Researchers Target Metalcasting Quality “Optimization”

April 20, 2004
Europeans aim to combine QNDC techniques

A group of European industrial-monitoring specialists and technical organizations are collaborating to improve the product quality and process control of cast metal parts. They claim their effort could result in manufacturing cost savings of 20% and inspection and/or quality-management cost reductions of 10-50%.

The QUME Project (“QUality optimization for the manufacturing of cast MEtallic parts”), is partly funded by the European Commission and involves several European research and academic organizations, as well as companies that specialize in monitoring and quality management.

“The aim is to focus the potential of proven developed technologies and techniques in inspection, testing and data management and to harness them in an integrated fashion that provides the optimum platform for in-line inspection and quality assurance of cast parts,” a press release explains.

While most castings undergo random or batch testing for faults, critical components must be 100% inspected before shipment. So, the challenge is to improve the reliability and effectiveness of inspection without adding cost. The QUME project team will work first to reduce the cost of implementing 100% testing. Then, they aim to develop in-line fault-detection techniques that will not complicate the production or quality processes.

The project team believes this approach will lead to a new intelligent manufacturing system based on a “fusion” of two quantitative non-destructive characterization (QNDC) techniques: intensity calibrated high spatial resolution (microfocus) x-radioscopy, and multi-sensor vibration analysis.

“By overlapping these technologies,” the release explains, “virtually any casting fault can be detected during the production process. Process or condition related decision taking will then be possible using multidimensional pattern recognition techniques, coupled with image processing procedures to allow a fast quantitative defect detection and characterization.”