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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 ‘classica- 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 amplied 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 dierent 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 signicantly 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 classication 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 congured 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 dierent 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 dened 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 articial 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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