{"id":6873,"date":"2025-05-20T09:44:36","date_gmt":"2025-05-20T09:44:36","guid":{"rendered":"https:\/\/lcm.web-email.at\/blog\/early-fault-detection-for-all-injection-molding-machines\/"},"modified":"2025-08-13T08:30:06","modified_gmt":"2025-08-13T08:30:06","slug":"early-fault-detection-for-all-injection-molding-machines","status":"publish","type":"post","link":"https:\/\/lcm.web-email.at\/en\/early-fault-detection-for-all-injection-molding-machines\/","title":{"rendered":"Early fault detection for all injection molding machines"},"content":{"rendered":"\n
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Early fault detection for all injection molding machines<\/h2>\n\n\n\n
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Reduction of machine downtimes<\/h3>\n\n\n\n

By detecting anomalies in injection molding machines at an early stage, condition monitoring can prevent both unwanted machine downtimes and unnecessary rejects.<\/p>\n\n\n\n

In the plastics cluster project \u201cReGuMa – Reduction of planned and unplanned machine downtimes\u201d, one or two defined error cases were analyzed at each company partner and the possibility of preventing unplanned downtimes was evaluated.<\/p>\n\n\n\n

The aim was early fault detection for all types of injection molding machines using non-invasive sensors and intelligent evaluation of the sensor data.<\/p>\n\n\n\n

Project partners: AISEMO GmbH, Asp\u00f6ck Systems GmbH, Ing. Gerhard Fildan GesmbH, Ing. H. Gradwohl GmbH, MKW Kunststofftechnik GmbH, PTM Kunststofftechnologie GmbH. <\/p>\n<\/div>\n<\/div>\n\n\n\n

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