Tool condition monitoring for form milling of large parts by combining spindle motor current and acoustic emission signals

Abstract

To ensure overall quality of a precision large-scale component, a tool condition monitoring (TCM) technique for multi-step form milling is presented. The form milling of fir tree slots for a steam turbine rotor is an appropriate example that requires a fine surface finish and high dimensional accuracy. Therefore, we propose a novel TCM system based on a multi-sensor fusion strategy which utilises the combination of spindle motor current and acoustic emission (AE) as well as adaptive thresholding for multiple manufacturing steps (roughing, semi-finishing and finishing). To investigate the tool deterioration process, tool longevity tests using a test piece are carried out for each step. With the aid of qualitative inspection, it is found that AE signals provide comprehensive tool state information regarding tool flank wear, crack propagation and severe adhesive wear. In addition, by intentionally adding a bundle of chips to the surface, bursts of AE of large amplitudes occur in finishing, which provides the possibility of discovering anomalous events related to surface quality. By careful consideration of such characteristics, provisional alert levels are determined using a two-dimensional diagram with respect to both sensors. The strategy is verified throughout the actual manufacturing processes of the rotors. The proposed TCM system shows not only an excellent ability to prevent catastrophic tool failure and surface irregularities in form milling but also acceptable expendability for various groove specifications.

Investigation of Milling Stability under Cutting Fluid Supply by Microphone Signal Analysis

“…The trends toward the unstable state of three cutting modes (roughing/semifinishing/finishing) are much different. A similar study was reported by Uekita and Takaya, (26) but the signals for the x-and y-axes are the AE signal and cutting force, respectively. In this work, SDTD was employed to observe the sound strength variation due to the variation in milling dynamics.…”

Tooling systems with integrated sensors enabling data based process optimization

“…Thus, the measurement of vibrations [20,21] and accelerations [22] for the detection of unfavorable machining-or process conditions, tool wear or even a crash become possible. Another advantage of sensor integration into tools can be found in a rather direct transmission behavior with little influence of other machine parts in the vicinity of the process compared to other methods like monitoring the motor current or the acoustic emission characteristics of the process [23,24]. At locations rather remote to the actual process, thermally induced changes in machine behavior [25], mechanical coupling and external disturbances easily play a major role when process conditions are measured.…”

Tool Condition Monitoring in Milling Process Based on Multi-Dource Pattern Recognition Model

“…Now more popular works are carried out based on signal processing and data mining. [15,16] On the one hand, it bene ts from previous research, scholars have studied a variety of signals that can re ect condition changes in tool wear process [17][18][19]. On the other hand, the tool wear state recognition method based on signal data has been proposed gradually [20][21][22][23][24].…”