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over 1°C. e hydraulic and electronic cabinets were temperature – controlled to ± 1°C. e ‘refractive index of the air’ had to be corrected – based upon a modi- cation to the Edlen (1966) equation, as the laser path positional monitoring system would otherwise be af- fected, with a correction factor being entered into the CNC controller. e ‘T-shaped base’ of the Nanocentre was sup- ported on three pneumatic mounts that were ‘tuned’ to eliminate oor-borne vibrations of ≥ 2.5 Hz. Two types of vibrational sources occur, namely forced- and self- excited, with the forced vibrations originating from external sources – through the foundations, while the self-excited vibrations normally being the result of in- ternal sources. A ‘oor vibration audit’ on the vibra- tional inuences was conducted, to establish whether the overall enclosure was suciently vibration absor- bent. e oor vibration spectra gave typical vibra- tional readings of 1 nm (rms) at frequencies of 25 Hz, during the tests, with the external air compressors emitting a oor borne 25 Hz frequency component, which had to be subsequently nullied. Further testing procedures were undertaken, including ‘modal analy- sis’ and ‘thermal imaging’ of the machine’s structure, together with a full calibration of the machine tool’s kinematics. Once all of these tests and various others had been completed and compensated for, then a machining testing program could then be undertaken. A typi- cal test piece is illustrated in Fig. 257b, where an al- uminium 6061-T6 part was heat treated and then stabilised, of φ250 mm copper-plated (200 µm depth coating) part. ese testpieces were faced-o with a monolithic diamond tool – taking very shallow D OC ’s of just a few micrometres, producing for example, a face-turned surface texture averaging ≈ 2.8 nm Ra. Later, proling tests were also conducted, prior to - nal operational acceptance by AWE, from the machine tool builder. Prior to completing this summation of the just some of the rigorous testing procedures carried out to ensure that the Nanocentre machine tool could oper- ate within the nanometric range of ultra-high machin- ing operation, it is worth making an unusual point concerning human intervention at this exacting-level of machining. It was found that when several person- nel were within the machine tool enclosure while machining took place, then the thermal output from these people, inuenced the part’s dimensional size – without actually contacting the machine, by simply acting as a heat-emitting source 73 . Moreover, it was also found that when diamond-turning 74 by facing-o a very ductile testpiece similar to that depicted in Fig. 257b, when these people were in conversation during the nanometric cutting of the part, their ‘voice signa- tures’ – in the form of air-borne vibrations were ‘ma- chined’ into the surface – in a similar manner to that of an acrylic recording of a record in the past! erefore, in order to ensure that both the human thermal eects and the vibrational perturbations (i.e. by air-borne vi- brations – talking), the personnel had to be removed while any ultra-precision machining operations were in progress. Ultra-precision machining at these nano-metric levels of operation, severely stretches today’s levels of technological challenges for: machine tools, me- trology, plant and equipment, as we approach that of atomic-levels of precisional uncertainties 75 . It is not just the case of purchasing an extremely accurate and 73 ‘Human body – as a heat source’. e average body – in a ‘sedentary state’ , will emit ≈100 W of heat. So, here in this case, when there are two people present in the machine tool enclosure, they will radiate ≈200 W of heat – inuencing the machine’s and hence, the workpiece’s thermal expansion – when machining at nanometric levels of accuracy and preci- sion. (Internet source: Burruss, R.A.P., Virtual People, 2005) 74 ‘Monolithic diamond’ , has some of the following characteris- tics: hardness of ≈8,000 Hv; Density (ρ) of 3,515 kg m –3 ; com- pressive strength of 7,000 MPa; and a Young’s modulus (E) of 930 GPa.(Source: Cardarelli et al., 2000) 75 ‘Atomic radius’ , for example, for some typical elements, ranges from that of: carbon, having an atomic radius of ≈0.071 nm (i.e. its atomic ≈φ0.142 nm)*, iron’s atomic radius is ≈0.124 nm (i.e. ≈φ0.248 nm, or ≈¼ nm)*, Aluminium’s atomic radius is ≈0.143 nm (i.e ≈φ0.286 nm, or >¼ nm), Cesium’s atomic radius is ≈0.265 nm (i.e ≈φ0.530 nm, or >½ nm).