Abstract
Scarcity of high-cost silica sand, casting defect such as hot tear in hard moulds and casting ejection problem after solidification are the key industrial problems. Sawdust is a by-product of wood working industries, and economic disposal of sawdust in these industries is a growing concern to the wood industries. The present work utilized sawdust as an additive in preparing mould cavity for casting applications. Sand mould properties such as compression strength (CS), mould hardness (MH), gas evolution (GE), permeability (P) and collapsibility (CP) will have good impact on the quality of castings. The effect of variables, namely quantity of resin, hardener, sawdust and setting time, on no-bake furan-bonded sand system is studied in the present research work. The experiments are conducted as per design of experiments, and the data are used to investigate the effect of individual and combined parametric contributions towards responses and establish nonlinear input–output relationships. All nonlinear regression models (that is, input–output relationships) are found to be statistically adequate. The input–output relations are analysed and presented for each of the response with the help of surface plots. Further, the models are found to predict the output close to the experiment (target value). The grand average value in predicting responses is found to be equal to 5.03%. The multi-objective optimization of responses with conflicting nature (minimize: GE and CP; maximize: CS, P and MH) is carried out with the help of global fitness function values determined using genetic algorithm, particle swarm optimization, teacher–learner-based optimization and JAYA algorithms. The optimized values of process parameters that resulted in best set of responses are found to be equal to 60 min, 2.01%, 0.6% and 0.93% for setting time, quantity of resin, hardener and sawdust, respectively. Two automobile coupling parts are cast by pouring molten aluminium into the mould cavity with the optimized and non-optimum sand mould conditions. Further, these two cast components are tested for their quality characteristics, such as surface finish, yield strength, hardness, density and secondary dendrite arm spacing. It has been observed that the quality characteristics of castings produced in mould with optimized parameters are found to be much better.
Similar content being viewed by others
References
Alonso-Santurde R, Coz A, Quijorna N, Viguri JR, Andres A (2010) Valorization of foundry sand in clay bricks at industrial scale. J Ind Ecol 14(2):217–230. https://doi.org/10.1111/j.1530-9290.2010.00233.x
Bobrowski A, Grabowska B (2012) The impact of temperature on furan resin and binder structure. Metall Foundry Eng 38(1):73–80. https://doi.org/10.7494/mafe.2012.38.1.73
Ji S, Wan L, Fan Z (2001) The toxic compounds and leaching characteristics of spent foundry sands. Water Air Soil Pollut 132:347–364. https://doi.org/10.1023/A:1013207000046
Chojecki A, Mocek J (2011) Gas pressure in sand mould poured with cast iron. Arch Foundry Eng 11(1):9–14
Holtzer M, Danko R, Kmita A (2016) Influence of a reclaimed sand addition to moulding sand with furan resin on its impact on the environment. Water Air Soil Pollut 227(1):16. https://doi.org/10.1007/s11270-015-2707-9
Mizuki T, Kanno T (2018) Establishment of casting manufacturing technology by introducing an artificial sand mold with furan resin and realizing a clean foundry. Int J Metalcast 12(4):772–778. https://doi.org/10.1007/s40962-018-0209-4
