develop an automated process control of screw presses. By retrofitting and applying an optimization algorithm, the energy demand is to be reduced and the component quality increased.

digitalization, forming technology, production technology

The use of a digital tool for workplace evaluation makes it possible to document and evaluate production workplaces in terms of their ergonomics with little effort. As part of the digitization project, a concept for optimizing workplace ergonomics was developed with the help of a digital tool and converted into a catalog of measures. The Institut für Integrierte Produktion Hannover (IPH), as a partner of the Mittelstand-Digital Zentrum Hannover, has supported WISTRO Elektro-Mechanik GmbH from Langenhagen in a project to carry out a systematic workplace assessment within a digital tool. In addition, ergonomic optimization measures for an assembly workplace were identified and evaluated using an individual utility value analysis.
By carrying out the project, it was possible to create awareness and concrete measures for the topic of ergonomics and thus to increase the possibilities for an increase in the number of employees and thus the possibilities for increasing employee motivation.

ergonomics, digitalization, employee satisfaction

An elementary factor for influencing the process quality and the energy requirement of an energy-bound forming machine is the stored work capacity. At present, the forming energy is calculated via the work energy introduced into the system. Here, factors such as spindle torsion or frictional resistance are only taken into account roughly.
Within the framework of the research project "Development of a retrofit system for friction screw presses for automation and minimization of the set-up time and development of a sensor array for the first-time recording of elementary process variables such as the forming force" (AutoPress), which is funded by the Industrielle Gemeinschaftsforschung (IGF), the IPH - Institut für Integrierte Produktion Hannover gGmbH and JOBOTEC GmbH are striving for an automated process control of screw presses and are providing initial findings in this regard.

retrofit, forging technology, digitization

Process Optimization through Thin Flash Prevention. Due to the good flow properties of aluminum, the material tends to flow into tool gaps during flashless precision forging and produce the so-called thin flash. For the industrial implementation of flashless precision forging processes, an innovative prediction method for thin flash as well as sealing concepts are to be developed in cooperation with an industrial partner. Simulative studies show that local form filling does not correlate with high pressure or an increased potential for thin flash.

thin flash, FEM-simulation, sealing concepts, precision forging, forming technology

Research projects