An aluminum automotive parts supplier adopts a new, advanced surface-temperature monitoring system to keep molds reliable and effective

Tecopress S.r.l. is an automotive diecaster in north central Italy, supplying light-alloy parts like aluminum cylinder heads, crankcases, bed plates, steering housings, gearboxes, engine blocks, and battery charger housings for diesel, gasoline, and hybrid engines, to OEMs that include Audi, Ducati, BMW, and Volkswagen.

Source: tecopress.it

The diecaster has adopted a commitment to process innovation and a research-based approach to process improvement.

One of its recent implementations has been an infrared-vision technology developed by Marposs called Total Thermal Vision (TTV). TTV is an advanced surface temperature monitoring system, which Tecopress has adopted to evaluate the condition and performance of diecasting molds. Reportedly, this system’s performance has exceeded the performance of previous monitoring systems Tecopress has used by significantly improving casting cycle time, increasing product quality, conserving production time, and saving production and maintenance costs.

Automotive manufacturers know even the slightest defect in a production system will have an expansive effect on the finished products, an effect that is magnified when product volumes and production sequences are increased. More specifically, it is critical to maintain surface temperatures on diecasting molds to avoid overheating, which may lead to metallization. That would result in cracks and splits in the mold. This costs manufacturers time and money by causing unexpected machine downtimes to replace molds and requiring additional machine maintenance.

Thermographic monitoring addresses these issues by helping to maintain surface temperatures within the optimal range, significantly reducing unexpected breakages and premature mold replacements, and increasing overall machine productivity and efficiency. When applied to diecasting, thermography is an essential tool for monitoring process variables, such as expected temperatures, temperature repeatability, and eventual deviations.

Source: tecopress.it