Major-Gabrys K, StM Dobosz, Jakubski J (2011) The estimation of harmfulness for environment of moulding sand with biopolymer binder based on polylactide. Arch Foundry Eng 11:69–72
Major-Gabrys K, StM Dobosz, Danko R et al (2011) The estimation of ability to reclaim of moduling sands with biopolymer binders. Arch Foundry Eng 11:79–84
Mocek J, Samsonowicz J (2011) Changes of gas pressure in sand mould during cast iron pouring. Arch Foundry Eng 11:87–92
Izdebska-Szanda I, Szanda M, Matuszewski S (2011) Technological and ecological studies of moulding sands with new inorganic binders for casting of non-ferrous metal alloys. Arch Foundry Eng 11:43–48
Wang JN, Fan ZT, Wang HF, Dong XP, Huang NY (2007) An improved sodium silicate binder modified by ultra-fine powder materials. China Foundry 4(1):26–30
Roy T (2013) Analysis of casting defects in foundry by computerised simulations (CAE)-A new approach along with some industrial case studies. In: Transactions of 61st Indian Foundry Congress –9
Nwajagu CO, Okafor ICI (1989) A study of the moulding properties of sand bonded by ukpor clay. Appl Clay Sci 4:211–223. https://doi.org/10.1016/0169-1317(89)90030-6
Reddy NS, Yong-Hyun B, Seong-Gyeong K, Young HB (2014) Estimation of permeability of green sand mould by performing sensitivity analysis on neural networks model. J Korea Foundry Soc 34(3):107–111. https://doi.org/10.7777/jkfs.2014.34.3.107
Chate GR, Patel GCM, Deshpande AS, Parappagoudar MB (2018) Modeling and optimization of furan molding sand system using design of experiments and particle swarm optimization. Proc Inst Mech Eng Part E J Process Mech Eng 232(5):579–598. https://doi.org/10.1177/0954408917728636
Khandelwal H, Ravi B (2015) Effect of binder composition on the shrinkage of chemically bonded sand cores. Mater Manuf Processes 30(12):1465–1470. https://doi.org/10.1080/10426914.2014.994779
Parappagoudar MB, PratiharDK Datta GL (2007) Linear and non-linear statistical modelling of green sand mould system. Int J Cast Met Res 20(1):1–13. https://doi.org/10.1179/136404607X184952
Surekha B, Hanumantha Rao D, Krishna G, Rao M, Vundavilli PR, Parappagoudar MB (2012) Modeling and analysis of resin bonded sand mould system using design of experiments and central composite design. J Manuf Sci Prod 12:31–50. https://doi.org/10.1515/jmsp-2012-0003
Habib SS (2009) Study of the parameters in electrical discharge machining through response surface methodology approach. Appl Math Model 33(12):4397–4407. https://doi.org/10.1016/j.apm.2009.03.021
Patel GCM, Krishna P, Parappagoudar MB (2016) Squeeze casting process modeling by a conventional statistical regression analysis approach. Appl Math Model 40(15–16):6869–6888. https://doi.org/10.1016/j.apm.2016.02.029
Pearl J (1984) Heuristics: intelligent search strategies for computer problem solving. Addison-Wesley, Boston
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison Wesley, Reading
AlRashidi MR, El-Hawary ME (2009) Applications of computational intelligence techniques for solving the revived optimal power flow problem. Electr Power Syst Res 79(4):694–702. https://doi.org/10.1016/j.epsr.2008.10.004
Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132. https://doi.org/10.1016/j.amc.2009.03.090
Holland HJ (1975) Adaptation in natural artificial systems: an introductory analysis with application to biology, control, and artificial intelligence. The University of Michigan Press, Ann Arbor
Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359. https://doi.org/10.1023/A:1008202821328
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