(Source: Callister, Jr. et al., 2003)*Slight digression here, may help explain why these atomic radii are important, when certain elements are alloyed together, such as iron and carbon, these being the main con- stituents of plain carbon steel.When an allotropic change oc- curs to the iron’s atomic lattice structure (i.e. BCC→FCC @ ≈910°C), then the carbon being somewhat smaller, can t into these (now) larger interstitial sites – voids – within the FCC iron lattice – distorting the adjacent iron atoms. Upon rapid cooling (e.g. by water quenching), some of the carbon is ‘trapped’ and severely distorts the structure as it attempts to transform back to the original BCC form. Hence, this dis- torted structure of iron-carbon – termed martensite, is both a very hard, but brittle structure, requiring tempering: if it is to act as a hardened and tempered workpiece material. is is the basis (i.e. somewhat simplied), behind this iron-carbon heat-treatment process. Chapter precise machine tool (Fig. 257a), and hoping to utilise it to machine when approaching nano-metric resolu- tion levels. ere are many oen interrelated factors that have to be considered and then dealt with, if one is to successfully operate at this ultra-precision level of machining operations. 9.11 Machine Tool Monitoring Techniques Introduction One of the most fundamental requirements for increas- ing productivity of CNC machine tools, is the ability to operate them ideally, in an untended manner, but at the very least, minimally-manned – whether they are ‘stand-alone’ machines, or part of a exible manu- facturing cell, or system (FMC/S). So, if an untended operation has been decided upon, then the absence of an operator will create a considerable number of problems that must be overcome, if the machining op- erations are to be satisfactory. ese problems arise in performing the monitoring and service functions that are usually seen by the operator, who would normally undertake the: monitoring of the cutting tool’s condi- tion and its performance; replacing worn, or defective tooling by interrupting the cutting cycle; assessing the workpiece quality during machining; changing speeds and feeds – if required; plus responding to unusual conditions that are either seen, or heard, during the cutting operation. While, in an unmanned machin- ing environment, the associated monitoring systems must provide the ‘articial intelligence’ (AI), necessary to ‘mirror’ the experience gained by a fully-skilled op- erator and their instinctive reactions and, to provide the type of expertise usually associated with human involvement. To cope with these every-day human- intelligence activities and their subsequent interven- tion during any machining operations, a considerable number of monitoring systems have been developed. In general, monitoring systems can be classied as: process-monitoring; workpiece-monitoring; machine tool monitoring; and tool-monitoring systems. Typical applications of these monitoring systems for untended operation on machine tools, include: • Monitoring the correct loading of the workpiece, correcting any set-up misalignments, or datum o- sets, while checking the quality of the workpiece, • Checking that the correct tools are available, by identifying both the tools and their setting osets, monitoring for tool wear and breakage and, initiat- ing tool replacements – as necessary, • Adjusting speed and feed as appropriate and, com- pensating for such eects as tool wear, thermal de- formation and chip congestion, • Monitoring of machine elements, including the CNC controller and taking any necessary correc- tive action in response to: program failure; diag- nostic error messages; etc. Whatever the function that is to be monitored, there is a need for some form of sensor to be incorporated into the system – to detect any problems as they arise, so that action can be appropriately taken, if necessary. us, a sensor’s output – triggered by an error mes- sage, must be processed to obtain the correct informa- tion, allowing decision(s ) to be made. e machine’s control unit, will then receive this ‘sensed’ result and initiate controlled actions to either correct, or re- cover the situation. Various types of monitoring and sensing systems are currently available for machine tools. Although, because this subject matter is so vast and sophisticated, only several of these monitoring techniques and sensing systems will now be consid- ered. .. Cutting Tool Condition Monitoring Introduction Whenever an operator is present during a machining operation, one of their major functions is to monitor the tool’s condition while the cutting continues, where they continually assure themselves that a tool in-cut is performing productively. e tool-related monitor- ing functions performed by an operator during any component’s manufacture, may be classied into four groups, briey these are: 1. Tool identication – this ensures that the correct tool will be used for a specic operation, with a va- riety of techniques being employed to achieve this crucial tooling activity. Techniques include the use of: touch-trigger probes (Fig. 133); non-contacting probing