Surface Integrity Cutting Fluids Machining and Monitoring Strategies_11 pdf

algorithm’ – in its ‘learning phase’ (Fig. 260a). Funda- mentally, this network operates in the following man- ner: the input layer neurons transmit signals via the hidden layers to the ouput layer. At this juncture in its operation, the desired and actual outputs are com- pared to evaluate the system error – usually Euclidean error. Hence, this error value is employed to adjust the strengths of the connectivity amongst the network’s neurons. e algorithm utilised to perform this un- dertaking is normally known as ‘error-back-propaga- tion’ , which is available in many forms. e hidden layer’s nodes have their capabilities developed during ‘training’ , this is achieved in such a manner, that the extracted features are better suited for the ‘classica- tion task’. Tool – Condition Monitoring System e ANN described above, was utilised on a two- axis slant-bed turning centre (Fig. 261b), which was equipped with sensors and associated equipment al- lowing on-line data capture, during a comprehensive run of machining trials. ree sensors were utilised in this work for monitoring the cutting process, t- ted onto a specially-manufactured platform, situ- ated on the tool turret (Figs. 261a and c). e three sensors were a: Kistler force dynamometer (model: 9275B) – which sensed/measured the cutting forces in three perpendicular axes (i.e. X, Y and Z); AE sensor – Physical Acoustics (type: WDI); vibrational sensor – Vibrometer (type: CE501 M101) miniature accelerometer. e force and acceleration signals were amplied and then sampled at 50 kHz, while the AE sampling was undertaken separately by a digital stor- age adaptor at a sampling rate of 1 MHz. All of this information was then stored on a ‘suitably- fast’ PC. e schematic layout of the monitoring hardware is illustrated in Fig. 260b. Prior to utilising by the neural network, a pre- processing procedure was operated to reduce what is termed its ‘dimensionality’ of the signals. is action was achieved by computing the ‘power spectral densi- ties’ of the captured time-domain signals and equally dividing the resulting spectrum into eight discrete fre- quency bands. is number of bands has been shown to be the optimum in terms of reducing dimensional- ity, yet maintaining information integrity. e multi- layered perceptron type of neural network was also utilised to further process data and subsequently cor- relate it with dierent cutting tool wear states. is correlation was achieved by integrating and fusing the data components in order to remove redundancy of sa- lient information in the data. So, depending upon the particular application, one channel of force was com- bined with the acceleration and AE channels, in order to create a 24-by-1 input vector for the neural network. In a practical example of its use, for a plunge-grooving operation, this system gave signicantly more detailed information in the axial direction, whereas, in simple longitudinal outside diameter turning, the tangential force was found to be more useful and as such, appli- cable. e neural network architecture used in these tool-condition monitoring machining trials, consist of an: input layer (24 nodes); a single hidden layer (10 nodes); with and output layer (3 nodes). is particu- lar ‘nodal-architecture’ was derived through arbitrary experimentation, with ve and een nodes in the hidden layer increasing the ‘convergence time’ with no appreciable improvement in tool wear classication performance. Finally, the output layer provided ve tool wear categories 79 which depended on the particu- lar machining application. e tool-condition monitoring system – once de- veloped at Southampton Solent University, was tted onto a two-axis at-bed turning centre at the Atomic Weapons Establishment (Aldermaston). e system proved to operate successfully at both establishments, which was congured to; capture cutting data; reduce and process the sensor data; apply the ‘previously-con- gured’ neural network, then correlate all of this in- formation into dierent tool wear states – in a mean- ingful manner. By utilising these ‘AI – neural networks’ , they have the potential to evolve into an: adaptive on-line fee- drate control system, which could be integrated into the tool-changing system for a CNC machine tool. Moreover recently, similar sensors have been utilised to monitor the machining process and data fusion/ analysis, through the application of neural networks and/or fuzzy-logic-based techniques. 79 ‘Tool wear states’ , were achieved by the system output gener- ated in the form of a [1 x 3] output vector, which is a binary encryption of ve wear states – being dened as follows: 000 – minimal ank wear; 001 – minor ank wear; 010 – major ank wear; 011 – minor tool damage (chipping); 111 – major tool damage (chipping). Machining and Monitoring Strategies  Figure 261. Tool condition monitoring using articial neural networks. [Source: Littlefair, Javed & Smith, 1995] .  Chapter  In the relatively near-future, the application of AI to the tasks involved in unmanned machining will increase, along with potential cutting speed and feeds. 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