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Productivity and Cycle Time Prediction Using Artificial Neural Network
Available online at www.sciencedirect.com
ScienceDirect Procedia Economics and Finance 15 (2014) 1563 – 1569
Emerging Markets Queries in Finance and Business
Productivity and Cycle Time Prediction Using Artificial Neural Network Cristian Gelmereanua, Liviu Morara, Stefan Bogdan* a
Technical University of Cluj, Napoca, Cluj-Napoca, Romania
Abstract The main purpose of the paper is to develop a neural network application that could predict the cycle time for High Speed Machining. Increase productivity by increasing the speed of process has been imposed by the cutting progresses theory and the fields related directly to the cutting process. Thus, this procedure aims to an increasing productivity, lowering costs and execution time and also improving the quality of parts. © 2014 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license © 2013 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Emerging (http://creativecommons.org/licenses/by-nc-nd/3.0/). Markets Queries Finance and local organization underinresponsibility of Business the Emerging Markets Queries in Finance and Business local organization Selection and peer-review Keywords: productivity, Artificial Neural Network, cycle time prediction, High Speed Machining,
1. Introduction It is a matter of common knowledge that higher productivity leads to a reduction in cost of production, reduces the sales price of an item, expands markets, and enables the goods to compete effectively in the world market. It yields more wages to the workers, shorter working hours and greater leisure time for the employees.
* Corresponding author. Cristian Gelmereanu E-mail address: [email protected].
2212-5671 © 2014 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and peer-review under responsibility of the Emerging Markets Queries in Finance and Business local organization doi:10.1016/S2212-5671(14)00626-1
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Cristian Gelmereanu et al. / Procedia Economics and Finance 15 (2014) 1563 – 1569
In fact the strength of a country, prosperity of its economy, standard of living of the people and the wealth of the nation are very largely determined by the extent and measure of its production and productivity. Productivity can be defined in many ways. Some of them are as follows: • Productivity is nothing but the reduction in wastage of resources such as labor, machines, materials, power, space, time, capital, etc. • Productivity can also be defined as human endeavor (effort) to produce more and more with less and less inputs of resources so that the products can be purchased by a large number of people at affordable price. • Productivity implies development of an attitude of mind and a constant urge to find better, cheaper, easier, quicker, and safer means of doing a job, manufacturing a product and providing service. • Productivity aims at the maximum utilization of resources for yielding as many goods and services as possible, of the kinds most wanted by consumers at lowest possible cost. • Productivity processes more efficient works involving less fatigue to workers due to improvements in the layout of plant and work, better working conditions and simplification of work. In a wider sense productivity may be taken to constitute the ratio of all available goods and services to the potential resources of the group (Salomon, C.). 2. Ways to increase productivity using the cutting parameters One way to increase productivity is by increasing the working regime parameters. Among these may be mentioned cutting depth (t), the feed (s) and the main cutting speed (v) measuring the volume "V" of material that will be removed per unit of time. In order to decrease the cycle time we have to take into account the quality of the surface, the required accuracy, the tool durability, the workpiece dimensions, the removed chips size etc. One of the possibilities to increase the profitability of the process is by raising cutting speed. This has always been a constant concern of cutting process specialists. This paper deals with this topic, highlight the main advantages of this method and trying to generate the cycle time using also neural networks. It was designed a stand in the Engineering Design and Robotics Laboratory from the Technical University of Cluj Napoca (Figure 1.) to conduct research regarding the productivity. We want to implement an algorithm that leads to an increase cutting regime having as signal the vibration level.
Fig.1. Milling machine stand
Cristian Gelmereanu et al. / Procedia Economics and Finance 15 (2014) 1563 – 1569
3. Literature review The first concept of High Speed Machining (1931) was presented by Carl J Salomon (Salomon, C.,1931), who is considered as the father of high speed machining. The results of his experiments, carried out on nonferrous metals such as Aluminium, Copper and Bronze, using helical milling cutters at speeds up to 18067 m/min (surface metres per minute) revealed that with the increase of cutting speed, the cutting temperature increases up to a maximum value close to the melting point of the material and then decreases with further increase in speed (King, R.I, 1984). Bhattacharyya et.al investigated on cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques (Bhattacharyya et.al, 2007). The tool wear in high speed milling using detection process approach has been done by Kious et.al. Estimation of tool wear during CNC milling using neural network-based sensor fusion was implemented by Ghosh et.al. On-line metal cutting tool condition monitoring using multi-layer perceptron neural networks was studied by Lister and Dimla. Tool condition monitoring using artificial intelligence methods has been carried out by Balazinski et.al. Cho and Ko estimated tool wear length in finish milling using a fuzzy inference algorithm. Intelligent process supervision for predicting tool wear in machining processes was done Alique by and Haber. Ning and Veldhuis analyzed mechanistic modeling of ball end milling including tool wear. (W. Yan, 1999) Development and processes manufacturing based on cutting speed from 10 m/min in 1800, up to 100 m / min in 1900, a thousand feet per minute in 1980 to several thousand in 1994 (figure 2) imposed default also a tendency to continue studying these processes in terms of the elements that contribute directly to their achievement, their deployment conditions, the level of cutting regime.
