NON-QUALITY RISK EVALUATION FROM TOOL WEAR ASSESSMENT

Abstract

The development of part quality virtual sensors requires knowledge and observability of cutting conditions and in particular tool wear as tool are consumables. This paper presents an unsupervised anomalies detection approach to assess tool wear from standard machine load sensors in order to evaluate a non-quality risk metric. The developed methodology combines physics and business rules with density estimators to analyse the behaviour of axes and spindle loads. Industrial data from an automotive production line are used to illustrate the methodology application.

Recommended articles

DIGITAL GEOMETRY GENERATION OF HIGH PRECISION BROACHING TOOL CUTTING EDGES THROUGH IMAGE PROCESSING ALGORITHM

Z. Dombovari, D. Plakhotnik, Z. Gabos
Keywords: broaching | High precision machining | Big data | image processing

MACHINE LEARNING BASED IDENTIFICATION AND PRIORITIZATION OF ELECTRICAL CONSUMERS FOR ENERGY MONITORING

A. Stobert, B. Ioshchikhes, M. Weigold
Keywords: Energy transparency | computer vision | decision support