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Systems for the prediction of process parameters
Journalof
Materials Processing Technology ELSEVIER
Journal of Materials Processing Technology 54 (1995) 64-69
Systems for the prediction of process parameters V. Narayanan Information Technology Institute, National Computer Board, Singapore, Singapore
Received 1 April 1994
Industrial summary
Manufacturers of machine tools, cutting tools and electrodes assist users by providing some form of performance data with their products. However, production engineers do not have resources and time to study manuals or performance charts and do detailed analysis to predict the performance of the system for the chosen operating parameters. Thus, an attempt has been made to develop a performance-prediction system for operating parameters such as machinability data selection, which will assist process planners and machinists in their decision-making processes. A back-propagation neural-network model is proposed for prediction. The use of traditional empirical models and expert systems for machinability studies is also discussed. The proposed method will also help in interpolating/extrapolating collected factual data from the shop-floor, for different machining conditions.
1. Introduction
Machining processes can be divided broadly into traditional and non-traditional processes. The traditional machining processes such as turning and milling remove material by chip formation, abrasion, or micro-chipping. There has been a rapid growth in the development of harder and difficult-to-machine materials. In some instances, the workpiece is too flexible, slender, or delicate to withstand the cutting forces. The shape of some parts are complex and sometimes the surface finish and tolerance requirements are stringent. These requirements have led to the development of non-traditional processes such as Electrical discharge machining (EDM), Ultrasonic machining and Laser-beam machining. These methods are unconventional in the sense that conventional tools are not employed for cutting; instead energy in its direct form is utilised. These unconventional methods can not replace the conventional machining processes and their usage depends on the work material and product requirements. For turning, the first requirement is that the tool must be harder than the material itself. The tool and the material must be held rigidly so that the tool can penetrate the workpiece and cut efficiently. Different cuttingtool materials require different tool geometry for optimum efficiency of the operation. Additional studies and research will be necessary as new tools and materials are developed. Despite the extensive work that has been conducted in this area over the past three decades, most of the developments in cutting have evolved through
0924-0136/95/$09.50 © 1995 ElsevierScienceS.A. All rights reserved SSDI 0 9 2 4 - 0 1 3 6 ( 9 5 ) 0 1 9 2 1 - Z
trial-and-error methods and are learnt on an empirical basis. The main objective is to satisfy the demand of the component dimensions and its surface finish. In order to achieve these results, the machinist is left with a wide range of possible speed, feed, depth of cut and other machining parameters (Fig. 1). Similarly in the case of spark erosion or electrical discharge machining, the machinist has a wide range of input parameters such as pulse current, gap voltage and pulse-on duration, to choose from. Machining conditions selected for economical manufacturing are based on various criteria such as maximum profit rate, minimum cost, maximum production rate, minimum scrap and maximum tool life [1]. The application of machining economics to shop practice has been limited and the correct choice of machining conditions is normally overlooked, the reasons for which are: (i) shortage of reliable tool life and cost data; (ii) difficulty in using various bulky manuals and handbooks; (iii) difficulty in updating machinability data; (iv) difficulty in understanding these complicated criteria and associated equations in practice; (v) difficulty in processing a large amount of other related data, which have to be extracted and processed; (vi) tedious interpolation and extrapolation of data to suit different machining conditions; and (vii) tedious calculation of optimum machining conditions. The solution to the above problems is a computerised machinability-data-base system which can extract data rapidly from data bases and perform tedious calculations [2]. Whilst the experience of the machinist plays a very
V. Narayanan / Journal of Materials Processing Technology 54 (1995) 64-69 f
65
Iflvut or
~ Toolmaterial Speed ~ Feed Depth of cut
Toolgeometry
Turning process
Qutput Toollife Surfacefinish Component dimensions
]
Intmtor Puetrm Pulse-off-duration C Vol El Machinin~ lscteta-rge oode n-duratiomat nConditions eria-l- - I ~ - [
Electrical discharge machining
Outlmt Surfacefinish Componentdimensions
Depthofdamagedlayer Surface crack density
Fig. 1. Schematic diagram of the turning and the EDM process.
important role, much of these machining knowledge have actually been captured, formatised and made available in the form of machining data books, data sheets and charts, nomograms and slide rules [3,4]. In this paper, development of a customisable PC-based system for predicting the performance of turning and electrical discharge machining operations is discussed.
Fig. 2. Scanning electron micrograph of the surface microstructure of AISI OI 15A and 300 ms.
where X and Y are independent-and dependent-variables and A, B, C are constants and exponents.