Fig.2. High Speed Machining
A very general definition of high speed processing method is proposed by French researcher Alain Defretin in his book “Usinage a Grande Vitesse”. (Ivan, R., 2011) "Processing of high speed milling includes all processes (high feed rates or high speed spindle rotation), processes that are superior to those that are considered today controlled from a practical standpoint."( Defretin, A.) In the case of high-speed processing, the main axis of the spindle is greater than 15,000 revolutions per minute, as illustrated above. Growth rate has a reverse effect as it reduces cutting forces, it improves the quality of machined surfaces and improves the natural fragmentation of chips, etc.. In this sense, today more than yesterday and less than tomorrow, high speed cutting technology is recognized as one of the key processing technologies in order to
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obtain increased productivity and a precision manufacturing performance. In the case of high-speed processing, the processing speed is higher than the propagation speed of the heat in the work piece. Below there is a comparison of speeds used during machining some selected materials using conventional and HSM methods. Table 1. Conventional vs. High Speed Machining (http://fstroj.utc.sk/journal/engl/papers/034_2002.pdf)
Work material Aluminium Cast iron
Steel
soft ductile Free mach steel alloy stainless Hardness HRC65 Titanium
Superalloy
Solid Tools (end mills, drills), WC, coated WC, PCD, ceramic Typical cutting High cutting Speed [m/min] speed [m/min] >305 (WC, >3050 (WC, PCD) PCD) 152
366
107 107 76 107
244 366 244 152
24
122
38
61
46
76
Indexable Tools (shell mills, face mills) WC, ceramic, CBN, PCD Typical cutting High cutting speed [m/min] speed [m/min] >3658 (WC, >610 PCD) 1219 (sialon, 366 ceramic) 244 914 (ceramic) 366 610 213 366 152 274 30 (WC) 91 46 (WC), 183 (CBN, ceramic) (CBN, ceramic) 46 91 84 (WC) 213 366 (sialon, (sialon) ceramic)
In fact, the question is what are the advantages of high speed milling compared to conventional milling (table 2) Table 2. The advantages/ disadvantages of using high-speed milling
Advantages of using high-speed milling Economic efficiency and increased production A high-quality machined surface with a roughness of up to 0.2 ȝm; Possible to obtain rapid milling of thin walls up to 0.02 mm thick to 20 mm; Temperature transferred into the surface layer of the workpiece is less than the feed rate; the heat doesn’t have time to propagate in the part; Achieve higher dimensional precision than with conventional process;
Disadvantages of using high-speed milling The need for qualified personnel. It required a good knowledge of the cutting process, of the equipment; Acceleration and deceleration are very fast processes, many stops and starts that lead to premature wear of the machine tools; The consequences are more severe for human errors or software failures; Need to take additional safety measures (doors closed during processing, tools verification etc.)
