Leveraging Sensor Measurements for Process Quality Assessment

Hot forging is a critical manufacturing process where metals are heated above their crystallisation temperature to a malleable state and then shaped under high pressure. This technique is widely used across various industries, including automotive, home technology, and power management, to produce high-strength components with complex geometries. Despite its effectiveness, maintaining top-tier quality throughout the hot forging process poses significant challenges.

One of the main difficulties in hot forging is ensuring defect-free production while maintaining optimal quality control. Traditional methods often struggle to consistently achieve the desired quality due to the complex interplay of process variables and their impact on the final product. As industries increasingly demand higher precision and reliability, there is a growing need to enhance quality assurance in these manufacturing processes.

To address these challenges, Bons & Evers is collaborating with FIP-AM@UT on the Qsense project. This initiative aims to enhance quality assurance and gain insights into the production processes for sustainable improvements. Qsense will leverage smart sensor technology to provide comprehensive insights into both product and process quality, addressing the limitations of current manual inspections.

The project focuses on a pilot study that uses off-the-shelf sensors to monitor and analyse a single hot forging production line. By evaluating key process measurements and their effects on product outcomes—ranging from ideal to defective or out-of-tolerance components—Qsense seeks to establish crucial relationships that can drive improvements in quality assurance and control (QA/QC). This targeted approach, concentrating on external surfaces and coarse tolerance limits, ensures precise and effective analysis.

Industry partner

WORK PACKAGES

Every FIP-AM@UT project is divided into separate work packages, to clearly distinguish the several stages and purposes within the project. QSense consists of four work packages.

icon_Analysis

Use case assessment

Assessing the current production technology

icon_Assessment

Sensor Integration and Data extraction

Integrating the sensors and extracting the data

Data Analytics

Analyse data to improve quality control

icon_OptimisationDemonstration

Demonstrator

Integrate machine learning algorithms into dashboard as proof of concept

FOR MORE INFORMATION ABOUT QSENSE, PLEASE FEEL FREE TO REACH OUT TO

Celal Soyarslan

Celal Soyarslan

Associate Professor

Can Ölmezoğlu

Can Ölmezoğlu

Engineering Support, Software Development

Estefanía Morás Jiménez

Research Engineer