2.2. Electrical discharge machining (EDM) 2. Machinability models and empirical relations
2.1. Turning Empirical models establishing relationships between different variables involved in turning processes are developed based on Taylor's equation. For example, tool life and surface finish can be expressed in terms of speed, feed, depth of cut and nose radius as in Eqs. (1) and (2), the constants and exponents depending on the hardness and machinability rating of the work materials, tool materials, types of coolant, tool geometry and other operational parameters. The constants and exponents can be determined from the actual data by statistical methods, such as regression analysis. Tool life = K1 (Speed) "1 (Feed) "2 (Depth of cut) "3, Surface finish = K2(Feed) "4 (Nose radius)"L
(1)
where K1 and K2 are constants and nl, n2, n3, n4 and n5 are exponents. The non-linear model may be linearised by taking natural logarithms and can be reduced to the form indicated in Eq. (3). To better represent the observed data, the polynomial model for the variables as well as the cross-product terms are used to detect the interactions between the effects of the variables as in Eq. (4). Though these methods give some representation of the actual cutting process, practitioners find it very difficult and tedious to use them. In Y = A + BIn Xi,
(3)
In Y = A + Bi In Xi + Cij In Xi In X j,
(4)
EDM has become one of the most widely used means of producing tools and dies. There are, however, a number of problems associated with the use of EDM, principal amongst which is the surface integrity after machining. If surface damage such as cracks, residual stresses and recast layer are not attended to, the effective tool life may be reduced severly. Thus, it is necessary to estimate the surface damage that has to be eliminated. Some of the input parameters are: (i) gap voltage (V in volts); (ii) pulse current (1 in amperes); (iii) pulse-on duration (ti in micro-seconds); and (iv) pulse-off duration (to in micro seconds). The pulse energy (E in milli joules) is given by Vlti. Generally, it has been observed that metal removal is predominantly thermal in nature. The rapid cooling rate causes surface defects such as cracks, plastic deformation and the build-up of residual stresses. The microstructures of the surfaces of tool steels, namely AISI O1, A2, D2 and D6 were examined [5]. Generally three distinct layers can be identified as in Fig. 2: the outermost layer, an intermediate layer and the unaffected parent metal. For tool steels, the top-most surface layer is an uneven, nonetchable layer often referred to as the white layer. Immediately beneath the white layer is an intermediate layer where the heat is not sufficiently high to cause melting but is sufficiently high to induce micro-structural transformation in the material. There is a need to quantify the depth of the white layer (dw in microns) and the thickness of the overall recast layer (d t in microns) [5-7]. Electrical discharge machined surfaces are covered with cracks, the extent of cracks on the surfaces of AISI D6 tools steels machined at a pulse energy of 75 mJ being
66
K
Narayanan / Journal of Materials Processing Technology 54 (1995) 64-69 Table 1 Empirical relationships
kl
kz nl nl
/; AISI i)2
AISI 1)6
0.1 nun m
Fig. 3. Tracing of the cracks as seen on the machined specimen surface of AISI D2 and D6 steel. shown in Fig. 3. The surface crack density (de in mm per mm 1) can be defined as the total length of cracks per unit surface area. Cracks are rarely found to extend beyond the white layer, so that the maximum depth of damage is given by the thickness of the white layer. In the case of EDM, as also in other unconventional machining processes, the cause and effect of the input parameters on the objective functions is very confusing to the machine operator. Thus, it is necessary to relate surface damage to a parameter such as the mean surface roughness value (R,) which can be measured easily. It is also important to know how the thickness of the white layer is affected by process parameters such as pulse current and energy. The thickness of the white layer can be expressed as a function of the input pulse energy (Eq. (5)) and surface roughness (Eq. (6)). The values of the constants and exponents based on experiments for AISI O1, A2, D2 and D6 steels are given in Table 1. dw = k~ E"',
(5)
dw = k2 R"~2,
(6)
In actual practice, it is very difficult to develop empirical equations for different materials machined under different conditions, thus a customisable system to predict performance based on observed data is proposed. 2.3. Knowledge bases
Though the process of machining appears to be simple, research done on the study of the mechanics of metalcutting processes is unable to answer many basic questions and the physical laws governing the machining processes are still unclear. Models have been developed based on the machining of solid blocks, rigidly clamped and machined under ideal conditions. The term "machinability" has no scientific definition. Tool-life scatter is inherent even in well-controlled laboratory tool-life tests.
