Affiliation:
1. GIT, GITAM Deemed to be University, Visakhapatnam, India
2. Mechanical Engg, VNR Vignana Jyothi Institute of Engineering & Technology, Hyderabad, India
Abstract
In order to manufacture low-cost, high-quality goods, in-process tool condition monitoring is an essential responsibility in the manufacturing industry. In this study, a multisensor fusion technique was used to build and execute an effective and reliable TCM system in turning operations with coated and uncoated tungsten carbide inserts. In dry turning, an attempt has been made to optimize the turning process parameter and monitor the tool's condition. Acoustic optic emission based sensor i.e., Laser Doppler Vibrometer and FLIR E60 infrared thermal camera are strategically placed near the machining zone. Thermal images and vibration signals are recorded using an appropriate charge amplifier. To extract characteristics from numerous sensor data, a National Instruments data acquisition (NI-DAQ) system is constructed utilizing LabVIEW software. Thermal images are used to gather temperatures from tool-work piece locations. Vibration signals are translated into vibration parameters. These characteristics serve as the foundation for establishing in-process TCMS. Tool wear, vibrational displacements (Disp), and cutting temperature are investigated as a result of varied tool insert materials and process conditions (CT). Utilizing ISO 10816-3, ISO 3685, and ISO-18434-2008 standards, the cutting tool condition was assessed using extracted features from multi sensor fusion techniques. For Ti-6Al-4 V, the displacement of uncoated and coated tools increased by 65.28% and 44.71%, respectively. For AISI 316L flank wear, the uncoated insert effected 41% and the coated insert impacted 24.14%, respectively. While machining Al7075, the relationship of depth of cut and feed rate on flank wear maintains an identical trend. It is discovered that both temperature and displacement have a significant role in the evolution of flank wear, which is examined in depth. This paper recognizes the use of multi-sensor data in tool condition monitoring when rotating with various cutting tool inserts.
Funder
Science and Engineering Research Board
Subject
Industrial and Manufacturing Engineering,Mechanical Engineering
Cited by
2 articles.
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