Kamerabasiertes Überwachungssystem zur Detektion kritischer Späne

Machining processes such as turning or drilling can produce unfavourable chip shapes, known as critical chips. Long chips or tangled chips can become entangled in the machine's working area and cause damage to the workpiece, tool or the machine itself. Until now, monitoring has mostly been carried out manually by machine operators, which ties up personnel capacity and prevents fully automated operation. Existing camera systems in machine tools do not offer automated image data analysis.

Objectives

The aim is to develop a camera-based monitoring system that automatically detects critical chips using AI. The chips are to be identified, classified and located in parallel with the process so that personnel can be informed in the event of critical shapes. This should prevent damage and enable a higher degree of automation in machining

Benefits

The project enables:

• Direct measurement and detection of chip shape
• Detection of critical chips under the influence of cooling lubricants
• Evaluation of the process based on chip shape

Approach

The project is being carried out in cooperation with industry partner Rotoclear. First, a camera system will be integrated into the working area of a machine tool to create a data set with different chip shapes. An AI model will be trained based on this data. This model should be able to classify and locate the chips in parallel with the process. The detection results are then fed into an AI-supported process evaluation to inform the operating personnel in the event of critical chips.

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Contact Lee Hartung via email at hartung@ifw.uni.hannover.de or by phone at +49 511 762 18316.