methods (Fig. 134); ‘tagged’ tooling of the contact (Figs. 116 and 117), or non-contact variet- ies (not depicted), Machining and Monitoring Strategies 2. Tool-oset measurement – of the cutting edge’s po- sition is necessary, in relation to that of the part’s datum point. is can be accomplished by the ‘probing-techniques’ and tool identication meth- ods mentioned above, 3. Tool life monitoring – is necessary to estimate the extent of a worn tool’s condition, which must be replaced prior to tool failure. e are a range of sensing devices available and they can be classied into two main groups: ‘Direct sensing’ – include: radioactive techniques; measurement of electrical resistance; optical observation of the wear zone; measurement of workpiece dimensional changes; or the distance between the workpiece and the tool post, ‘Indirect sensing’ – based upon either: temperature; sound; vibration; acoustic emission and force. is latter method can be measured and monitored either directly, by dynamometry (Figs. 178–180, 237 and 244), or indirectly via mea- surements of power, current, or torque – some of these techniques will shortly be discussed, 4. Tool breakage detection – can be monitored to en- sure that the cutting edge does not fail in-cut, as damage to both the tooling assembly and the work- piece may occur as a result. Once again, a variety of commercially-available techniques based upon force-related signals are available, including: those methods that use a dynamometer, either situated on the tool block, or in say, a turning operation be- low the tooling turret (Fig. 179); thrust-/feed-force sensors (Fig. 258); spindle-bearing/motor-current monitoring (not shown); power-/torque monitor- ing (Fig. 259a). is latter technique (Fig. 259a), is oen known as: ‘Torque-controlled machining’ (TCM). NB In order to fully appreciate the complexity and sophistication of any tool- condition monitoring, on CNC machine tools, the following section has been included. Tool-Condition Monitoring – With Feed-Force Sensors Modern microprocessor-designed tool-monitoring systems can be utilised for a variety of reasons, for example, to monitor the tool’s condition, or to reduce machining value-added costs. e advantages of using monitoring detection, are • Tool wear is monitored and tool changes initiated when necessary, so avoiding damage to the ma- chine, or workpiece, • If breakage occurs, a signal will be immediately produced to stop the machine tool – usually within milliseconds, • e system detects if a tool, or workpiece is miss- ing, thus eliminating wasted machine time and the likelihood of unpredictable crashes. While the cost advantages of using tool monitoring are: • Tool life can be optimised, meaning that the tools need to be only changed when they are worn – to a specied amount (Figs. 174 and 176) and so reduce the tool costs (Fig. 177e), • Down-time (i.e. here, it is normally associated with unanticipated wear rates, or tool crashes) is re- duced, which increases the machine’s output and as a result, improving cycle-time and costs per part, • Repairs to the machine tool and cutting tools may be reduced to a minimum, so the maintenance costs are lower, • e machining operation is automatically moni- tored, limiting any costly labour rates by subse- quent operator involvement. e above listed advantages for tool-condition moni- toring are quite an impressive recommendation, but how does it achieve consistent and accurate tool moni- toring, while simultaneously controlling the cutting process? ese questions will now be considered, deal- ing in the rst instance, with how the system monitors the tool’s performance during machining. A well known fact is that a tool will produce rela- tively high loads during a cutting operation, as it begins to wear. For eective ‘process monitoring’ it is important that the signal utilised should vary in a progressive manner as the tool wears and, not just at the time that it actually breaks. It has been shown (Fig. 258b) that during a machining operation, the axial force component (F A ) provides a better indication of the cutting edge’s condition as a function of tool wear, than the torque value (M). us, the increase in the axial force is more clearly dened – in both cases, from that of a sharp tool (Fig. 258b – le) to that of a worn tool (Fig. 258b – right). is change in the force gener - ated whilst cutting is instantly detected by a feed force sensor (Fig. 258a). e sensor transforms the force change into an electrical signal which is transmitted Chapter Figure 258. Tool-condition monitoring on a turning centre. [Courtesy of Sandvik Coromant]. Machining and Monitoring Strategies to the signal-processing device. Once the signal is re- ceived, the processing device can immediately initiate action by the machine’s CNC controller, if the tool is either: worn, broken, or