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.
Highlights
The manufacturing industry has been a fundamental and highly relevant sector in the economic development of many countries [1]
In order to demonstrate the effectiveness and robustness of the proposed approach, through the fusion between the AC current sensor signal and the stray flux sensor signals acquired from the spindle motor of the computer numerical control (CNC) machine, the results are compared with a processing that considers the tool-wear condition using a single variable, which means that AC current and stray flux are analyzed separately
This work develops a novel methodology that uses the signal of AC current and the signals of stray flux coming from the axial, radial, and axial + radial directions from the spindle motor of a CNC lathe for detecting and classifying tool-wear condition under cutting parameter variation
Summary
The manufacturing industry has been a fundamental and highly relevant sector in the economic development of many countries [1]. Cutting tools are subjected to constant stresses, which leads to an imminent and gradual wear that tends to deteriorate the machining quality and cutting efficiency, no matter the tuning of optimal cutting parameters This situation may lead to the shutdown of the machine tool in severe cases, with approximately 20% of the production time being wasted in this kind of downtimes [6,7]. In this regard, it is essential to develop tool condition monitoring (TCM) systems and methodologies capable of extracting relevant information from the machining process and its different elements and signals in order to effectively correlate and diagnose the healthiness state of the cutting tool and achieve adequate maintenance actions. The feasibility of applying diverse methods in real operating conditions is directly affected and limited according to the sensors used and their characteristics [8]
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