Thus, this procedure aims to increase productivity, lower the costs and also improve the quality of the made parts (table 3). (http://www.slideshare.net/hurco/high-speed-machining-hurco-imts-2012)
Cristian Gelmereanu et al. / Procedia Economics and Finance 15 (2014) 1563 – 1569
Table 3. Traditional Processing vs High Speed Machining
Hourly Shop Rate Hourly Burden Rate Cycle Time Cost to Produce Quoted Price Profit per Part Profitx100 pieces Profitx500pieces
Traditional Processing (12K) 60.00$ 35.00$ 11min 16sec 6.57$ 13.50$ 6.93$ 693.00$ 3465.00$
High Speed Machining (12K) 60.00$ 35.00$ 2min 51sec 1.67$ 13.50$ 11.83$ 1183.00$ 5915.00$
High Speed Machining (18K) 60.00$ 35.00$ 1min 54sec 1.11$ 13.50$ 12.39$ 1239.00$ 6195.00$
However, this quality is directly influenced by the state of the cutting tool, which is exposed to wear during machining. Thus control of wear during the cutting process proves to be important. What unites all these terms "target" are the cutting parameters, which can optimize the milling operation (reduced wear, reduced dynamic aspects) and thus reduce manufacturing costs by placing the operation in a stable and intermediate processing zone, and reducing the cycle time, while increasing productivity. For this are consider the following parameters: (Ivan, R., 2011) • Spindle speed (rpm) • Feed rate (mm/min) • Depth of cut (mm) • Cycle time (min) The experiments were conducted in a ZXX6350ZA vertical axis milling machine using tool life 20 mm diameter cutter mill with 4 cutter inserts. The cutter mill was made by high speed steel-E (HSS-E). Workpieces used for this experiment was Al 7075 (Table 4) (http://fstroj.utc.sk/journal/engl/papers/034_2002.pdf). Table 4. Cutting data
No.
Spindle speed (rpm)
Feed Rate Depth of cut (mm) Cycle time (min) (mm/min) 1 95 132 0.2 26.4 2 360 98 0.6 45.5 3 565 200 0.2 38.7 4 950 98 1 15.7 5 1500 132 0.4 14 6 18000 143 1.5 15.6 7 24100 1360 1 35 8 24050 1390 0.4 5 9 35000 1750 0.2 8.02 10 40000 2120 3 12 11 40500 2200 1.5 3.5 With the parameters in Table 4 was tried modeling and generation of the cycle time using ANN reducing cycle time due to use of high-speed milling.
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4. Tool life determination using Artificial Neural Networks (ANN) An Artificial Neural Network (ANN) is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks. According to Simon Haykin, neural network is “a massively parallel distributed processor with the natural tendency to store experimental knowledge and make them available for use”. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase (Haykin, 2009). The similarity with the human brain consists in learning request before acquiring knowledge. Learning takes place through the entries that are brought in the system. Knowledge is stored in connections interneurons, ie neural weights, which stores the results (Beale et al., 2011). We used the data from table 4, having in the input data the spindle speed (rpm), the feed rate (mm/min)and the depth of cut (mm), and in the output layer the cycle time. The Levenberg-Marquardt algorithm was used for training and the MLP (Multilayer perceptrons) training was done automatically using the elaborated software package.(Campean, E., 2012) The ANN consists in 5 neurons. simulating first with 11 pairs of input in the input layer. It is noted that the number of neurons chosen, the number of intermediate layers and the activation functions were well chosen because, after only 3 iterations (epochs), neural network has reached a result in the indicated error, having a running time of five seconds. It can be seen that the error of 10-2 is satisfactory. The error gradient is 10-15. Along with this result, is made available a graphic illustration of the training process. Table 5 show the result: Table 5. The ANN results
No.
Spindle speed (rpm)
Feed Rate (mm/min)
Depth of cut (mm)
Cycle (min)
time
ANN cycle time
Error (%)
1
95
132
0.2
26.4
25.71
0.0069
2
565
200
0.2
38.7
41.01
-0.0231
3
950
98
1
15.7
15.96
-0.0026
4
24100
1360
1
35
35.62
-0.0062
5
24050
1390
0.4
5
5.31
-0.0031
6
35000
1750
0.2
8.02
8.36
-0.0034
7
40000
2120
3
12
11.91
0.0009
8
40500
2200
1.5
3.5
2.92
0.0058
9
20000
900
1.5
8.3
7.52
0.0078
10
10095
4040
1
14.7
12.56
0.0214
11
8514
5110
1
12.3
12.01
0.0029
12
12038
4820
0.4
3.6
3.02
0.0058
13
13403
5360
0.4
3.5
3.02
0.0048
14
11196
3360
1.5
4.2
3.5
0.007
The results shown in table 5 indicate approximately the same values, what means that the ANN approach is suitable for such application. The great advantage of the ANN is the time needed to obtain the results.
Cristian Gelmereanu et al. / Procedia Economics and Finance 15 (2014) 1563 – 1569
5. Conclusion In this study, ANN for modeling and predicting cycle time for milling parts made of Aluminum material was developed. Given the accuracy that was achieved it is safe to conclude that the ANN approach is suitable for such application. It was found that (ANN) prediction correlates very well with the experimental results. Given the results obtained by the ANN simulation was designed an algorithm that involves increasing the cutting regime during machining. Maximum cutting regime parameters (cutting depth for beginning and advanced) will be equal to the acceptable level of vibration.
