Int J Adv Manuf Technol (2013) 66:1315–1318 DOI 10.1007/s00170-012-4409-4
ORIGINAL ARTICLE
Automation and parameters optimization in production line: a case of study Marco A. Jimenez & Susana V. Gutierrez & Giovanni Lizarraga & Mauricio A. Garza & David S. Gonzalez & Jorge L. Acevedo & Mario C. Osorio & Roberto A. Rodríguez
Received: 27 March 2012 / Accepted: 23 July 2012 / Published online: 24 August 2012 # Springer-Verlag London Limited 2012
Abstract A metal-forming production line was automated and the critical parameters of the process of feeding to the press were determined using experiments designed to optimize production with a minimum number of tests. In order to carry out a factorial design of experiments, we registered the level of lubricant, the speed of the feeder, the advance of raw material, and the pressure of the lubrication to be taken as experimental factors. The results of the experiment design showed that the advance of raw material and the interactions between the level of lubricant and the pressure of the lubricant and between the feeder speed, the level of lubricant, and the pressure of the lubricant are the main parameters for feeding to the press for production optimization. Several tests were carried out and the production in the automated and optimized process increased more than 400 % with respect to the artisan process. This paper demonstrates that optimization of the feed to the press in a production line is very important for high operational
M. A. Jimenez (*) : S. V. Gutierrez : G. Lizarraga : M. A. Garza : D. S. Gonzalez : J. L. Acevedo : M. C. Osorio Corporación Mexicana de Investigación en Materiales S.A. de C.V., Ciencia y Tecnología 790, Saltillo, Coahuila, Mexico e-mail:
[email protected] URL: http://www.comimsa.com R. A. Rodríguez Universidad Popular Autónoma Del Estado De Puebla, A. C., 21 sur No. 1103 Colonia Santiago, Z.C., 72160 Puebla, Puebla, Mexico R. A. Rodríguez U.A.E.M.-C.I.I.C.A.P. Av. Universidad 1001, Col. Chamilpa, 62500 Cuernavaca, Morelos, Mexico
efficiency and for maintaining a factory competitive and sustainable. Keywords Metal forming . Metal stamping . Design of experiments . Automation . Optimization
1 Introduction Recently, with increasing demand for quality and volume in production due to globalization, it is necessary to implement strategies in order to produce high-quality products in large volumes in less time. For this reason, companies in different industries have been selecting automation of its production lines as a solution to the challenges of volume and quality. Metal forming is a powerful tool for the manufacturing industry because of the large number of products that may be obtained, such as desktops, bodies of cars, fuselages of airplanes, and tins for drinks among others [1]. For these products to be made in large quantities and high quality, it is very important for metal forming to be done correctly. The process of metal forming can be classified into different processes: dies, stamping, extrusion, forging, and folding [2, 3]. The die process for metals has several steps such as (1) selection of raw materials in roll or plate form; (2) grooving, sawing, or cutting; (3) feeding into the press; (4) punching or die stamping (compound, progressive, or transference); and finally (5) painting if necessary and cleaning. Each step must be analyzed meticulously. Unfortunately, the industry requires quick reactions to increases in demand and this makes it very hard for factories to ensure scientific validation in changes on production lines. This makes it imperative for industries
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Fig. 1 Circular pallet
Int J Adv Manuf Technol (2013) 66:1315–1318
objective conclusions as to output variables [8]; however, the designing of experiments is the first step used to determine if is convenient use linear regression analysis or another optimization technique. For this paper, analysis and automation of the feed line to the press for metal forming was carried out using factorial experiment design to obtain optimized production. Then the automated and optimized process was compared with the hand-worked process. The objective of this work is to present a case of study where a problem of parameter optimization and automation for feed a press is solved using design of experiments and neural network [9]. The results of design were implemented in factory, effectively solving a practical problem. The details of the development of this work are presented in the following sections, describing the considerations and decision made in order to optimize parameter and implement the automation.
2 Automation details
Fig. 2 Feeder
to us scientific bases to implement processes in order to avoid problems when extreme changes in production volume are required. One way to be sure that a process has been correctly implemented is to optimize it. There are several optimization techniques, such as genetic algorithms [4], artificial neural networks [5], industrial process controls, [6], and linear regressions, among others. Each one of the techniques mentioned has advantages, but in each technique a design of experiments [7] is required in order to minimize the number of tests needed to obtain satisfactory statistical results since the idea is to plan experiments through the analysis of input variables in order to obtain valid and Fig. 3 Lubricant dispenser
For automation of feed to the press, we used a circular pallet (Fig. 1), a feeder (Fig. 2), and a lubricant dispenser (Fig. 3) with the characteristic listed in Tables 1, 2, and 3. The selection of machinery was based on the factory's need to produce small pieces of steel caliber 16.