AISI Ol
AISI A2
AISI D2
AISI D6
3.974 6.98 0.30 0.48
4.345 5.94 0.29 0.59
4.254 7.52 0.30 0.43
4.046 6.76 0.43 0.49
There are always variations in the mechanical and metallurgical properties such as the chemical composition and the microstructure of the materials [8]. Thus, machinability data are only a set of reasonable recommendations for operational variables such as tool materials, feeds, speeds and cutting fluids. Cutting data from hand-books therefore cannot be used as they are, and have to be supplemented by rulesof-thumb/heuristics used by expert/experienced machinists. Rules have to be collected, validated and converted into computer readable form. Examples of these rules are: 1. If a machine is very new, use 80% of recommended speed. If it is one year old, use a factor of 0.75 to calculate the cutting speed. 2. For milling, the surface should be dry. 3. If you mill thin plates and use a negative rake angle, thin plates will bend. Increase the clamping forces by 50%. 4. Use down-milling for milling pockets. If up-milling is used, give tool compensation. 5. When machining a mould, the last cut should be down-milling. 6. Upmilling should be used when machines are old or not very rigid. 7. If the tool material is tungsten, ceramic or diamond, use continuous cooling or no coolant at all. There should not be any intermittent cooling. 8. Do not use oil coolants for cuts of less than 0.1 mm. For finishing, oil should not be used. 9. If you do not have automatic feeding and a ball spindle, use up-milling. These rules will also take into account a few anomalies. An example is that it is easy to machine a 10 mm diameter aluminium bar at 1000 rpm. However at 3000 rpm, the tool life will be very much reduced and the end results will be very poor, whilst at 30000 rpm, the chips will fly away very quickly and the machinability will be very much improved [2].
3. Neural net methods
The available models discussed in the earlier paragraphs are derived based on laboratory-generated data under the best possible conditions. In practice, the data
K Narayanan/Journal of Materials Processing Technology 54 (1995) 64-69 generated at any two laboratories do not agree and furthermore the shop conditions are very much different from those under which the laboratory experiments are conducted. So, a simple prediction method using backpropagation algorithms based on the actual data is suggested. The typical back-propagation network has an input layer, an output layer and at least one hidden layer. Increasingly, they are being proposed to compute standard statistical procedures. F o r the problem discussed in this paper, one hidden layer would be sufficient. The network is made up of two modules, training and forecasting (Fig. 4). In the training phase, the user specifies the file containing the target (in this case the tool life) and variables speed, feed and depth of cut influencing the target. This is used to define the input vector. The network is initialised with r a n d o m variables of weights and threshold (between - 1 and 1). During training, information is also propagated back through the network. The error at the output based on the current network activation and expected value are calculated and used to change the connection weights and threshold. The number of training cases should be kept very large compared to the number of parameters. When the expected accuracy is achieved, the iterative process is stopped and the
weights and threshold are stored in a file for forecasting. To improve the accuracy, more training cases can be added to the input pattern for retraining [9]: when this system is deployed in a shop floor, the actual data can be collected periodically for retraining purposes. Neural networks are little more than non-linear regression. Though neural networks m a y not be more accurate than traditional methods, they have their own advantages and are convenient for this problem. There are moves to build special-purpose hardware implementations on a large scale which will help to provide practical neural-network-based solutions for predicting machining parameters.
4. Examples 4.1. Tool life prediction for turning The cutting data for turning Nitralloy 135 with carbides is used for predicting the performance (in this case the tool life) for different conditions. The input data consists of 60 sets of variables speed, feed and depth of cut and observed tool life. The prediction accuracy is
More datafor retraining InputPattern { Speed, feed, d e p t h o f c u t ~ ( Tool fife, finish} - - ~
Training Module ~ Weights and threshold
depth of cut...
Fig. 4. Block diagram of the prediction system.
Work Material: Nitralloy 135 Tool Material: Carbides Operation: Turning Speed Feed Depth Tool life (m/rain) (mm) of cut (mm) observed(min)
Tool life predicted(min)
80 80 85 100 120 100 90
48 30 33 42 53 45 43
0.4 0.4 0.36 0.15 0.1 0.2 0.3
4 8 6 2 1.5 2 1
67
45 35 34 45 50 47 40
Prediction: Tool life at 100 m/min, .25 feed, 4 mm depth is 40 min. Fig. 5. Example of tool-life prediction for the turning operation.
K Narayanan/ Journal of Materials Processing Technology 54 (1995) 64-69
68
determined by removing one set of input data and training the system with the remaining data, the error being the difference between the predicted and the observed value. This is repeated for all of the 60 cases to find the mean, minimum and maximum error. In this case, the mean error is observed to be 6.2%. During the forecasting process, the tool life for different combinations of speed, feed and depth of cut can be predicted (Fig. 5). The model is being extended for predicting other output parameters such as surface finish.
4.2. Surface damage prediction for electrical discharge machined products Prediction of the white-layer thickness and the surface roughness for AISI O1 steel electrical-discharge machined with copper electrodes is presented in Fig. 6. The pulse current and pulse-on-time were varied to generate 36 cases of test data for training the system. The gap voltage and pulse-off time were maintained constant. The mean errors for the white-layer thickness and the surface roughness are 6.8% and 9.1% respectively. For the same input conditions, the surface crack density is found to vary by as much as 20% amongst the test samples. The mean errors are also found to be very high, of the order of 25% which shows that cracking depends not only on these input conditions but also on others not
considered in this study. When some specimens were subjected to fatigue stressing, the crack pattern changed, this suggesting that micro-cracks must already have been present in the material even though they are irresolvable at the magnification employed I-5]. It is therefore difficult to quantify and measure the surface crack density.