Acknowledgements This paper was supported by the project "Improvement of the doctoral studies quality in engineering science for development of the knowledge based society-QDOC” contract no. POSDRU/107/1.5/S/78534, project co-funded by the European Social Fund through the Sectorial Operational Program Human Resources 2007-2013.
References Salomon, C., Critical Machining Velocities. 1931: German Patent No. 523594. King, R.I. and R.L. Vaughn. A synoptic review of High-speed machining from Salomon to the present. in High speed machining : presented at the winter annual meeting of the American Society of Mechanical Engineers 1984. New Orleans, Louisiana: ASME (345 E. 47th St., New York 10017): p. 1-13. P. Bhattacharyya, D. Sengupta, S. Mukhopadhyay, “Cutting forcebased real-time estimation of tool wear in face milling using a combination of signal processing techniques,” Mechanical Systems and Signal Processing, Vol 21, 2007, pp 2665–2683. M. Kious, A. Ouahabi, M. Boudraa, R. Serra, A. Cheknane, “Detection process approach of tool wear in high speed milling,” Measurement, Vol 43, 2010, pp 1439–1446. N. Ghosh, Y.B. Ravi, A. Patra, S. Mukhopadhyay, S. Paul, A.R. Mohanty, A.B. Chattopadhya, “Estimation of tool wear during CNC milling using neural network-based sensor fusion,” Mechanical Systems and Signal Processing, Vol 21, 2007, pp 466–479. D.E. Dimla Sr. a, P.M. Lister, “On-line metal cutting tool condition monitoring.II: tool-state classification using multi-layer perceptron neural networks,” International Journal of Machine Tools & Manufacture, Vol 40, 2000, pp 769–781. M. Balazinski, E. Czogala, K. Jemielniak, J. Leski, “Tool condition monitoring using artificial intelligence methods,” Engineering Applications of Artificial Intelligence, Vol 15 , 2002, pp 73–80. T. J. Ko, D. W. Cho, “Estimation of tool wear length in finish milling using a fuzzy inference algorithm,” Wear, Vol 169, 1993, pp 97106. R. E. Haber, A. Alique, “Intelligent process supervision for predicting tool wear in machining processes,” Mechatronics, Vol 13, 2003, pp 825–849. W. Yan, Y.S. Wong, K.S. Lee, T. Ning, “An investigation of indices based on milling force for tool wear in milling,” Journal of Materials Processing Technology, Vol 89, 1999, pp, 245–253. Ing. IVAN Radu Alexandru, „ Studiul comportamentului dinamic al sistemului port-scula-scula aschietoare-piesa in cazul operatiei de frezare cu viteze inalte”, Teza de doctorat, Universitatea Transilvania din Brasov, Brasov, 2011 Defretin, A., Usinage a grande vitesse, available at http:/www.techniques-ingenieur.fr/dossier/usinage a grande vitesse/BM7180. http://fstroj.utc.sk/journal/engl/papers/034_2002.pdf http://www.slideshare.net/hurco/high-speed-machining-hurco-imts-2012 Campean E., Morar L., Baru P., Bordea C., Pop D., Aspects regarding some simulation models for logistic management, Procedia Economics and Finance 00 (2012) 000–000, EFQM, Targu Mures, Romania E. Ciupan, A Model for the Management of a Supply Activity, Based on Statistical Data, Quality and Innovation in Engineering and Management, Cluj Napoca, 2011, p.249-253. Russell S., Norvig, P.,” Artificial Intelligence: A Modern Approach”. Prentice Hall, 2002. Simon Haykin, “Neural networks and learning machines”, Volume 10, Prentice Hall, 2009 Mark Hudson Beale, Martin T. Hagan, Howard B. Demuth, Neural Network Toolbox™ User’s Guide
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ScienceDirect Procedia Economics and Finance 15 (2014) 1563 – 1569
Emerging Markets Queries in Finance and Business
Productivity and Cycle Time Prediction Using Artificial Neural Network Cristian Gelmereanua, Liviu Morara, Stefan Bogdan* a
Technical University of Cluj, Napoca, Cluj-Napoca, Romania
Abstract The main purpose of the paper is to develop a neural network application that could predict the cycle time for High Speed Machining. Increase productivity by increasing the speed of process has been imposed by the cutting progresses theory and the fields related directly to the cutting process. Thus, this procedure aims to an increasing productivity, lowering costs and execution time and also improving the quality of parts. © 2014 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license © 2013 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Emerging (http://creativecommons.org/licenses/by-nc-nd/3.0/). Markets Queries Finance and local organization underinresponsibility of Business the Emerging Markets Queries in Finance and Business local organization Selection and peer-review Keywords: productivity, Artificial Neural Network, cycle time prediction, High Speed Machining,
1. Introduction It is a matter of common knowledge that higher productivity leads to a reduction in cost of production, reduces the sales price of an item, expands markets, and enables the goods to compete effectively in the world market. It yields more wages to the workers, shorter working hours and greater leisure time for the employees.
