KIPOS

Project process

• Start

  Project start

  The overall project is divided into 3 sub-projects, with each party taking on one sub-project depending on its own competencies.
  

  As an expert in the field of software technologies, antares is developing an interaction platform for the worker in the injection molding process. The tool is intended to provide the operator with AI-supported parameter recommendations based on real-time process data and thus optimally improve the process.
  

  Weißer & Grießhaber is responsible for the implementation and validation of the AI process model based on training data from industrial production.
  
  The AI-based process model is being developed by Hahn-Schickard. It will be used for process and component optimization in the plastic injection moulding process.

• 7/20

  Design of a software architecture model

  Based on current technology, a hybrid architecture was chosen that includes both local and cloud components at project partners. This chosen overall architecture provides the ideal basis for the research project, as it offers cloud-based scalability and enables the connection of local IoT devices such as cameras and sensors.
  
  In order to collect, process and use data from various sources, interfaces to all databases and text sources as well as templates for data visualization are integrated.

• 12/20

  Development of a software solution for static design of experiments (DoE)

  The web application allows you to create an experimental design with various settings, including input and output parameters as well as the definition of the name and the model to be used (full/partial factorial designs, designs according to the Taguchi method). Values for the previously defined factors and specific settings can then be entered.
  
  The subsequent view of the resulting plan shows each test run in which the specified parameter values must be set on the machine and the output parameters listed at the end must be measured and entered. All created plans are displayed in the plan overview, including useful information such as parameters or model. The current status is indicated by different colors, and it is possible to delete or subsequently edit plans.

• 2/21

  Implementation of a model for the detection of surface anomalies on plastic components

  In order to detect defects, the focus is mainly on the use of a Convolutional Neural Network (CNN). For this purpose, the images obtained from a previous production process are divided into five different classes, with one class describing good components and the remaining four different anomalies. Using this method, over 93% of workpieces are already correctly categorized.
  
  Subsequent hyperparameter tuning and classification using an algorithm ensure a further increase in accuracy. Thanks to pre-processing optimization, the computing effort can also be reduced to such an extent that the processing time for an image is reduced to 1/100 of a second.
  

• 2/21

  Development of the assistance system

  Even before the development phase of the assistance system, the decision was made in favor of an architecture model that would ensure problem-free scalability and maintainability. To this end, a microservice was developed that links all data from the project partners' various data sources in a central database
  

  Further components of the assistance system summarized:
  

  • Generating and maintaining the various models for predicting component quality
  • Front-end application for viewing all data in real time
  • Individual assignment of roles and authorizations to users
  • Backend (service) for (real-time) communication between all endpoints

• 11/21

  Development of a process model together with Hahn-Schickard

  Different types of models are used to analyze the initial data that will be used to create the process models. Based on the results, a selection is made to use a limited number of models for further experiments. The selected model types include: 1. linear regression, 2. random forest, 3. support vector machine and 4. neural network.
  
  The first adjustments already lead to improved results for all four models, so that even at this stage none of the generated models provide unrealistic values when predicting quality features.

• 4/22

  Software solution for the automatic creation of a process report

  The individual process reports have a modular structure and can be called up at any level of abstraction depending on the data displayed. The generated reports are based on both current and historicized data.

  For example, general information on the respective machines can be called up at the highest level of abstraction and reports can be created to view individual orders or analyze errors in the process.

• 9/22

  Installation and adaptation of signal processing and process models in the field at Weißer + Grießhaber and Hahn-Schickard

  Thanks to the container architecture used and the prior data collection and preparation, the installation of the software solution in the industrial environment is largely problem-free. The adjustments only affect the local conditions, such as the number of integrated machines.

  By making a few modifications, the model quality could be increased considerably, in particular by reducing the parameters and quality characteristics used for the prediction.
  

• 3/23

  Project completion

  Although the processes involved in injection molding are extremely complex and each machine has individual characteristics, we have developed a solid basic architecture that will serve as the basis for future injection molding projects.
  

  The DoE tool is already available as a ready-to-use standard solution.
  

  The models we developed for process parameters and for detecting surface anomalies are another success of the project. With very high accuracy in the detection of defective components and precise defect type categorization, excellent results were achieved. Provided certain conditions are met, existing models can easily be supplemented with additional data and retrained.
  

Do you have any questions?

Would you like to find out more about the KIPOS project or become our pilot customer directly? Then please get in touch with us! We look forward to hearing from you.