5. Machinists Digital Notepad Machinists Digital Notepad, a low-cost PC-based data bank, has been developed to enhance the efficiency of local machining shops. It has a collection of machining data for turning, milling and drilling operations, data being given in metric or Imperial units. A totally interactive menu-and-window driven user-interface enables new users, to learn to use the system in a short time. There is no need to remember any commands. It is a tool for the operators, shop supervisors, and process planners in the machining workshop. It provides good recommendations for machining parameters and also gives recommendations for the selection of tools and cutting fluid. In addition, it calculates the optimal cutting conditions and does basic machining calculations, such as power requirements, rpm and speed conversions. It is fully textbased and interfaces with the user through menus and text windows.
Work material: AISI Ol Electrode material: Copper (positive) G a p voltage: 30 V Pulse current: 5-25 A Pulse-on time: 5(~800 las Pulse-off time: 100 gs Dielectric: Paraffin Flushing: Pressure (220 ml/min) Pulse current
Pulse-on time
Pulse energy
(A)
(ps)
5 5 5 5 5 5 7.5 7.5 7.5
50 100 220 400 600 800 50 100 220
Predicted
(mj)
White-layer thickness (gin) Observed
7.5 15 33 60 90 120 11.3 22.5 49.5
8.1 10.4 14.1 15.5 15.8 18.0 10.6 14.2 15.0
7.3 10.6 13.2 14.5 16.3 18.5 9.8 13.1 13.8
25 220 165 24.3 22.1 25 400 300 28.2 27.4 25 600 450 27.3 30.9 25 800 600 34.1 36.3 Prediction: White-layer thickness at 12 A, pulse-on time 300 is 20.1 gm
Surface roughness Ra (gin) Observed
Predicted
1.8 2.3 3.1 4.1 4.8 5.8 2.1 2.4 3.5
1.5 1.9 2.8 4.6 4.9 5.3 1.7 2.6 3.9
11.0 10.9 12.0 11.5
10.6 11.2 11.8 12.4
Fig. 6. Prediction of the surface roughness and the white-layer thickness for A I S 1 0 I steel.
v. Narayanan/Journal of Materials Processing Technology 54 (1995) 64-69
69
.........................i".........................................
Fig. 7. Sample screen for optimisation.
The system maintains a user machining data base where users can modify the recommended data and store them separately as customised data. Users can also add their own material to the data bank. In this way, a company can store the machine settings used by an expert machinist for everyone's use. The system also maintains a user machine data base that contains pertinent machine data for machines declared by the user. This data is used during calculations, relieving the user of the tedious task of entering machine parameters such as power and labour cost. Fig. 7 shows a sample screen. During query operation, the system displays recommended machine settings for a range of depth of cuts. In addition, the user can interpolate between the depths of cut to secure other recommendations. The neural-net-based prediction system discussed in this paper can be incorporated in future versions of Notepad.
6. Conclusions Conducting a large number of experiments and building of empirical/statistical models require extensive efforts and are only suitable for research and development organisations. The neural-network method is found to be easy to implement on a PC and is more than 90% accurate for turning and electrical discharge machining. A prediction system should incorporate empirical models, knowledge base and neural-net prediction. Prediction systems can be used for storage and processing of observed data for estimation of machining parameters based on actual data. These systems will help machine shops to machine under optimum conditions resulting in a reduction of production cost.
Most accidents in the machine shop occur due to the wrong selection of machining conditions. With this aid, such accidents can be minimised. To conclude, prediction is better than correction after observation.
Acknowledgements The author would like to express his sincere thanks to Ms. Mala of ITI for her help in using the neural-net method.
References [1] V. Narayanan and M. Rahman, Manufacturing information processing for automation, J. Institution Eng. Singapore 28 (4) (1988) 59. [2] J. Heng Kheng, V. Narayanan and M. Rahman, Machinists digital Notepad, Proc. Conf. on CAD/CAM for Product Design and Manufacturing, Singapore, 1993. [3] Machining Data Hand Book, 3rd edition, 2 Vol. Cincinnati: Machinability Data\Center, 1980. [4] Metals Handbook, 9th Ed., Vol. 16: Machining, ASM International, Metals Park, Ohio 1989. I-5] L.C. Lee, LC.Lim, V. Narayanan and V.C. Venkatesh, Quantification of surface damage of tool steels after EDM, lnt. J. Mach. Tools Manuf. 28 (4) (1988) 359. [6] C.H. Kahng and K.P. Rajurkar, Surfacecharacteristics behaviour due to rough and fine cutting by EDM, Ann. CIRP, 25 (1) (1977) 77. [7] Schumacher, Modern electrical discharge cutting systems and applications, Indus. Production Eng., 11 (2) (1987) 94. [8] V.C. Venkatesh and V. Narayanan, Machinability correlation among turning, milling and drilling processes, Ann. CIRP, 35 (1) (1986) 59. [9] Neural Ware User Manuals, NeuralWare, Inc., Pittsburgh, 1991.
