- The IPH
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
In order to automate the order control of tooth replacement products, an AI model was developed that enables classification into different product classes. The individual tooth replacement products are available in STL files. A mixed-data approach was used for the AI model. The STL file is converted to an image file and passed to a CNN and in parallel, information such as volume and surface dimensions were extracted from the STL file and passed to a ANN. The output from the ANN and the CNN is then combined to produce the final classification of the tooth replacement product.
Automated order control, AI, ANN, image processing, CAD
A concept was developed for the digitization of business processes in a craft enterprise. By introducing a document management system, the functional scope of the ERP system already in use was expanded and the business processes were converted to a paperless office. In addition, a concept for a digital construction file was developed in order to integrate employees in the field into digitized processes.
DMS, ERP, digitization, business processes, paperless office, trade
Solid formed components are subject to ever higher load requirements while at the same time striving for resource efficiency.
ciency at the same time. An ultrafine-grained microstructure can improve the strength and ductility of the component. This makes it possible to design smaller and lighter components and to exploit lightweight construction potential. One possibility
process for producing an ultrafine-grained microstructure is cross wedge rolling.
Cross wedge rolling, Fine-grained structure, Lightweight construction
In a research project at the Institute for Integrated Production in Hanover, the process parameters for cross-wedge rolling are to be determined with which an ultrafine microstructure can be achieved in cylindrical blanks. The aim is to achieve grain sizes of the rolled part in the range of 500 nm.
Process development, cross wedge rolling, material properties,Ultra fine microstructure
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