* Corresponding author. Cristian Gelmereanu E-mail address: [email protected].
2212-5671 © 2014 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and peer-review under responsibility of the Emerging Markets Queries in Finance and Business local organization doi:10.1016/S2212-5671(14)00626-1
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Cristian Gelmereanu et al. / Procedia Economics and Finance 15 (2014) 1563 – 1569
In fact the strength of a country, prosperity of its economy, standard of living of the people and the wealth of the nation are very largely determined by the extent and measure of its production and productivity. Productivity can be defined in many ways. Some of them are as follows: • Productivity is nothing but the reduction in wastage of resources such as labor, machines, materials, power, space, time, capital, etc. • Productivity can also be defined as human endeavor (effort) to produce more and more with less and less inputs of resources so that the products can be purchased by a large number of people at affordable price. • Productivity implies development of an attitude of mind and a constant urge to find better, cheaper, easier, quicker, and safer means of doing a job, manufacturing a product and providing service. • Productivity aims at the maximum utilization of resources for yielding as many goods and services as possible, of the kinds most wanted by consumers at lowest possible cost. • Productivity processes more efficient works involving less fatigue to workers due to improvements in the layout of plant and work, better working conditions and simplification of work. In a wider sense productivity may be taken to constitute the ratio of all available goods and services to the potential resources of the group (Salomon, C.). 2. Ways to increase productivity using the cutting parameters One way to increase productivity is by increasing the working regime parameters. Among these may be mentioned cutting depth (t), the feed (s) and the main cutting speed (v) measuring the volume "V" of material that will be removed per unit of time. In order to decrease the cycle time we have to take into account the quality of the surface, the required accuracy, the tool durability, the workpiece dimensions, the removed chips size etc. One of the possibilities to increase the profitability of the process is by raising cutting speed. This has always been a constant concern of cutting process specialists. This paper deals with this topic, highlight the main advantages of this method and trying to generate the cycle time using also neural networks. It was designed a stand in the Engineering Design and Robotics Laboratory from the Technical University of Cluj Napoca (Figure 1.) to conduct research regarding the productivity. We want to implement an algorithm that leads to an increase cutting regime having as signal the vibration level.
Fig.1. Milling machine stand
Cristian Gelmereanu et al. / Procedia Economics and Finance 15 (2014) 1563 – 1569
3. Literature review The first concept of High Speed Machining (1931) was presented by Carl J Salomon (Salomon, C.,1931), who is considered as the father of high speed machining. The results of his experiments, carried out on nonferrous metals such as Aluminium, Copper and Bronze, using helical milling cutters at speeds up to 18067 m/min (surface metres per minute) revealed that with the increase of cutting speed, the cutting temperature increases up to a maximum value close to the melting point of the material and then decreases with further increase in speed (King, R.I, 1984). Bhattacharyya et.al investigated on cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques (Bhattacharyya et.al, 2007). The tool wear in high speed milling using detection process approach has been done by Kious et.al. Estimation of tool wear during CNC milling using neural network-based sensor fusion was implemented by Ghosh et.al. On-line metal cutting tool condition monitoring using multi-layer perceptron neural networks was studied by Lister and Dimla. Tool condition monitoring using artificial intelligence methods has been carried out by Balazinski et.al. Cho and Ko estimated tool wear length in finish milling using a fuzzy inference algorithm. Intelligent process supervision for predicting tool wear in machining processes was done Alique by and Haber. Ning and Veldhuis analyzed mechanistic modeling of ball end milling including tool wear. (W. Yan, 1999) Development and processes manufacturing based on cutting speed from 10 m/min in 1800, up to 100 m / min in 1900, a thousand feet per minute in 1980 to several thousand in 1994 (figure 2) imposed default also a tendency to continue studying these processes in terms of the elements that contribute directly to their achievement, their deployment conditions, the level of cutting regime.