Materials Processing Technology ELSEVIER
Journal of Materials Processing Technology 54 (1995) 64-69
Systems for the prediction of process parameters V. Narayanan Information Technology Institute, National Computer Board, Singapore, Singapore
Received 1 April 1994
Industrial summary
Manufacturers of machine tools, cutting tools and electrodes assist users by providing some form of performance data with their products. However, production engineers do not have resources and time to study manuals or performance charts and do detailed analysis to predict the performance of the system for the chosen operating parameters. Thus, an attempt has been made to develop a performance-prediction system for operating parameters such as machinability data selection, which will assist process planners and machinists in their decision-making processes. A back-propagation neural-network model is proposed for prediction. The use of traditional empirical models and expert systems for machinability studies is also discussed. The proposed method will also help in interpolating/extrapolating collected factual data from the shop-floor, for different machining conditions.
1. Introduction
Machining processes can be divided broadly into traditional and non-traditional processes. The traditional machining processes such as turning and milling remove material by chip formation, abrasion, or micro-chipping. There has been a rapid growth in the development of harder and difficult-to-machine materials. In some instances, the workpiece is too flexible, slender, or delicate to withstand the cutting forces. The shape of some parts are complex and sometimes the surface finish and tolerance requirements are stringent. These requirements have led to the development of non-traditional processes such as Electrical discharge machining (EDM), Ultrasonic machining and Laser-beam machining. These methods are unconventional in the sense that conventional tools are not employed for cutting; instead energy in its direct form is utilised. These unconventional methods can not replace the conventional machining processes and their usage depends on the work material and product requirements. For turning, the first requirement is that the tool must be harder than the material itself. The tool and the material must be held rigidly so that the tool can penetrate the workpiece and cut efficiently. Different cuttingtool materials require different tool geometry for optimum efficiency of the operation. Additional studies and research will be necessary as new tools and materials are developed. Despite the extensive work that has been conducted in this area over the past three decades, most of the developments in cutting have evolved through
0924-0136/95/$09.50 © 1995 ElsevierScienceS.A. All rights reserved SSDI 0 9 2 4 - 0 1 3 6 ( 9 5 ) 0 1 9 2 1 - Z
trial-and-error methods and are learnt on an empirical basis. The main objective is to satisfy the demand of the component dimensions and its surface finish. In order to achieve these results, the machinist is left with a wide range of possible speed, feed, depth of cut and other machining parameters (Fig. 1). Similarly in the case of spark erosion or electrical discharge machining, the machinist has a wide range of input parameters such as pulse current, gap voltage and pulse-on duration, to choose from. Machining conditions selected for economical manufacturing are based on various criteria such as maximum profit rate, minimum cost, maximum production rate, minimum scrap and maximum tool life [1]. The application of machining economics to shop practice has been limited and the correct choice of machining conditions is normally overlooked, the reasons for which are: (i) shortage of reliable tool life and cost data; (ii) difficulty in using various bulky manuals and handbooks; (iii) difficulty in updating machinability data; (iv) difficulty in understanding these complicated criteria and associated equations in practice; (v) difficulty in processing a large amount of other related data, which have to be extracted and processed; (vi) tedious interpolation and extrapolation of data to suit different machining conditions; and (vii) tedious calculation of optimum machining conditions. The solution to the above problems is a computerised machinability-data-base system which can extract data rapidly from data bases and perform tedious calculations [2]. Whilst the experience of the machinist plays a very
V. Narayanan / Journal of Materials Processing Technology 54 (1995) 64-69 f
65
Iflvut or
~ Toolmaterial Speed ~ Feed Depth of cut
Toolgeometry
Turning process
Qutput Toollife Surfacefinish Component dimensions
]
Intmtor Puetrm Pulse-off-duration C Vol El Machinin~ lscteta-rge oode n-duratiomat nConditions eria-l- - I ~ - [
Electrical discharge machining
Outlmt Surfacefinish Componentdimensions
Depthofdamagedlayer Surface crack density
Fig. 1. Schematic diagram of the turning and the EDM process.
important role, much of these machining knowledge have actually been captured, formatised and made available in the form of machining data books, data sheets and charts, nomograms and slide rules [3,4]. In this paper, development of a customisable PC-based system for predicting the performance of turning and electrical discharge machining operations is discussed.
Fig. 2. Scanning electron micrograph of the surface microstructure of AISI OI 15A and 300 ms.
where X and Y are independent-and dependent-variables and A, B, C are constants and exponents.