Sierra MR, Coello CC (2005) Improving PSO-based multi-objective optimization using crowding, mutation and e-dominance. Evolut Multi-criterion Optim 3410:505–520. https://doi.org/10.1007/978-3-540-31880-4_35
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06. Erciyes University, Engineering Faculty, Computer Engineering Department, Vol. 200
Jain NK, Jain VK, Deb K (2007) Optimization of process parameters of mechanical type advanced machining processes using genetic algorithms. Int J Mach Tool Manuf 47(6):900–919. https://doi.org/10.1016/j.ijmachtools.2006.08.001
Surekha B, Kaushik LK, Panduy AK, Vundavilli PR, Parappagoudar MB (2012) Multi-objective optimization of green sand mould system using evolutionary algorithms. Int J Adv Manuf Tech 58:9–17. https://doi.org/10.1007/s00170-011-3365-8
Patel GCM, Krishna P, Vundavilli PR, Parappagoudar MB (2016) Multi-objective optimization of squeeze casting process using evolutionary algorithms. Int J Swarm Intell Res 7(1):57–76. https://doi.org/10.4018/IJSIR.2016010103
Samanta S, Chakraborty S (2011) Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm. Eng Appl Artif Intell 24:946–957. https://doi.org/10.1016/j.engappai.2011.03.009
Yildiz AR (2012) A comparative study of population-based optimization algorithms for turning operations. Inf Sci 210:81–88. https://doi.org/10.1016/j.ins.2012.03.005
Vundavilli PR, Kumar JP, Parappagoudar MB (2013) Weighted average-based multi-objective optimization of tube spinning process using non-traditional optimization techniques. Int J Swarm Intell Res 4(3):42–57. https://doi.org/10.4018/ijsir.2013070103
Venkata Rao R, Kalyankar VD, Waghmare G (2013) Parameters optimization of selected casting processes using teaching–learning-based optimization algorithm. Appl Math Model 38(23):5592–5608. https://doi.org/10.1016/j.apm.2014.04.036
Venkata Rao R, Kalyankar VD (2013) Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm. Eng Appl Artif Intell 26:524–531. https://doi.org/10.1016/j.engappai.2012.06.007
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315. https://doi.org/10.1016/j.cad.2010.12.015
Rao RV, Patel V (2013) Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm. Appl Math Model 37(3):1147–1162. https://doi.org/10.1016/j.apm.2012.03.043
Rao RV, Rai DP, Balic J (2016) Surface grinding process optimization using Jaya algorithm. In: Kruse R (ed) Computational intelligence in data mining, vol 2. Springer, New Delhi, pp 487–495. https://doi.org/10.1007/978-81-322-2731-1_46
Rao RV (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34. https://doi.org/10.5267/j.ijiec.2015.8.004
Rao RV (2019) Single-and multi-objective optimization of casting processes using Jaya algorithm and its variants. In: Venkata Rao R (ed) Jaya: an advanced optimization algorithm and its engineering applications. Springer, Cham, pp 273–289. https://doi.org/10.1007/978-3-319-78922-4_9
Rao RV (2019) Jaya optimization algorithm and its variants. In: Venkata Rao R (ed) Jaya: an advanced optimization algorithm and its engineering applications. Springer, Cham, pp 9–58. https://doi.org/10.1007/978-3-319-78922-4_2
Mahapatra SS, Patnaik A (2007) Optimization of wire electrical discharge machining (WEDM) process parameters using Taguchi method. Int J Adv Manuf Tech 34(9–10):911–925. https://doi.org/10.1007/s00170-006-0672-6
Acknowledgements
The authors would like to thank Mr. Ravi Sangolli, VRJ Traders, Belagaum, Mr. Sadanand Humbarwadi, Director of Foundry Cluster, Belgaum, Abhishek Alloys Pvt. Ltd, and AKP Foundry, Belagaum, India, for their constant support in successful conduction of research work.