Fig.2. High Speed Machining
A very general definition of high speed processing method is proposed by French researcher Alain Defretin in his book “Usinage a Grande Vitesse”. (Ivan, R., 2011) "Processing of high speed milling includes all processes (high feed rates or high speed spindle rotation), processes that are superior to those that are considered today controlled from a practical standpoint."( Defretin, A.) In the case of high-speed processing, the main axis of the spindle is greater than 15,000 revolutions per minute, as illustrated above. Growth rate has a reverse effect as it reduces cutting forces, it improves the quality of machined surfaces and improves the natural fragmentation of chips, etc.. In this sense, today more than yesterday and less than tomorrow, high speed cutting technology is recognized as one of the key processing technologies in order to
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obtain increased productivity and a precision manufacturing performance. In the case of high-speed processing, the processing speed is higher than the propagation speed of the heat in the work piece. Below there is a comparison of speeds used during machining some selected materials using conventional and HSM methods. Table 1. Conventional vs. High Speed Machining (http://fstroj.utc.sk/journal/engl/papers/034_2002.pdf)
Work material Aluminium Cast iron
Steel
soft ductile Free mach steel alloy stainless Hardness HRC65 Titanium
Superalloy
Solid Tools (end mills, drills), WC, coated WC, PCD, ceramic Typical cutting High cutting Speed [m/min] speed [m/min] >305 (WC, >3050 (WC, PCD) PCD) 152
366
107 107 76 107
244 366 244 152
24
122
38
61
46
76
Indexable Tools (shell mills, face mills) WC, ceramic, CBN, PCD Typical cutting High cutting speed [m/min] speed [m/min] >3658 (WC, >610 PCD) 1219 (sialon, 366 ceramic) 244 914 (ceramic) 366 610 213 366 152 274 30 (WC) 91 46 (WC), 183 (CBN, ceramic) (CBN, ceramic) 46 91 84 (WC) 213 366 (sialon, (sialon) ceramic)
In fact, the question is what are the advantages of high speed milling compared to conventional milling (table 2) Table 2. The advantages/ disadvantages of using high-speed milling
Advantages of using high-speed milling Economic efficiency and increased production A high-quality machined surface with a roughness of up to 0.2 ȝm; Possible to obtain rapid milling of thin walls up to 0.02 mm thick to 20 mm; Temperature transferred into the surface layer of the workpiece is less than the feed rate; the heat doesn’t have time to propagate in the part; Achieve higher dimensional precision than with conventional process;
Disadvantages of using high-speed milling The need for qualified personnel. It required a good knowledge of the cutting process, of the equipment; Acceleration and deceleration are very fast processes, many stops and starts that lead to premature wear of the machine tools; The consequences are more severe for human errors or software failures; Need to take additional safety measures (doors closed during processing, tools verification etc.)
Thus, this procedure aims to increase productivity, lower the costs and also improve the quality of the made parts (table 3). (http://www.slideshare.net/hurco/high-speed-machining-hurco-imts-2012)
Cristian Gelmereanu et al. / Procedia Economics and Finance 15 (2014) 1563 – 1569
Table 3. Traditional Processing vs High Speed Machining
Hourly Shop Rate Hourly Burden Rate Cycle Time Cost to Produce Quoted Price Profit per Part Profitx100 pieces Profitx500pieces
Traditional Processing (12K) 60.00$ 35.00$ 11min 16sec 6.57$ 13.50$ 6.93$ 693.00$ 3465.00$
High Speed Machining (12K) 60.00$ 35.00$ 2min 51sec 1.67$ 13.50$ 11.83$ 1183.00$ 5915.00$
High Speed Machining (18K) 60.00$ 35.00$ 1min 54sec 1.11$ 13.50$ 12.39$ 1239.00$ 6195.00$
However, this quality is directly influenced by the state of the cutting tool, which is exposed to wear during machining. Thus control of wear during the cutting process proves to be important. What unites all these terms "target" are the cutting parameters, which can optimize the milling operation (reduced wear, reduced dynamic aspects) and thus reduce manufacturing costs by placing the operation in a stable and intermediate processing zone, and reducing the cycle time, while increasing productivity. For this are consider the following parameters: (Ivan, R., 2011) • Spindle speed (rpm) • Feed rate (mm/min) • Depth of cut (mm) • Cycle time (min) The experiments were conducted in a ZXX6350ZA vertical axis milling machine using tool life 20 mm diameter cutter mill with 4 cutter inserts. The cutter mill was made by high speed steel-E (HSS-E). Workpieces used for this experiment was Al 7075 (Table 4) (http://fstroj.utc.sk/journal/engl/papers/034_2002.pdf). Table 4. Cutting data
No.