not in-cut. is situation is all very well, but when should tool monitoring take place and, what action should result? In Fig. 258a (i.e. the inset diagram), the graphical depiction shows how continuous monitoring of the axial force can be used to triggered several alarm-states: • Level I – can be utilised to monitor whether a tool is in-cut, or not-in-cut, as the situation arises, mean- ing that either the tool, or component, or indeed both, are missing, • Level II – can be used to detect tool wear, with the alarm signal being used to initiate a tool change (i.e. to a ‘sister tool’) on completion of the operation, • Level III – can be utilised for tool breakage, with the signal being used to immediately stop the ‘feed- ing-function’ of the machine tool, when breakage is detected, • Tool crashes – a further level can also be employed for crash protection, which acts in a similar man- ner to ‘Level III’ , but this alarm immediately stops all motions and in so doing, protects the machine tool 76 . In Fig. 258c, the schematic diagram illustrates typi- cal monitoring positions on a two-axis turning centre, showing potential sites to place the sensors, such as on the ball-screw nuts of the recirculating ballscrew as- semblies, for both the X- and Z-axes. Not shown here, but normally also tted is a current sensor. us, these signals are continuously monitored by either a single- or multi-channel control unit, as will be the control signals from the machine tool’s CNC controller. Any alarm signals triggered, being passed back through a closed-loop to the machine’s control unit for appropri- ate action to be taken, or indeed if any. e function of a typical commercially-available multi-channel signal processing unit, might be to: • Sense – then process tool-cutting information from signals at the various sensors and sites for the mul- tiple channels of the unit, 76 ‘Tool breakage detection times’ , it is possible to vary the reac- tion time, which is usually between: 0.1 to 1 second, but for any form of tool breakage a shorter reaction time is desirable, typically ranging from: 1 to 10 milliseconds. • Learn – by automatically memorising the signal values obtained from the sharp cutting tools, whilst in this ‘learn-mode’ 77 , • Stores data – for a signicant number of cutting operations per channel in its memory for each cut- ting operation, as well as automatically setting the appropriate levels for each alarm signal from its memory, • Reacts – by sending alarms to the machine’s control unit, informing it if the tool is either: worn; broken; or not-in-cut, • Coordinates – automatically, machining and moni- toring on commands from the machine’s control unit, • Adapts – to the particular machine and its cutting environment: once installed and programmed to suit the machine tool, with the setup parameters being modied to adapt to any further machining requirements, • Communicates – between the operator and the machine via the control panel, informing the oper- ating personnel about cutting tool conditions and providing an interface for control of all functions. In Fig. 258c, this line-diagram depicts a typical turn- ing centre application of tool condition monitoring. e machine is controlled on two axes, with sensors on the feed-drive bearings of both the X- and Z-axes. A representative nominal force for these sensors is 40 kN, but this rating will depend upon the end-user’s re- quirements. e sensors can be designed for tapered, or angular contact bearings, or for a combined axial and radial bearing application – suiting the particular machine tool. When tool monitoring is needed for a four-axis turning centre, two tool monitoring units are usually needed, since each turret (i.e. to and bottom) can be operated both independently. e key elements in any tool condition monitoring situation are the sensor’s position and its design. For universal instal- lation on a variety of machine tool congurations, the positioning of the sensing devices is usually on the re- circulating ballscrew nut assembly. 77 ‘System-learning’ , in the past this was somewhat a basic of functional performance, but with the advent of ‘articial neural networks’ , they have an ‘AI-ability’* to ‘mimic’ human involve- ment and react to their environment – once ‘trained’. More will be said on this ‘AI-topic’ in the nal section of this book. * ‘AI’ is a term that is normally utilised when some form of ‘arti- cial Intelligence’ is employed in the decision-making process. Chapter Much more could be said concerning the informa- tion on their: operational setup; range; and adaptabil- ity; for these tool condition monitoring systems. In the interests of brevity, the reader should look to the manufacturers of such equipment, or the references and available literature for more specic in-depth in- formation. .. Adaptive Control and