Companies & Partners
Bernard KRONE Holding SE & Co. KG, MAN Trucks, Fraunhofer IPT, Machine Tool Laboratory WZL, DATAbility GmbH, i2solutions GmbH

Problem
The commercial vehicle industry is an extremely challenging industry. With the ever-rising demands from industry and logistics customers, it is highly important to have defect-free manufacturing of commercial vehicles, optimal operation, and identify errors along the product life cycle as early as possible.

Solution
An AI-based failure management system is developed across the whole value chain of commercial vehicles from the raw material to the actual operation based on machine learning models. These models are trained also on production and quality data from the manufacturing processes of commercial vehicles.
The resultant model is used for error management by predicting deviations from quality targets in production as well as necessary maintenance and foreseeable damages in operation of the vehicles. Further, to reduce the defects, optimal process parameter for the manufacturing process will be recommended based on the trained models.

Outcome
A system for error management and minimal defect manufacturing of commercial vehicles. Reduced production and operation costs, minimal failures, increased service life, higher customer satisfaction.

The post Commercial Vehicles – Min. Failures appeared first on IconPro.

Companies and Partners
Tessonics Inc., University of Waterloo, NRC’s Aerospace Research Centre, Ford-Werke (Ford Company) GmbH, RWTH Aachen University

Problem
One of the essential production steps in industrial production is the automated joining of metallic structures. One of the most important joining methods is welding. In automobile production industries, Resistance Spot welding (RSW) and Laser Brazing welding (LBW) are primarily used with the help of the robot-guided system. An enormous high-quality assurance effort is still required for this automated joining process which in turn is very expensive.

Goal
An AI-based welding monitoring is developed that predicts the quality of the weld, thereby reducing the costs of quality inspection. With the help of ultrasound data and the ground truth annotation, an AI-based image segmentation algorithm is trained to correctly predict segmentation for weld nuggets for both RSW and LBW. The results obtained from the trained model are used for meaningful characterization of the welding process based on the company’s guidelines. The trained model is integrated into the production environment to provide real-time analysis of quality criteria.

Outcome
Real time intelligent AI-based welding monitoring systems for Resistance Spot welding and Laser Brazing welding. Significantly reduced manufacturing and quality inspection costs.

The post Manufacturing – Ultrasonic Inspection appeared first on IconPro.

Companies & Partners
Mubea, Tailor Rolled Blanks GmbH, Eichsfelder Schraubenwerke GmbH, Schomäcker Federnwerk GmbH, Quality Automation GmbH and MAWI GmbH and Schuler Pressen GmbH, WZL of RWTH Aachen University.

Problem
Instabilities due to external influencing variables and unknown interdependencies between process parameters and product quality characteristics often lead to scrap in bulk metal forming processes despite existing process controls.

Solution
A method based on reinforcement and transfer learning is implemented for the implementation of novel controllers in the quality control loops of bulk metal forming processes. In order to reduce the learning time of the reinforcement learning algorithm and to save resources, it is not only trained directly on the real process, but also on a stochastic process simulation. The knowledge gained from the simulation is then transferred to the quality control loop of the control loop by means of transfer learning. The result is a quality control of bulk metal forming processes, which controls the processes automatically, comprehensively and in real time and optimizes the quality of the processes.

Outcome
The improved process quality in turn increases the quality of the automotive products and reduces scrap.

The post Automotive Metal Forming appeared first on IconPro.

Companies & Partners
WBA Aachener Werkzeugbau Akademie GmbH, Makino Europe GmbH, WZL of RWTH Aachen University,

Problem
Automation requires stable and adaptive processes that no longer require manual intervention. One common manufacturing process that is commonly used in tool and mold making and is becoming increasingly important in the aerospace industry is wire electrical discharge machining. Today, it is not possible to evaluate the productivity and quality of the wire electrical discharge machining process online based on physical variables and other process data.

Solution
A suitable model for the given data is determined automatically by systematic data preparation and data reduction using machine learning. With data from variable process conditions, statistical models are trained. In addition, mechanical physical models for eroding removal behavior and flushing will be developed for the studies. Merging these models will provide a digital representation of the workpiece to predict and optimize quality. With the help of this comprehensive model, both online process monitoring and data-based optimization of process parameters for a machining technology will be realized. Finally, the functions will be transferred to an industrial app to simplify the automation of the electrical discharge machining process.

Outcome
Increased capability of the wire electrical discharge machining process

The post Mechanical – Wire Eroding appeared first on IconPro.