Spindle speed (rpm)
Feed Rate Depth of cut (mm) Cycle time (min) (mm/min) 1 95 132 0.2 26.4 2 360 98 0.6 45.5 3 565 200 0.2 38.7 4 950 98 1 15.7 5 1500 132 0.4 14 6 18000 143 1.5 15.6 7 24100 1360 1 35 8 24050 1390 0.4 5 9 35000 1750 0.2 8.02 10 40000 2120 3 12 11 40500 2200 1.5 3.5 With the parameters in Table 4 was tried modeling and generation of the cycle time using ANN reducing cycle time due to use of high-speed milling.
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4. Tool life determination using Artificial Neural Networks (ANN) An Artificial Neural Network (ANN) is a mathematical model or computational model that is inspired by the structure and/or functional aspects of biological neural networks. According to Simon Haykin, neural network is “a massively parallel distributed processor with the natural tendency to store experimental knowledge and make them available for use”. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase (Haykin, 2009). The similarity with the human brain consists in learning request before acquiring knowledge. Learning takes place through the entries that are brought in the system. Knowledge is stored in connections interneurons, ie neural weights, which stores the results (Beale et al., 2011). We used the data from table 4, having in the input data the spindle speed (rpm), the feed rate (mm/min)and the depth of cut (mm), and in the output layer the cycle time. The Levenberg-Marquardt algorithm was used for training and the MLP (Multilayer perceptrons) training was done automatically using the elaborated software package.(Campean, E., 2012) The ANN consists in 5 neurons. simulating first with 11 pairs of input in the input layer. It is noted that the number of neurons chosen, the number of intermediate layers and the activation functions were well chosen because, after only 3 iterations (epochs), neural network has reached a result in the indicated error, having a running time of five seconds. It can be seen that the error of 10-2 is satisfactory. The error gradient is 10-15. Along with this result, is made available a graphic illustration of the training process. Table 5 show the result: Table 5. The ANN results
No.
Spindle speed (rpm)
Feed Rate (mm/min)
Depth of cut (mm)
Cycle (min)
time
ANN cycle time
Error (%)
1
95
132
0.2
26.4
25.71
0.0069
2
565
200
0.2
38.7
41.01
-0.0231
3
950
98
1
15.7
15.96
-0.0026
4
24100
1360
1
35
35.62
-0.0062
5
24050
1390
0.4
5
5.31
-0.0031
6
35000
1750
0.2
8.02
8.36
-0.0034
7
40000
2120
3
12
11.91
0.0009
8
40500
2200
1.5
3.5
2.92
0.0058
9
20000
900
1.5
8.3
7.52
0.0078
10
10095
4040
1
14.7
12.56
0.0214
11
8514
5110
1
12.3
12.01
0.0029
12
12038
4820
0.4
3.6
3.02
0.0058
13
13403
5360
0.4
3.5
3.02
0.0048
14
11196
3360
1.5
4.2
3.5
0.007
The results shown in table 5 indicate approximately the same values, what means that the ANN approach is suitable for such application. The great advantage of the ANN is the time needed to obtain the results.
Cristian Gelmereanu et al. / Procedia Economics and Finance 15 (2014) 1563 – 1569
5. Conclusion In this study, ANN for modeling and predicting cycle time for milling parts made of Aluminum material was developed. Given the accuracy that was achieved it is safe to conclude that the ANN approach is suitable for such application. It was found that (ANN) prediction correlates very well with the experimental results. Given the results obtained by the ANN simulation was designed an algorithm that involves increasing the cutting regime during machining. Maximum cutting regime parameters (cutting depth for beginning and advanced) will be equal to the acceptable level of vibration.
Acknowledgements This paper was supported by the project "Improvement of the doctoral studies quality in engineering science for development of the knowledge based society-QDOC” contract no. POSDRU/107/1.5/S/78534, project co-funded by the European Social Fund through the Sectorial Operational Program Human Resources 2007-2013.
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