ABSTRACT
In the present work, the surface integrity and flank wear of uncoated cermet inserts in dry turning of AISI 1045 steel were evaluated. Three-dimensional techniques were used to assess the surface roughness. Previously, finite element analysis was carried out to predict the cutting forces and heat distribution in the chip formation region. Cutting speed and feed were the parameters varied in the experiments. Feed is decisive in the final quality of the turned surface and cutting speed had little influence on this aspect. The surface was significantly damaged with the progression of the insert flank wear. Considering an average flank wear VBB of 0.1 mm, a tool life of 35 min was achieved using a cutting speed of 175 m/min, and of 23 min for a cutting speed of 275 m/min. Abrasive wear was predominant during the experiments. No microstructure defects were observed, as well as crack propagation or accentuated deformations near the machined surface region. Therefore, the dry turning of 1045 steel with cermet inserts route has proven extremely viable from the standpoints of tool life, surface integrity, chip formation, and sustainability.
ABSTRACT
The computer numerical control (CNC) machine has recently taken a fundamental role in the manufacturing industry, which is essential for the economic development of many countries. Current high quality production standards, along with the requirement for maximum economic benefits, demand the use of tool condition monitoring (TCM) systems able to monitor and diagnose cutting tool wear. Current TCM methodologies mainly rely on vibration signals, cutting force signals, and acoustic emission (AE) signals, which have the common drawback of requiring the installation of sensors near the working area, a factor that limits their application in practical terms. Moreover, as machining processes require the optimal tuning of cutting parameters, novel methodologies must be able to perform the diagnosis under a variety of cutting parameters. This paper proposes a novel non-invasive method capable of automatically diagnosing cutting tool wear in CNC machines under the variation of cutting speed and feed rate cutting parameters. The proposal relies on the sensor information fusion of spindle-motor stray flux and current signals by means of statistical and non-statistical time-domain parameters, which are then reduced by means of a linear discriminant analysis (LDA); a feed-forward neural network is then used to automatically classify the level of wear on the cutting tool. The proposal is validated with a Fanuc Oi mate Computer Numeric Control (CNC) turning machine for three different cutting tool wear levels and different cutting speed and feed rate values.
Subject(s)
Mechanical Phenomena , Neural Networks, Computer , AcousticsABSTRACT
Manufacturing processes are of great relevance nowadays, when there is a constant claim for better productivity with high quality at low cost. The contribution of this work is the development of a fused smart-sensor, based on FPGA to improve the online quantitative estimation of flank-wear area in CNC machine inserts from the information provided by two primary sensors: the monitoring current output of a servoamplifier, and a 3-axis accelerometer. Results from experimentation show that the fusion of both parameters makes it possible to obtain three times better accuracy when compared with the accuracy obtained from current and vibration signals, individually used.