Development of Online Tool Wear-Out Detection System Using Silver–Polyester Thick Film Sensor for Low-Duty Cycle Machining Operations

Abstract

This paper deals with the design and development of a silver–polyester thick film sensor and associated system for the wear-out detection of single-point cutting tools for low-duty cycle machining operations. Conventional means of wear-out detection use dynamometers, accelerometers, microphones, acoustic emission sensors, thermal infrared cameras, and machine vision systems that detect tool wear during the process. Direct measurements with optical instruments are accurate but affect the machining process. In this study, the use of a thick film sensor to detect wear-out for aa real-time low-duty machining operation was proposed to eliminate the limitations of the current methods. The proposed sensor monitors the tool condition accurately as the wear acts directly on the sensor, which makes the system simple and more reliable. The effect of tool temperature on the sensor during the machining operation was also studied to determine the displacement/deformation of tracing and the polymer substrate at different service temperatures. The proposed tool wear detection system with the silver–polyester thick film sensor mounted directly on the cutting tool tip proved to be highly capable of detecting the tool wear with good reliability.

Sensor-based intelligent tool online monitoring technology: applications and progress

“…Therefore, the use of sensors for realtime monitoring of the cutting process of cutting force, cutting temperature, cutting vibration, and other physical signals, the purpose of which is to ensure the parts meet the standards of qualification, to avoid excessive wear or the tool, resulting in unnecessary waste. Rakkiyannan et al [101] installed a thick silver polyester film sensor directly at the tip of the cutting tool, allowing wear to act directly on the sensor to monitor the real-time status of the tool under low loads. In addition to the above monitoring of typical physical quantities in the cutting process, there are a variety of methods for online monitoring of tool wear, including optical monitoring, audio monitoring, AE technology, spindle motor current/power monitoring, and ultrasonic monitoring which are also methods of indirectly monitoring the state of tool wear using sensors.…”

Methodology for Tool Wear Detection in CNC Machines Based on Fusion Flux Current of Motor and Image Workpieces

“…Another variable used is temperature; in the work presented by Rakkiyannan et al [10], a sensor was designed and placed on a high-speed steel cutting tool and the changes in temperature and deformation provided information on the state of the tool while cutting mild steel. The results were corroborated with thermographic images, and three levels were successfully detected with a span of 1.2 mm due to the sensor's degradation.…”

“…Several literature reviews have identified the main techniques, tools and trends for processing [21][22][23][24]. Firstly, signals are processed directly in the time domain [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] or techniques or transforms are used for their analyses in the frequency [3,5,[18][19][20] or time-frequency [19,20] domains. From here, several techniques are used to obtain features that allow the analysis to be carried out in a better way, such as the use of statistical indicators [3][4][5][6]8,14,[18][19][20], time or time-frequency transforms for direct feature extraction [3][4][5]19,20] and, in some cases, methods for the selection of the most appropriate features or dimensionality reduction such as heuristic techniques [5,6,22], linear discriminant analysis (LDA) [19] or principal component analysis (PCA).…”

“…The use of intelligent techniques is of great value when processing large amounts of information generated by fused sensors, allowing a more adequate manipulation and interpretation of the data. Another point to note is the implementation of the methodologies, since they can be performed online, allowing process monitoring [3][4][5][7][8][9][10][11][12][14][15][16][17][18][19], or offline once the machining of a part has been carried out [6,13,20,26]. This is an important aspect, since in methodologies that use vision systems or images, the machining process must be stopped for the acquisition of images of the tool or they may be taken after the process; however, this depends on the needs and planning of the process.…”