2.2. Electrical discharge machining (EDM) 2. Machinability models and empirical relations
2.1. Turning Empirical models establishing relationships between different variables involved in turning processes are developed based on Taylor's equation. For example, tool life and surface finish can be expressed in terms of speed, feed, depth of cut and nose radius as in Eqs. (1) and (2), the constants and exponents depending on the hardness and machinability rating of the work materials, tool materials, types of coolant, tool geometry and other operational parameters. The constants and exponents can be determined from the actual data by statistical methods, such as regression analysis. Tool life = K1 (Speed) "1 (Feed) "2 (Depth of cut) "3, Surface finish = K2(Feed) "4 (Nose radius)"L
(1)
where K1 and K2 are constants and nl, n2, n3, n4 and n5 are exponents. The non-linear model may be linearised by taking natural logarithms and can be reduced to the form indicated in Eq. (3). To better represent the observed data, the polynomial model for the variables as well as the cross-product terms are used to detect the interactions between the effects of the variables as in Eq. (4). Though these methods give some representation of the actual cutting process, practitioners find it very difficult and tedious to use them. In Y = A + BIn Xi,
(3)
In Y = A + Bi In Xi + Cij In Xi In X j,
(4)
EDM has become one of the most widely used means of producing tools and dies. There are, however, a number of problems associated with the use of EDM, principal amongst which is the surface integrity after machining. If surface damage such as cracks, residual stresses and recast layer are not attended to, the effective tool life may be reduced severly. Thus, it is necessary to estimate the surface damage that has to be eliminated. Some of the input parameters are: (i) gap voltage (V in volts); (ii) pulse current (1 in amperes); (iii) pulse-on duration (ti in micro-seconds); and (iv) pulse-off duration (to in micro seconds). The pulse energy (E in milli joules) is given by Vlti. Generally, it has been observed that metal removal is predominantly thermal in nature. The rapid cooling rate causes surface defects such as cracks, plastic deformation and the build-up of residual stresses. The microstructures of the surfaces of tool steels, namely AISI O1, A2, D2 and D6 were examined [5]. Generally three distinct layers can be identified as in Fig. 2: the outermost layer, an intermediate layer and the unaffected parent metal. For tool steels, the top-most surface layer is an uneven, nonetchable layer often referred to as the white layer. Immediately beneath the white layer is an intermediate layer where the heat is not sufficiently high to cause melting but is sufficiently high to induce micro-structural transformation in the material. There is a need to quantify the depth of the white layer (dw in microns) and the thickness of the overall recast layer (d t in microns) [5-7]. Electrical discharge machined surfaces are covered with cracks, the extent of cracks on the surfaces of AISI D6 tools steels machined at a pulse energy of 75 mJ being
66
K
Narayanan / Journal of Materials Processing Technology 54 (1995) 64-69 Table 1 Empirical relationships
kl
kz nl nl
/; AISI i)2
AISI 1)6
0.1 nun m
Fig. 3. Tracing of the cracks as seen on the machined specimen surface of AISI D2 and D6 steel. shown in Fig. 3. The surface crack density (de in mm per mm 1) can be defined as the total length of cracks per unit surface area. Cracks are rarely found to extend beyond the white layer, so that the maximum depth of damage is given by the thickness of the white layer. In the case of EDM, as also in other unconventional machining processes, the cause and effect of the input parameters on the objective functions is very confusing to the machine operator. Thus, it is necessary to relate surface damage to a parameter such as the mean surface roughness value (R,) which can be measured easily. It is also important to know how the thickness of the white layer is affected by process parameters such as pulse current and energy. The thickness of the white layer can be expressed as a function of the input pulse energy (Eq. (5)) and surface roughness (Eq. (6)). The values of the constants and exponents based on experiments for AISI O1, A2, D2 and D6 steels are given in Table 1. dw = k~ E"',
(5)
dw = k2 R"~2,
(6)
In actual practice, it is very difficult to develop empirical equations for different materials machined under different conditions, thus a customisable system to predict performance based on observed data is proposed. 2.3. Knowledge bases
Though the process of machining appears to be simple, research done on the study of the mechanics of metalcutting processes is unable to answer many basic questions and the physical laws governing the machining processes are still unclear. Models have been developed based on the machining of solid blocks, rigidly clamped and machined under ideal conditions. The term "machinability" has no scientific definition. Tool-life scatter is inherent even in well-controlled laboratory tool-life tests.