Machine Tool Optimisation Adaptive Control Adaptive control systems have been utilised since their introduction in the 1960’s, where their operational performance and reliability was somewhat dubious, because of the type of sensors utilised, the speed of signal-data processing and their installation on the machine tool. Many of these early systems attempted to undertake many functions simultaneously and were oen termed; ‘adaptive control optimisation’ (ACO), but due to the problems mentioned above, they were somewhat unreliable and as such, fell out of favour. Later, a more pragmatic approach to adaptive control constraint (ACC) was introduced called: ‘torque-con- trolled machining’ (TCM), which oered a simpler termed: ‘feed-only system’ – with a typical system be- ing depicted in Fig. 259a. us, the operation of a TCM system, involves unique sensory circuitry and compu- tation methods that measure the net cutting torque, then compares this value obtained, to that of the preset torque limits – these previously being established for the cutting tool and workpiece combination utilised. e appropriate control actions, namely, a feedrate reduction is then automatically taken, whilst keeping within the maximum torque and power limits of the spindle motor. If a condition arises where the feedrate falls below a preset limit, a new tool (i.e. sister tool) is called-up to complete the machining operation. is feedback-loop in which continuous monitoring by the sensors and updating the machine control unit – using adaptive control, produces optimal cutting conditions for the tool and workpiece combination. Adaptive control via TCM (Fig. 259a), basically op- erates in the following manner. Prior to its activation and if for example, a variation of stock was present for roughing operation with a large face-mill. e unpre- dictability of the height of this stock if a TCM system was not activated, might otherwise over-load the cut- ting edges, possibly causing damage to the: cutter as- sembly; workpiece; or even the machine tool. Once the TCM has been correctly activated and preset to a torque limit, then if the D OC is large, the control sys- tem senses a torque increase and simultaneously the feedrate over-ride is initiated. is over-riding of the programmed feedrate decreases the feed for this large D OC , it will then increase as the D OC lessens, or rapidly move over an ‘air-cut’ , thus producing optimal cutting tool protection and eciency as the chip-load is more uniform, regardless of the variable D OC ’s. Even if there is no discernible dierence in the relative height of the D OC taken, but the bulk hardness of the part may vary by up to 300% in some cases, machining with the TCM activated will protect the tooling. So to mention the some benets to be gained from TCM, they include: extended tool life; optimised feedrates – without the risk of tool damage; higher throughput of machined parts; tool breakage minimised; quicker setup times; and reduced operator intervention. Obviously very small diameter tooling, may not respond to the torque demands so readily, but for most machining opera- tions and tool/workpiece combinations the system has distinct benets to the overall machining production process. To summarise the principal benets of utilising some form of adaptive control system, they are: • Main spindle motor is protected from overload, • Damage to the cutter and to the expensive value- added workpiece are protected, • Optimal stock removal rates are possible, under steady-state machining conditions, • Using a constant: cutting power; cutting force; and feed force; optimises tool life, • If unpredictable air-gaps occur – whilst cutting, the fastest tool travel is utilised, • Where workpiece hardness signicantly varies, tool edges are protected by adjustments of the chip- loads, • Where an operator’s experience, or the program’s eciency may dier for varying cutting operations, the adaptive control system eliminates this ‘techni- cal gap’ , • ere is no over-shooting of the permitted cutting power during re-entry into the workpiece material whilst machining the part under regular condi- tions. Costs vary the for ‘post-installation’ of adaptive con- trol systems to CNC machine tools, but at today’s prices they range from: $ 9,000 to $ 15,000 (US). How - ever, once installed they last the life of the machine Machining and Monitoring Strategies Figure 259. Either use: adaptive control or CNC program optimisation – for variable tool path trajectories. Chapter tool, giving a superb pay-back on the original invest- ment, when one considers the major benets listed above. Machine Tool Optimisation If a company has signicant numbers of CNC machine tools in their manufacturing facility, then it may not be feasible to introduce an ‘adaptive control’ system across all of these machines – despite the