Author information
Authors and Affiliations
Corresponding author
Additional information
Technical Editor: Márcio Bacci da Silva, Ph.D.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
Appendix 1: Experimental matrices of nonlinear model on central composite design for different responses (sand mould properties)
Exp. no | Input variables (coded) | Sand mould properties | |||||||
---|---|---|---|---|---|---|---|---|---|
X 1 | X 2 | X 3 | X 4 | Compression strength (KPa) | Permeability | Mould hardness | Gas evolution (ml/gm) | Collapsibility (KPa) | |
1 | − 1 | − 1 | − 1 | − 1 | |||||
2 | 1 | − 1 | − 1 | − 1 | |||||
3 | − 1 | 1 | − 1 | − 1 | |||||
4 | 1 | 1 | − 1 | − 1 | |||||
5 | − 1 | − 1 | 1 | − 1 | |||||
6 | 1 | − 1 | 1 | − 1 | |||||
7 | − 1 | 1 | 1 | − 1 | |||||
8 | 1 | 1 | 1 | − 1 | |||||
9 | − 1 | − 1 | − 1 | 1 | |||||
10 | 1 | − 1 | − 1 | 1 | |||||
11 | − 1 | 1 | − 1 | 1 | |||||
12 | 1 | 1 | − 1 | 1 | |||||
13 | − 1 | − 1 | 1 | 1 | |||||
14 | 1 | − 1 | 1 | 1 | |||||
15 | − 1 | 1 | 1 | 1 | |||||
16 | 1 | 1 | 1 | 1 | |||||
17 | − 1 | 0 | 0 | 0 | |||||
18 | 1 | 0 | 0 | 0 | |||||
19 | 0 | − 1 | 0 | 0 | |||||
20 | 0 | 1 | 0 | 0 | |||||
21 | 0 | 0 | − 1 | 0 | |||||
22 | 0 | 0 | 1 | 0 | |||||
23 | 0 | 0 | 0 | − 1 | |||||
24 | 0 | 0 | 0 | 1 | |||||
25 | 0 | 0 | 0 | 0 | |||||
26 | 0 | 0 | 0 | 0 | |||||
27 | 0 | 0 | 0 | 0 |
Appendix 2: Input–output data of test cases
Test no. | Input variables | Experimental output values | |||||||
---|---|---|---|---|---|---|---|---|---|
A | B | C | D | Compression strength (KPa) | Permeability | Mould hardness | Collapsibility (KPa) | Gas evolution (ml/gm) | |
1 | 2.15 | 0.5 | 100 | 1.40 | 190 | 118 | 74.1 | 148 | 10.1 |
2 | 2.05 | 0.4 | 119 | 1.90 | 175 | 120 | 71.4 | 132 | 9.9 |
3 | 2.15 | 0.45 | 115 | 1.30 | 246 | 110 | 75.3 | 194 | 9.4 |
4 | 2.00 | 0.55 | 067 | 1.50 | 204 | 123 | 68.6 | 138 | 8.8 |
5 | 2.15 | 0.40 | 103 | 0.55 | 185 | 096 | 75.2 | 142 | 9.1 |
6 | 1.95 | 0.50 | 073 | 0.95 | 158 | 113 | 70.4 | 120 | 8.2 |
7 | 1.85 | 0.55 | 109 | 0.55 | 248 | 117 | 77.7 | 184 | 10.4 |
8 | 1.95 | 0.45 | 068 | 1.65 | 120 | 106 | 61.4 | 088 | 10.8 |
9 | 1.80 | 0.50 | 079 | 0.85 | 178 | 103 | 68.9 | 126 | 10.6 |
10 | 2.10 | 0.40 | 113 | 1.70 | 184 | 124 | 76.2 | 134 | 9.6 |
11 | 2.05 | 0.60 | 118 | 1.15 | 320 | 126 | 75.4 | 240 | 8.1 |
12 | 1.85 | 0.55 | 089 | 0.65 | 194 | 114 | 75.8 | 155 | 9.4 |