AISI Ol
AISI A2
AISI D2
AISI D6
3.974 6.98 0.30 0.48
4.345 5.94 0.29 0.59
4.254 7.52 0.30 0.43
4.046 6.76 0.43 0.49
There are always variations in the mechanical and metallurgical properties such as the chemical composition and the microstructure of the materials [8]. Thus, machinability data are only a set of reasonable recommendations for operational variables such as tool materials, feeds, speeds and cutting fluids. Cutting data from hand-books therefore cannot be used as they are, and have to be supplemented by rulesof-thumb/heuristics used by expert/experienced machinists. Rules have to be collected, validated and converted into computer readable form. Examples of these rules are: 1. If a machine is very new, use 80% of recommended speed. If it is one year old, use a factor of 0.75 to calculate the cutting speed. 2. For milling, the surface should be dry. 3. If you mill thin plates and use a negative rake angle, thin plates will bend. Increase the clamping forces by 50%. 4. Use down-milling for milling pockets. If up-milling is used, give tool compensation. 5. When machining a mould, the last cut should be down-milling. 6. Upmilling should be used when machines are old or not very rigid. 7. If the tool material is tungsten, ceramic or diamond, use continuous cooling or no coolant at all. There should not be any intermittent cooling. 8. Do not use oil coolants for cuts of less than 0.1 mm. For finishing, oil should not be used. 9. If you do not have automatic feeding and a ball spindle, use up-milling. These rules will also take into account a few anomalies. An example is that it is easy to machine a 10 mm diameter aluminium bar at 1000 rpm. However at 3000 rpm, the tool life will be very much reduced and the end results will be very poor, whilst at 30000 rpm, the chips will fly away very quickly and the machinability will be very much improved [2].
3. Neural net methods
The available models discussed in the earlier paragraphs are derived based on laboratory-generated data under the best possible conditions. In practice, the data
K Narayanan/Journal of Materials Processing Technology 54 (1995) 64-69 generated at any two laboratories do not agree and furthermore the shop conditions are very much different from those under which the laboratory experiments are conducted. So, a simple prediction method using backpropagation algorithms based on the actual data is suggested. The typical back-propagation network has an input layer, an output layer and at least one hidden layer. Increasingly, they are being proposed to compute standard statistical procedures. F o r the problem discussed in this paper, one hidden layer would be sufficient. The network is made up of two modules, training and forecasting (Fig. 4). In the training phase, the user specifies the file containing the target (in this case the tool life) and variables speed, feed and depth of cut influencing the target. This is used to define the input vector. The network is initialised with r a n d o m variables of weights and threshold (between - 1 and 1). During training, information is also propagated back through the network. The error at the output based on the current network activation and expected value are calculated and used to change the connection weights and threshold. The number of training cases should be kept very large compared to the number of parameters. When the expected accuracy is achieved, the iterative process is stopped and the
weights and threshold are stored in a file for forecasting. To improve the accuracy, more training cases can be added to the input pattern for retraining [9]: when this system is deployed in a shop floor, the actual data can be collected periodically for retraining purposes. Neural networks are little more than non-linear regression. Though neural networks m a y not be more accurate than traditional methods, they have their own advantages and are convenient for this problem. There are moves to build special-purpose hardware implementations on a large scale which will help to provide practical neural-network-based solutions for predicting machining parameters.
4. Examples 4.1. Tool life prediction for turning The cutting data for turning Nitralloy 135 with carbides is used for predicting the performance (in this case the tool life) for different conditions. The input data consists of 60 sets of variables speed, feed and depth of cut and observed tool life. The prediction accuracy is
More datafor retraining InputPattern { Speed, feed, d e p t h o f c u t ~ ( Tool fife, finish} - - ~
Training Module ~ Weights and threshold
depth of cut...
Fig. 4. Block diagram of the prediction system.
Work Material: Nitralloy 135 Tool Material: Carbides Operation: Turning Speed Feed Depth Tool life (m/rain) (mm) of cut (mm) observed(min)
Tool life predicted(min)
80 80 85 100 120 100 90
48 30 33 42 53 45 43
0.4 0.4 0.36 0.15 0.1 0.2 0.3
4 8 6 2 1.5 2 1
67
45 35 34 45 50 47 40
Prediction: Tool life at 100 m/min, .25 feed, 4 mm depth is 40 min. Fig. 5. Example of tool-life prediction for the turning operation.
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determined by removing one set of input data and training the system with the remaining data, the error being the difference between the predicted and the observed value. This is repeated for all of the 60 cases to find the mean, minimum and maximum error. In this case, the mean error is observed to be 6.2%. During the forecasting process, the tool life for different combinations of speed, feed and depth of cut can be predicted (Fig. 5). The model is being extended for predicting other output parameters such as surface finish.
4.2. Surface damage prediction for electrical discharge machined products Prediction of the white-layer thickness and the surface roughness for AISI O1 steel electrical-discharge machined with copper electrodes is presented in Fig. 6. The pulse current and pulse-on-time were varied to generate 36 cases of test data for training the system. The gap voltage and pulse-off time were maintained constant. The mean errors for the white-layer thickness and the surface roughness are 6.8% and 9.1% respectively. For the same input conditions, the surface crack density is found to vary by as much as 20% amongst the test samples. The mean errors are also found to be very high, of the order of 25% which shows that cracking depends not only on these input conditions but also on others not
considered in this study. When some specimens were subjected to fatigue stressing, the crack pattern changed, this suggesting that micro-cracks must already have been present in the material even though they are irresolvable at the magnification employed I-5]. It is therefore difficult to quantify and measure the surface crack density.
