Surface roughness is one of the most significant parameters to determine quality of machined parts. Surface roughness is defined as a group of irregular waves in the surface, measured in micrometers (μm). Many investigations have been performed to verify the relationship between surface roughness and cutting parameters such as cutting speed, feed rate and depth of cut. To predict the surface produced by end milling, surface roughness models have been developed in this paper using the machining forces by assuming the end mill cutter as a cantilever beam rigidly or semi- rigidly supported by tool holder. An Aluminium workpiece and solid carbide end mill tools are used in this work. Model to predict surface roughness has been developed. Close relationship between machined surface roughness and roughness predicted using the measured forces signals.
This paper describes the utilisation of multi sensor fusion model using force, vibration, acoustic emission, strain and sound sensors for monitoring tool wear in end milling operations. The paper applies the ASPS approach (Automated Sensor and Signal Processing Selection) method for signal processing and sensor selection [1]. The sensory signals were processed using different signal processing methods to create a wide range of Sensory Characteristic Features (SCFs). The sensitivity of these SCFs to tool wear is investigated. The results indicate that the sensor fusion system is capable of detecting machining faults in comparison to a single sensor using the suggested approach.
Surface finish of machined parts in end milling operations is significantly influenced by process faults such as tool wear and tool holding (fixturing system). Therefore, monitoring these faults is considerably important to improve the quality of the product. In this paper, an investigation is presented to design the condition monitoring system to evaluate the surface roughness of the workpiece under effects of gradual tool wear and different types of the fixturing system. Automated Sensor and Signal Processing Selection (ASPS) approach is implemented and tested to determine the sensitivity of the sensory signals to estimate surface roughness under the variable conditions in comparison to surface roughness measurement device. The results indicate that the system is capable of detection the change and the trend in surface roughness. However, the sensitive features are found to be different based on the change in the fixturing system.
This paper aims to develop an effective sensor fusion model for turning processes for the detection of tool wear. Fusion of sensors’ data combined with novelty detection algorithm and learning vector quantisation (LVQ) neural networks is used to detect tool wear and present diagnostic and prognostic information. To reduce the number of sensors required in the monitoring system and support sensor fusion, the ASPS approach (Automated Sensor and Signal Processing Selection System) is used to select the most appropriate sensors and signal processing methods for the design of the condition monitoring system. The experimental results show that the proposed approach has demonstrated its efficacy in the implementation of an effective solution for the monitoring tool wear in turning. The results prove that the fusion of sensitive sensory characteristic features and the use of AI methods have been successful for the detection and prediction of the tool wear in turning processes and show the capability of the proposed approach to reduce the complexity of the design of condition monitoring systems and the development of a sensor fusion system using a self-learning method.
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