13 | 1.80 | 0.45 | 088 | 1.75 | 87 | 98 | 64.5 | 078 | 12.5 |
14 | 1.90 | 0.55 | 062 | 0.50 | 232 | 107 | 70.4 | 153 | 7.2 |
15 | 2.00 | 0.60 | 091 | 1.75 | 199 | 128 | 77.2 | 134 | 11.3 |
16 | 1.95 | 0.55 | 077 | 0.75 | 187 | 105 | 70.4 | 138 | 9.1 |
17 | 2.00 | 0.50 | 088 | 0.60 | 156 | 093 | 74.8 | 124 | 9.4 |
18 | 1.95 | 0.50 | 118 | 1.45 | 232 | 119 | 70.0 | 182 | 8.2 |
19 | 2.20 | 0.40 | 087 | 0.90 | 128 | 103 | 75.8 | 103 | 10.8 |
20 | 1.80 | 0.60 | 114 | 0.75 | 298 | 129 | 77.7 | 232 | 9.7 |
Appendix 3: Summary of test case results for the responses—CS, P, MH and CP
Test no. | Compression strength (KPa) | Permeability | ||||||
---|---|---|---|---|---|---|---|---|
Experimental value | CCD prediction | Deviation (%) | Absolute deviation (%) | Experimental Value | CCD prediction | Deviation (%) | Absolute deviation (%) | |
1 | 190 | 200.04 | − 5.29 | 5.29 | 118 | 113.11 | 4.15 | 4.15 |
2 | 175 | 157.88 | 9.78 | 9.78 | 120 | 112.79 | 6.01 | 6.01 |
3 | 246 | 233.86 | 4.94 | 4.94 | 110 | 113.40 | − 3.09 | 3.09 |
4 | 204 | 199.06 | 2.42 | 2.42 | 123 | 118.91 | 3.32 | 3.32 |
5 | 185 | 167.93 | 9.23 | 9.23 | 96 | 102.01 | − 6.26 | 6.26 |
6 | 158 | 166.05 | − 5.09 | 5.09 | 113 | 108.43 | 4.05 | 4.05 |
7 | 248 | 227.06 | 8.44 | 8.44 | 117 | 109.26 | 6.61 | 6.61 |
8 | 120 | 134.17 | − 11.81 | 11.81 | 106 | 110.64 | − 4.38 | 4.38 |
9 | 178 | 160.15 | 10.03 | 10.03 | 103 | 107.18 | − 4.06 | 4.06 |
10 | 184 | 162.74 | 11.56 | 11.56 | 124 | 116.54 | 6.01 | 6.01 |
11 | 320 | 310.41 | 3.00 | 3.00 | 126 | 119.96 | 4.80 | 4.80 |
12 | 194 | 188.45 | 2.86 | 2.86 | 114 | 109.48 | 3.97 | 3.97 |
13 | 87 | 95.36 | − 9.61 | 9.61 | 98 | 102.81 | − 4.91 | 4.91 |
14 | 232 | 216.67 | 6.61 | 6.61 | 107 | 104.10 | 2.71 | 2.71 |
15 | 199 | 188.37 | 5.34 | 5.34 | 128 | 122.57 | 4.24 | 4.24 |
16 | 187 | 192.85 | − 3.13 | 3.13 | 105 | 109.32 | − 4.12 | 4.12 |
17 | 156 | 163.63 | − 4.89 | 4.89 | 93 | 100.92 | − 8.52 | 8.52 |
18 | 232 | 211.40 | 8.88 | 8.88 | 119 | 111.90 | 5.96 | 5.96 |
19 | 128 | 145.75 | − 13.87 | 13.87 | 103 | 109.91 | − 6.71 | 6.71 |
20 | 298 | 271.69 | 8.83 | 8.83 | 129 | 121.91 | 5.50 | 5.50 |
Mould hardness | Collapsibility (KPa) | |||||||
---|---|---|---|---|---|---|---|---|
1 | 74.1 | 73.21 | 1.20 | 1.20 | 148 | 153.73 | − 3.87 | 3.87 |
2 | 71.4 | 71.85 | − 0.63 | 0.63 | 132 | 119.07 | 9.79 | 9.79 |