5. Machinists Digital Notepad Machinists Digital Notepad, a low-cost PC-based data bank, has been developed to enhance the efficiency of local machining shops. It has a collection of machining data for turning, milling and drilling operations, data being given in metric or Imperial units. A totally interactive menu-and-window driven user-interface enables new users, to learn to use the system in a short time. There is no need to remember any commands. It is a tool for the operators, shop supervisors, and process planners in the machining workshop. It provides good recommendations for machining parameters and also gives recommendations for the selection of tools and cutting fluid. In addition, it calculates the optimal cutting conditions and does basic machining calculations, such as power requirements, rpm and speed conversions. It is fully textbased and interfaces with the user through menus and text windows.
Work material: AISI Ol Electrode material: Copper (positive) G a p voltage: 30 V Pulse current: 5-25 A Pulse-on time: 5(~800 las Pulse-off time: 100 gs Dielectric: Paraffin Flushing: Pressure (220 ml/min) Pulse current
Pulse-on time
Pulse energy
(A)
(ps)
5 5 5 5 5 5 7.5 7.5 7.5
50 100 220 400 600 800 50 100 220
Predicted
(mj)
White-layer thickness (gin) Observed
7.5 15 33 60 90 120 11.3 22.5 49.5
8.1 10.4 14.1 15.5 15.8 18.0 10.6 14.2 15.0
7.3 10.6 13.2 14.5 16.3 18.5 9.8 13.1 13.8
25 220 165 24.3 22.1 25 400 300 28.2 27.4 25 600 450 27.3 30.9 25 800 600 34.1 36.3 Prediction: White-layer thickness at 12 A, pulse-on time 300 is 20.1 gm
Surface roughness Ra (gin) Observed
Predicted
1.8 2.3 3.1 4.1 4.8 5.8 2.1 2.4 3.5
1.5 1.9 2.8 4.6 4.9 5.3 1.7 2.6 3.9
11.0 10.9 12.0 11.5
10.6 11.2 11.8 12.4
Fig. 6. Prediction of the surface roughness and the white-layer thickness for A I S 1 0 I steel.
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.........................i".........................................
Fig. 7. Sample screen for optimisation.
The system maintains a user machining data base where users can modify the recommended data and store them separately as customised data. Users can also add their own material to the data bank. In this way, a company can store the machine settings used by an expert machinist for everyone's use. The system also maintains a user machine data base that contains pertinent machine data for machines declared by the user. This data is used during calculations, relieving the user of the tedious task of entering machine parameters such as power and labour cost. Fig. 7 shows a sample screen. During query operation, the system displays recommended machine settings for a range of depth of cuts. In addition, the user can interpolate between the depths of cut to secure other recommendations. The neural-net-based prediction system discussed in this paper can be incorporated in future versions of Notepad.
6. Conclusions Conducting a large number of experiments and building of empirical/statistical models require extensive efforts and are only suitable for research and development organisations. The neural-network method is found to be easy to implement on a PC and is more than 90% accurate for turning and electrical discharge machining. A prediction system should incorporate empirical models, knowledge base and neural-net prediction. Prediction systems can be used for storage and processing of observed data for estimation of machining parameters based on actual data. These systems will help machine shops to machine under optimum conditions resulting in a reduction of production cost.
Most accidents in the machine shop occur due to the wrong selection of machining conditions. With this aid, such accidents can be minimised. To conclude, prediction is better than correction after observation.
Acknowledgements The author would like to express his sincere thanks to Ms. Mala of ITI for her help in using the neural-net method.
References [1] V. Narayanan and M. Rahman, Manufacturing information processing for automation, J. Institution Eng. Singapore 28 (4) (1988) 59. [2] J. Heng Kheng, V. Narayanan and M. Rahman, Machinists digital Notepad, Proc. Conf. on CAD/CAM for Product Design and Manufacturing, Singapore, 1993. [3] Machining Data Hand Book, 3rd edition, 2 Vol. Cincinnati: Machinability Data\Center, 1980. [4] Metals Handbook, 9th Ed., Vol. 16: Machining, ASM International, Metals Park, Ohio 1989. I-5] L.C. Lee, LC.Lim, V. Narayanan and V.C. Venkatesh, Quantification of surface damage of tool steels after EDM, lnt. J. Mach. Tools Manuf. 28 (4) (1988) 359. [6] C.H. Kahng and K.P. Rajurkar, Surfacecharacteristics behaviour due to rough and fine cutting by EDM, Ann. CIRP, 25 (1) (1977) 77. [7] Schumacher, Modern electrical discharge cutting systems and applications, Indus. Production Eng., 11 (2) (1987) 94. [8] V.C. Venkatesh and V. Narayanan, Machinability correlation among turning, milling and drilling processes, Ann. CIRP, 35 (1) (1986) 59. [9] Neural Ware User Manuals, NeuralWare, Inc., Pittsburgh, 1991.