UROP Project

Data-based model building and process optimization with artificial neural network for injection molding.

Contact

Name

Daniel Holder

Program Director UROP

Telephone

workPhone
+49 241 80-90695

E-Mail

Key Info

Basic Information

Project Offer-Number:
1142
Category:
RWTH UROP, UROP Network, UROP International
Field:
Mechanical Engineering
Faculty:
4
Organisation unit:
Institute for Plastics Processing (IKV) in Industry and Craft at the RWTH Aachen University
Language Skills:
Fluency in English

MoveOn

Excellence Cluster: Internet of Production

In the field of injection molding, the optimization of machine parameters is a crucial part of the set-up process in order to ensure efficient and stable manufacturing. Due to its high non-linearity, the injection molding process is frequently optimized utilizing expert knowledge, e. g. from experienced technicians or engineers. However, this strategy guarantees neither an optimized process nor reproducibility, as it depends solely on the technician's experience. A possible solution to an unbiased set-up is the usage of data-based methods that are fitted to the specific process by gathered data samples. The already has vast experience with artificial neural networks and evolutionary algorithms in this field.

Task

One highly important aspect to data-based modelling is the necessity of an adequate amount of data samples to train the models. As data acquisition is costly, in terms of time and material, the objective is to reduce the necessary amount of data to train the artificial neural networks. One possible approach to this is so-called transfer learning. During your time at the IKV, you will assist in conceptual as well as practical research to achieve this goal. Some of your tasks may be: - Literature research with latest publications, describing the state-of-the-art of artificial neural networks and transfer learning (generally and in the field of plastics processing) - Injection molding simulations - Construction of new molds for manufacturing - Data acquisition with injection molding machines on a fully connected production/shop floor - Measurements on produced parts for quality evaluation - Implementing new models of artificial neural networks in a Python-microservice

Requirements

The research is highly complex and requires a lot of skills in its entirety. You are NOT required to have experience in ALL of the following fields, even no experience at all is ok! - Experience with Sumitomo / Arburg / Engel injection molding machines - Experience with Cadmould / Moldex3D injection molding simulation software - Experience with Inventor (2019/2020) CAD software - Experience with Python, Javascript and/or AngularJS - Knowledge in the field of injection molding, algorithms and machine learning - Communicative, self-reliant and reliable person