3 | 75.3 | 73.25 | 2.72 | 2.72 | 194 | 180.22 | 7.10 | 7.10 |
4 | 68.6 | 68.03 | 0.83 | 0.83 | 138 | 132.37 | 4.08 | 4.08 |
5 | 75.2 | 76.99 | − 2.38 | 2.38 | 142 | 136.82 | 3.65 | 3.65 |
6 | 70.4 | 69.01 | 1.97 | 1.97 | 120 | 124.88 | − 4.07 | 4.07 |
7 | 77.7 | 75.58 | 2.73 | 2.73 | 184 | 186.98 | − 1.62 | 1.62 |
8 | 61.4 | 63.81 | − 3.93 | 3.93 | 88 | 92.84 | − 5.50 | 5.50 |
9 | 68.9 | 70.79 | − 2.74 | 2.74 | 126 | 128.04 | − 1.62 | 1.62 |
10 | 76.2 | 73.27 | 3.85 | 3.85 | 134 | 122.23 | 8.78 | 8.78 |
11 | 75.4 | 74.29 | 1.47 | 1.47 | 240 | 229.28 | 4.47 | 4.47 |
12 | 75.8 | 74.69 | 1.46 | 1.46 | 155 | 149.69 | 3.42 | 3.42 |
13 | 64.5 | 66.3 | − 2.79 | 2.79 | 78 | 76.83 | 1.50 | 1.50 |
14 | 70.4 | 70.61 | − 0.30 | 0.30 | 153 | 156.56 | − 2.32 | 2.32 |
15 | 77.2 | 74.43 | 3.59 | 3.59 | 134 | 127.01 | 5.21 | 5.21 |
16 | 70.4 | 72.4 | − 2.84 | 2.84 | 138 | 143.72 | − 4.15 | 4.15 |
17 | 74.8 | 73.28 | 2.03 | 2.03 | 124 | 135.03 | − 8.89 | 8.89 |
18 | 70 | 70.13 | − 0.19 | 0.19 | 182 | 167.21 | 8.13 | 8.13 |
19 | 75.8 | 74.91 | 1.17 | 1.17 | 103 | 113.08 | − 9.79 | 9.79 |
20 | 77.7 | 76.49 | 1.56 | 1.56 | 232 | 210.21 | 9.39 | 9.39 |
Appendix 4: Summary of the test case results for the response—GE
Exp. no. | Experimental GE (ml/gm) | CCD prediction | Deviation (%) | Absolute deviation (%) |
---|---|---|---|---|
1 | 10.1 | 10.56 | − 4.56 | 4.56 |
2 | 9.9 | 9.67 | 2.34 | 2.34 |
3 | 9.4 | 8.67 | 7.72 | 7.72 |
4 | 8.8 | 8.39 | 4.66 | 4.66 |
5 | 9.1 | 9.97 | − 9.54 | 9.54 |
6 | 8.2 | 9.06 | − 10.49 | 10.49 |
7 | 10.4 | 9.95 | 4.33 | 4.33 |
8 | 10.8 | 10.90 | − 0.92 | 0.92 |
9 | 10.6 | 11.17 | − 5.35 | 5.35 |
10 | 9.6 | 9.75 | − 1.60 | 1.60 |
11 | 8.1 | 7.67 | 5.31 | 5.31 |
12 | 9.4 | 10.24 | − 8.91 | 8.91 |
13 | 12.5 | 13.98 | − 11.86 | 11.86 |
14 | 7.2 | 7.59 | − 5.40 | 5.40 |
15 | 11.3 | 10.41 | 7.90 | 7.90 |
16 | 9.1 | 8.68 | 4.64 | 4.64 |
17 | 9.4 | 9.92 | − 5.52 | 5.52 |
18 | 8.2 | 8.48 | − 3.38 | 3.38 |
19 | 10.8 | 11.01 | − 1.94 | 1.94 |
20 | 9.7 | 9.34 | 3.71 | 3.71 |
Rights and permissions
About this article
Cite this article
Chate, G.R., Patel, G.C.M., Bhushan, S.N.B. et al. Comprehensive modelling, analysis and optimization of furan resin-based moulding sand system with sawdust as an additive. J Braz. Soc. Mech. Sci. Eng. 41, 183 (2019). https://doi.org/10.1007/s40430-019-1684-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s40430-019-1684-0