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A model-based approach for monitoring of shape deviations in peripheral milling

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TLDR
In this article, a model-based approach for monitoring of shape deviations for milling operations is presented, which can be monitored against geometric tolerances, providing a quality monitoring of manufacturing processes.
Abstract
This paper presents a model-based approach for monitoring of shape deviations for milling operations. In order to detect occurring shape deviations of the machined workpiece during the milling process, different kinds of process models are presented and discussed for their application on manufacturing quality monitoring. Thereby, a model-based system was presented for the monitoring of shape deviations based on measured cutting forces. For the transformation of cutting forces into shape deviations, a tool deflection model and material removal model were designed and applied to a monitoring system. The presented model-based monitoring approach delivers accurate quality information, like geometric shape deviations, which can be monitored against geometric tolerances, providing a quality monitoring of manufacturing processes. The reconstruction of shape deviations from measured cutting forces is verified experimentally by comparing measured and reconstructed shape contours.

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Self-optimizing tool path generation for 5-axis machining processes

TL;DR: A self-optimizing process planning approach for 5-axis milling that allows an automatic compensation for tool deflection and the resulting shape error can be reduced by 50%.
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Identification of the specific cutting force for geometrically defined cutting edges and varying cutting conditions

TL;DR: In this article, a co-operative force model for the identification of the specific cutting forces and prediction of integral forces is presented, coupled bidirectionally with a multi-dexel based material removal model that provides geometrical contact zone information.
References
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Journal ArticleDOI

Tool Condition Monitoring (TCM) — The Status of Research and Industrial Application

TL;DR: The use of sensor systems for tool condition monitoring in machining and grinding is becoming more commonplace to enhance productivity as mentioned in this paper, and the motivation and basis for the utilization of these systems in industry, the sensors used in such systems including industrial application, new developments in signal and information processing, sensor based process optimization and control and directions for future developments.
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Neural Network Sensor Fusion for Tool Condition Monitoring

TL;DR: The design and implementation of a neural network-based system combining the outputs of several sensors (acoustic emission, force and spindle motor current) for monitoring progressive tool wear in a single point turning operation is described.
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Error compensation strategy in milling flexible thin-wall parts

TL;DR: In this paper, an advanced error prediction and compensation strategy specifically focused on force-induced errors in machining of thin-wall structures was proposed, where the machining error is predicted using a theoretical flexible force-deflection model and compensated for by optimising the tool path prior to machining.
Journal ArticleDOI

Surface roughness monitoring application based on artificial neural networks for ball-end milling operations

TL;DR: The aim of this work was to develop a reliable surface roughness monitoring application based on an artificial neural network approach for vertical high speed milling operations.
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