positive merits described above, simply on nancial grounds alone. Under such circumstances, perhaps a ‘soware- approach’ by simulating the cutting operations to the problem of machining optimisation, may be the way forward? Some companies oer CNC programming optimisation packages that are based upon literally thousands of ‘man-hours’ of development and rene- ment (i.e. Fig. 259b, shows a very sophisticated version of such a tool verication and simulation system). ese simulation systems are oen part of a larger: op- timisation; verication and analysis product that can be ‘tailored’ to suit a machining company’s product range and manufacturing output. ese ‘knowledge- based’ systems of the machining process, via previous simulation, know the exact: D OC ; width of cut; and angle of cut (i.e. for cutter orientation, when prol- ing); for the machining process under consideration. Further, the system also knows how much material is to be removed by each cutting edge, as such, the system also has information on the tooling available from the magazine, therefore it selects correct tool and assigns to it the optimum feedrate. Moreover, once this information has been established for the new tool, it outputs the tool path – which was identical to say, that of the original tool, but now having signicantly improved feedrates, although the system does not alter its trajectory. While setting up the system, it is usual for such soware (Fig. 259b) to prompt the user for cutter set- tings as the part simulation occurs, by in essence, add- ing the user’s intelligence to that of the cutter’s opera- tion. With these systems it is usual to have all cutter settings stored in an optimisation library, thus the user only has to dene the setting once. While, the more sophisticated systems nd the maximum volume re- moval rate and chip thickness for each tool, then it employs them to determine the optimisation settings for that tool. In optimised roughing-out, the objective here is ob- viously to remove as much stock material as possible in the fastest time. Conversely, for nish-machining, chip-loads may vary considerably, as the tool proles through the workpiece material that was le behind during previous roughing cuts over the contours – to near-net shape. By optimising the tool’s path, the soware adjusts the feedrates to maintain a constant chip-load 78 (Fig. 259b). is cutter optimisation will improve the tool life and give an enhanced machined surface nish to the component. is fact is especially critical when ‘tip-cutting’ , with either a ball-nosed end mill (Fig. 247b), or contouring over a surface with a small step-over, such as when semi-nishing, or n- ishing a steel mould cavity (Fig. 249b). Summarising the advantages of utilising a simu- lated optimisation cutter-path soware package, such as the one in Fig. 259b which only illustrates some ba- sic and simple tool paths. us, cutter-path optimisa- tion oers the user the ability to: • Machine more eciently – cutting more parts in the same amount of time, by signicantly reducing the machined component’s cycle-time, • Reducing part cost thereby saving money – increas- ing productivity by reducing the time it takes to cut parts, will become a signicant saving per annum, • Improving part quality – by minimising the con- stant cutting pressure, thus reducing cutter deec- tion, with nished corners, edges and blend areas, needing less subsequent hand-nishing, • Cutter life improved – because of optimised cut- ting conditions are used, which prolongs tool life. Moreover, with shorter in-cut time, this results in less tool wear, also having the benet of reducing down-time to change inserts, or tooling, • Reduction in machine tool wear – as a more con- stant cutting pressure between the machine tool and the workpiece reduces variable forces on the axis motors, giving smoother machine operation, • Utilises time available more eectively – allowing machinists to operate several CNC machine tools, or setup the following job, etc., as they do not have to be constantly ready to reduce/increase the ma- chine’s feedrate over-ride. By investing in suitable simulation and optimisation soware of the tool’s path, enables a company that is currently involved in a considerable amount of ma- 78 ‘Constant chip-loads’ , are normally recommended by cutting tool manufacturers, as they reduce the eect of ‘chip-thinning’ somewhat. Machining and Monitoring Strategies chining activities to become very cost-eective and ecient when compared to their direct competition, both nationally and internationally. One could cer- tainly ask the question, under these circumstances just mentioned: ‘Can a company aord not to be using such soware, if their main competition – both here and abroad have it available now?’ .. Artificial Intelligence: AI and Neural Network Integration Introduction Over the past decade and a half, some signicant ad- vances in machining materials have occurred, while complementary progress has also been made in the machine tool’s CNC controllers, coupled to their faster micro-processor speed and additional technological renements. Many of these machine tools are inte- grated into fully-automated systems machining lines – for volume part production purposes, or into ex- ible manufacturing cells/systems (FMC/S) – allowing scope for mixing batch sizes and perhaps employing a ‘Group Technology’ (GT) approach (i.e. see Footnote 24, Chapter 6). So that the full potential of these ma- chine tools can be exploited, it is exceedingly impor- tant that production processes are both monitored and controlled in an ‘intelligent manner’. Previously, when little cutting data and minimal tooling-related behaviour had been established for a new production run, it was necessary to instigate some form of tool measurement procedure. So, aer operat- ing a cutting tool for an extended time-period in-cut, so that the tool’s wear pattern (Fig. 174) had begun to reach the end of its productive life (Fig. 176), it was necessary to exchange it for a new tool. is arbitrary tool-changing strategy was at the discretion of the op- erator, therefore it relied upon their past machining experience to decide when it was advisable to instigate the necessary down-time – for this tooling-related ac- tivity. An alternative approach, was to employ some form of condition monitoring procedure, by utilising o-line direct measurements to ascertain the amount of wear that had occurred so far. is assessment ac- tivity entails a certain degree of operator competence in a variety of disciplines, because the cutting tool’s inspection required microscopical analysis by metro- logical/metallographical techniques to determine the current status of the tool’s cutting edge(s). is tool- ing investigation necessitated that the tool be at rest and out-of-cut, so that its life could be correctly estab- lished, which can be a costly and time-consuming pro- cess, diminishing the cost-eectiveness of the overall production process. One machining strategy that can be used to over- come most production deciencies, is to have some form of on-line, indirect system, which has the ad- vantages of being benecial in terms of: improved running costs; enhanced component quality; and e- ciency in production performance. In order to achieve such benecial tooling-related and part production enhancements, it is necessary to utilise some form of ‘on-line tool condition monitoring’. So that this tool monitoring objective can be successful, a number of hard- and so-ware activities must be undertaken, then integrated into a usable ‘workshop-hardened’ instrumental package. In the early-to-mid 1990’s a novel approach to this problem, but also included the some distinct renements by: ‘on-line tool condition monitoring – using neural networks’ was developed by Littlefair et al. (1995). is fundamental and applied research work was fully-supported by a range of in- dustrial companies, it was later also installed at sev- eral widely-diering manufacturing companies. In or- der to comprehend the complexity of such an on-line tooling related activities, the following case-study has been included (Littlefair, et al., 1995), as it succinctly describes the hard- and so-ware issues that had to be overcome. .. Tool Monitoring Techniques – a ‘Case-Study’ e technique of tool wear monitoring can be classi- ed in two distinct manners, these are by either: • Direct monitoring – produce accurate results, but they are dicult to fully-implement in a shop-oor environment, • Indirect monitoring – considers various parameters which change as a result of increasing tool wear. e latter tool monitoring strategy was utilised in a single-point turning operation on a CNC turning cen- tre, by incorporating: tool force; vibration; and acous- tic emission; by being integrated into a neural network; and this theme will now be mentioned. Each of these monitoring systems will be briey described, plus the neural network – appropriate for complete sensor-fu- sion, will then be described. Chapter Tool – Force Monitoring In single-point turning, if one ignores the orthogonal cutting condition, then for oblique cutting three re- actionary forces are experienced by the tool, termed: tangential; axial; and radial force components (Fig. 19a). e tangential force is generated due to the workpiece’s rotation, this being by far the greatest of the three forces. An axial force component is the re- sult of the applied feed force, while the radial force is a function of, in the main, the inclination of the ap- proach angle and to a lesser extent inuenced by that of the tool nose radius – this radial component being the smallest of the forces. Each of these component forces