3 Design of experiments The goal of this work was to minimize excess material on the pieces then carry out an experiment designed in order to find the best technique for optimization of our press feed system in hopes that the number of good pieces would increase substantially and eliminate re-work that does not add value to our product and also the waste in excess of material on the pieces. With the machinery installed, we can manipulate only four input variables in the press feed process: level of lubrication, feeder speed, raw material advance, and lubrication pressure. We had four states for the level
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Table 1 Technical data of pallet
Table 3 Technical data of lubricant dispenser
Characteristic
Value
Characteristic
Value
Capacity Diameter of pallet Maximum diameter of rolls Pallet velocity Reduction with motor Maximum width of steel sheet Maximum height of rolls Power of motor with direct current
3,500 lb 42 in. 48 in. 0–24 RPM 20/1 or 10/1 6 in. 40 in. 1/2 HP
Maximum width of sheet Minimum caliber of steel sheet Maximum caliber of steel sheet Number of pairs of lubricant applicator rollers Length of lubricant applicator rollers Thickness of roller covers Diameter of lubricant applicator rollers Viscosity range at 100 °F (40 °C)
3 in. 0.010 in. 0.125 in. 1 3 in. 0.25 in. 3 in. 100–800 SUS [10]
Weight of pallet
390 lb
Entrance height for sheet Width of dispenser Length of dispenser Height of dispenser
3.51 in. 4.7 in. 7.75 in. 6.8 in.
of lubrication where in state 1 the sheet is only lubricated on the top, in state 4 the sheet is only lubricated on the bottom, in state 2 the sheet is lubricated on both sides with more lubrication on the top, and in state 3 both sides are lubricated but there is more lubrication on the bottom. For lubrication pressure, we can obtain values from 0 to 60 PSI, increasing PSI by 5, while for feeder speed we can obtain values from 0 up to 100 in/ min and for raw material advance we can obtain values of 0 up to 10 in. with increases of 0.5 in. Because we needed to have a good approach with few but sufficient experiments, we decided to carry out a factorial experiment design with two levels, three central points, and one replicate. Several tests were carried out and the results are shown in Table 4. Analysis of data with interaction between input variables is shown on Table 5.
In Table 4, we can see that excess material was controlled about 80 %, however upon analyzing Table 5 we obtained R 2 076.7 %, R2adjusted ¼ 60:4% , and R2predicted ¼ 27% in agreement with Montgomery [8]. These results indicate a problem in the model of optimization which led us to conclude that we should not use linear regression as an optimization technique, so we looked for another optimization technique, but if we focused our attention on interactions among input variables level, advance, pressure+level, and speed+pressure +level, we obtain less than 5 % significance. In other words, there is more than 95 % trustworthiness in results. Moreover, it is important to remember that we have technical specifications in the machinery of that complicates the implementation of more sophisticated parameters.
4 Results and discussion
5 Conclusions
Considering that the maximum tolerance for excess material is ±0.01 in. in order to avoid re-work and that the specification for length of the piece is 0.655 in., then we were able to consider as correct an output in the range of [0.645, 0.665]. On Table 4 are marked pieces out of correct range or with excess material.
