|Theme||Process design, Forming technology, Industry 4.0, Artificial Intelligence|
|Project title||Design of efficient forging sequences with mass distribution around the center of gravity line for forging parts (Effiziente Stadienplanung)|
|Project duration||01.11.2017 – 31.01.2021|
To increase the economic efficiency in the production of geometrically complicated forgings, material efficiency is a determining factor. For a high degree of material utilization, the preforms are decisive, which can be designed using efficient planning of process chains and preform geometries.
The aim of the project is to develop a universal method for process-reliable planning of multi staged forging sequences for forgings of all kinds. Based on a CAD geometry, the method should determine the resulting center of gravity line (3D) and mass distribution of the workpiece. Starting from the finished forging, the mass distribution is to be approximated to the mass distribution of the bar stock. The method considers forming rules in order to develop the forging sequence.
This method saves enterprises time in the development of the forging sequence. Furthermore, it is possible to increase the material efficiency in the production process by using the method.
Publications about the project
Reducing the planning and development time for efficient staging sequences in closed die forging offers companies in the forging industry a high potential for responding to competitive to respond to competitive challenges and remain competitive.The digitization of development processes opens up innovative support options for companies.
forging sequence desing, forming technology, digitization, process development, CAD
Artificial Intelligence, Forming technology Hedicke-Claus, Y.; Kriwall, M.; Langner, J.; Stonis, M.; Behrens, B.-A..: Validation of Automatically Generated Forging Sequences by Using FE Simulations. In: Daehn, G.; Cao, J.; Kinsey, B.; Tekkaya, E.; Vivek, A.; Yoshida, Y. (Eds): Forming the Future. The Minerals, Metals & Materials Series. Springer, Cham, 2021, pp. 2867-2881. DOI: 10.1007/978-3-030-75381-8_238.
To increase the economic efficiency in the production of geometrically complicated forgings, material efficiency is a determining factor. In this study, a method is being validated to automatically design a multi-staged forging sequence initially based on the CAD file of the forging. The method is intended to generate material-efficient forging sequences and reduce development time and dependence on reference processes in the design of forging sequences. Artificial neural networks are used to analyze the geometry of the forging and classify it into a shape class. Result of the analysis is information on component characteristics, such as bending and holes. From this, special operations such as a bending process in the forging sequence can be derived. A slicer algorithm is used to divide the CAD file of the forging into cutting planes and calculate the mass distribution around the center of gravity line of the forging. An algorithm approaches the mass distribution and cross-sectional contour step by step from the forging to the semi-finished product. Each intermediate form is exported as a CAD file. The algorithm takes less than 10 min to design a four-stage forging sequence. The designed forging sequences are checked by FE simulations. Quality criteria that are evaluated and investigated are form filling and folds. First FE simulations show that the automatically generated forging sequences allow the production of different forgings. In an iterative adaptation process, the results of the FE simulations are used to adjust the method to ensure material-efficient and process-reliable forging sequences.
Automatic process design, Forging, FEA, Resource efficiency, CAD
A method is presented that enables the complexity of a forging to be determined automatically on the basis of the CAD file of the forging. An automated evaluation of the forging complexity is necessary for a digitized and automated design of stage sequences in order to be able to determine important design parameters such as the flash ratio or the number of stages.
CAD, forming technology, algorithms
This paper presents a method for the automated classification of forged parts for classification into the Spies order of shapes by artificial neural networks. The aim is to develop a recognition program within the framework of automated forging sequence planning, which can directly identify a shape class from the CAD file of the forged part and characteristics of the forged part relevant for the design of the process.
Material efficiency and the development time of a forging sequence are decisive criteria for increasing the economic efficiency in the production of complex forgings. SMEs can often only interpret forging sequences in a shortened form due to insufficient capacities and high competitive pressure. Therefore, a generally valid method is to be developed that automatically generates multi-stage, efficient forging sequences based on the mass distribution of any forged part.
automated process design, die forging, resource efficiency