in oblique cutting are inuenced by a range of factors, such as: workpiece material and its condition; D OC ; tool cutting insert geometry; and cutting data utilised – speed and feed. In this case, a special-pur- pose holder for a platform-based dynamometer was manufactured (Fig. 261a). Tool – Vibration Monitoring In machining processes, the onset and subsequent development of vibration orginates from the overall dynamic behaviour of the tool-workpiece-machine system. e anticipated vibrational causes can be both cyclic in nature – resulting from changes due to com- pression and sliding of the workpiece material in the shear zone, and, changes in the frictional conditions in the contact zones – between the tool and workpiece. So that vibrational inuences during continuous cut- ting could be monitored, accelerometers tend to be utilised. Normally, accelerometers are situated as close to the cutting edge as possible, usually at a convenient position on the toolholder. e vibration parameters monitored are usually related to either the toolholder’s natural frequency, or the frequency of chip segmenta- tion. Moreover, it is also possible to eectively utilise that of a dynamometer’s ‘force signal’ for indirect vi- bration monitoring. Tool – Acoustic Emission Monitoring Acoustic emissions (AE) are those high-frequency stress waves generated due to the spontaneous energy release in materials undergoing: deformation; fracture; phase transformations; etc. us, AE signatures can be divided into two distinct types: continuous – contain- ing low-amplitude and high-frequency signals (i.e. in the range: 100 to 400 kHz); burst – containing higher amplitude and lower frequency signals (i.e. in the range: 100 to 150 kHz). By the application of Fourier transforms coupled to that of statistical analysis-based techniques, it is possible to utilise both of them for the analysis of AE signals. e root-mean-square (rms) value has been shown to produce an increasing trend with increased amounts of tool ank wear, further, the combination of both skew and kurtosis of the AE signal will also indicate a correlation with ank wear rates. Tool – Sensor Fusion and Multi-sensor Integration e application of multiple sensors can be eectively- employed in a complex tool-wear monitoring system for machining environments, to obtain harmonizing information about the turning production process. is multi-sensor monitoring acts to rearm the ‘con- dence factor’ , when dealing with the prospective di - agnostics from the single-point turning process. How- ever, the exercise of utilising multiple sensors, entails integration and fusion of the sensory information, to extract the essential features from the data, by remov- ing the ‘redundancy’ present in this data. In this re- gard, the application of articial neural networks, can provide the solution to the sensor-fusion and auto- matic decision-making processes for this tool-condi- tion monitoring system. Artificial Neural Networks (ANN) Articial neural networks (Fig. 260a), are composed of many simple processing nodes which operate simulta- neously. ese ANN’s mimic the functional behaviour of biological neural network systems, allowing them to be utilised to integrate and fuse information from multiple-sensor sources. e functional behaviour of the overall system is primarily determined by the pattern of connectivity of the nodes (Fig. 260a). As a system, ANN’s are capable of performing some high- level functions, such as: adaptation; generalisation and target-learning. ese capabilities are particularly rel- evant for any form of tool-wear monitoring applica- tions. e advantages of employing ANN’s to integrate and fuse data, are their inherent capabilities to: adapt to instructed environments; robustness to noise; fault tolerance; simultaneous processing; and feasibility of on-line realisation (i.e. via hardware implementation). Possibly the most widely used ANN and the one reported in this section, is that of the ‘multi-layer per- ceptron’ type, which uses an ‘error-back-propagation Machining and Monitoring Strategies [...]...540 Chapter 9 Figure 260. Neural network architecture and tool condition monitoring system [Source: Littlefair, Javed & Smith, 1995] . both the tools and their setting osets, monitoring for tool wear and breakage and, initiat- ing tool replacements – as necessary, • Adjusting speed and feed as appropriate and, com- pensating. tooling of the contact (Figs. 116 and 117), or non-contact variet- ies (not depicted), Machining and Monitoring Strategies 2. Tool-oset measurement – of the cutting edge’s po- sition is necessary,. transmitted Chapter Figure 258. Tool-condition monitoring on a turning centre. [Courtesy of Sandvik Coromant]. Machining and Monitoring Strategies to the signal-processing device. Once
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