1. A production line was used that needed to control excess of material and the output variable was controlled 80 % by automation via linear regression. 2. Linear regression is a good technique for an optimization approach of parameters for press feed in production; however, there are techniques like artificial intelligence or non-linear correlation, that would be more precise and that should be explored in future work. We should also remember that there are technical specifications for the machinery that make implementation of more sophisticated parameters difficult. 3. Experiment design is a very useful technique for minimizing the number of tests in a factory and in consequence allows the company to spend less money in the implementation of new technology. 4. Interactions of input variables level, advance, pressure+ level, and velocity+pressure+level are controlled by experiments designed and explained by linear regression
Table 2 Technical data of feeder Characteristic
Value
Maximum width of sheet Maximum raw material advance Maximum caliber of steel sheet Roller diameter Precision at speed of 1,200 in./min Yield of 60 rpm for raw material advance Electric connection
6 in. 0 to 99.99 in. 0.060 in. 2 in. ±0.002 in. 6 in. 220 V/1 phase/60 Hz
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Table 4 Results of experiment design
Table 5 Analysis of interaction between input variables [1]
Input variables Feeder speed {In/ min}
Output
Lubrication pressure {PSI}
Lubrication Raw material Length of level {state} advance {In} piece {In}
10 50 10 50 10 50 10 50 10 50 10 50 10 50 10
15 15 55 55 15 15 55 55 15 15 55 55 15 15 55
1 1 1 1 4 4 4 4 1 1 1 1 4 4 4
1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5
0.657 0.656 0.657 0.647 0.663 0.665 0.665 0.674 0.659 0.665 0.661 0.656 0.665 0.662 0.661
50 10 50 10 50 10 50 10 50 10 50 10 50 10 50 10 50 30
55 15 15 55 55 15 15 55 55 15 15 55 55 15 15 55 55 35
4 1 1 1 1 4 4 4 4 1 1 1 1 4 4 4 4 2.5
3.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 2.5
0.671 0.654 0.653 0.664 0.658 0.669 0.659 0.661 0.670 0.665 0.670 0.665 0.650 0.665 0.661 0.670 0.671 0.656
30 30
35 35
2.5 2.5
2.5 2.5
0.665 0.665
due to the fact that they have a linear correlation with output the variable. 5. Matching academic and innovative processes in factories is possible when adequate techniques are used to establish and obtain common goals. 6. In hand-worked processes, the factory produced around 800–900 pieces per hour while in automated and optimized processes production increase to obtain around 3,600–3,800 pieces/h.
Coefficient Coefficient of error
P value
−0.001188 −0.000594 0.000687 0.000344 0.007188 0.003594 0.003063 0.001531 0.000187 0.000094 0.002438 0.001219 0.000063 0.000031 0.003563 0.001781 −0.002063 −0.001031 −0.002813 −0.001406 0.005563 0.002781
7×10−4 7×10−4 7×10−4 7×10−4 7×10−4 7×10−4 7×10−4 7×10−4 7×10−4 7×10−4 7×10−4
0.413 0.633 0 0.043 0.896 0.101 0.965 0.021 0.162 0.061 0.001
−0.001312 −0.000656
7×10−4
0.366
−0.000813 −0.000406
7×10−4
0.573
7×10−4
0.119
Interaction
Effect
Speed Pressure Level Advance Speed+pressure Speed+level Speed+advance Pressure+level Pressure+advance Level+advance Velocity+pressure+ level Velocity+pressure+ advance Velocity+level+ advance Pressure+level+ advance
0.002313
0.001156
In Table 5 level 0Lubrication level, pressure0lubrication pressure, advance0raw material advance and speed0feeder speed
Acknowledgments The authors are grateful to the National Council of Science and Technology (CONACyT-Mexico) for supporting this project through the call for innovation in the INNOVAPYME modalities for small and medium-sized factories.
References 1. Kalpakjian S, Schmid S (2002) Manufacture, engineering and technology, 4th edn. Pearson Education, Singapore, pp 393–402 2. Semiatin SL et al (1993) Forming and forging, volume 14, 9th edn. ASM International, Russel Township 3. Facultad de ingeniería industrial Escuela colombiana de ingeniería Julio Garavito (2008) CONFORMADO DE METALES-ProtocoloCurso de Materiales 4. Tumkor S, Pochiraju K (2010) Progressive die strip layout optimization for minimum unbalanced moments. J Manuf Sci Eng 24502:1–7 5. Su CT, Chen MC, Chan HL (2005) Applying neural network and scatter search to optimize parameter design with dynamic characteristics. J Oper Res Soc 56:1132–1140 6. Groover M (2001) Industrial process control. McGraw-Hill, New York, Maynard's Industrial Engineering Handbook 7. Chakarov E, Adams JB, Kieffer J (2004) Application of design of experiments methodology to optimization of classical molecular dynamics generation of amorphous Sio2 structure. Mater Sci Eng 12(2):337 8. Montgomery D (2006) Design and analysis of experiments. Limusa Wiley, Mexico 9. Gutierrez S, Lizarraga G, Jimenez M (2011) Optimización de parámetros de una línea de estampado, usando redes neuronales y estrategias evolutivas. In: González Mendoza M, Herrera Alcántara Ó (eds) Sociedad Mexicana de Inteligencia Artificial, Avances recientes en sistemas inteligentes
