Prediction of Mechanical Properties in Rotational Moulding …
11
training data set. Using this model, value of mechanical properties at the given oven
residence time is obtained, and provided value of oven residence time is in the
range of the training data set. It is suggested that, a similar procedure of creating
a machine learning model can be used for the other materials for a range of oven
residence timings. Also, from a reverse approach, from the given mechanical properties, we can obtain the corresponding oven residence time required for that particular
roto moulded product. And this approach can also be used in various fields and
contribute significantly reducing human errors, reducing work time, making things
more economical.
References
1. Crawford RJ (ed) (1996) Rotational moulding of plastics. Research Studies Press, London, UK
2. Unkles PJ (2001) Rotational moulding process. U.S. Patent No. 6,180,203, 30 Jan 2001
3. Ramkumar PL et al (2015) Prediction of heating cycle time in rotational moulding. Mater
Today Proc 2(4–5):3212–3219
4. Shaker R, Rodrigue D (2019) Rotomolding of thermoplastic elastomers based on low-density
polyethylene and recycled natural rubber. Appl Sci 9(24):5430
5. Garg A et al (2019) Handling constraints and raw material variability in rotomolding through
data-driven model predictive control. Processes 7(9):610
6. Ramkumar PL et al (2014) Investigation of melt flow index and impact strength of foamed
LLDPE for rotational moulding process. Procedia Mater Sci 6:361–367
7. Torres FG, Aragon CL (2006) Final product testing of rotational moulded natural fibrereinforced polyethylene. Polym Test 25(4):568–577
8. Rasmussen CE (2003) Gaussian processes in machine learning. In: Summer school on machine
learning. Springer, Berlin
9. Harrington P (2012) Machine learning in action. Manning Publications Co., New York
10. Cevikalp H, Triggs B (2010) Face recognition based on image sets. In: 2010 IEEE computer
society conference on computer vision and pattern recognition. IEEE
11. Biran O, McKeown KR (2017) Human-centric justification of machine learning predictions.
IJCAI 2017:1461–1467
12. Bowling M et al (2006) Machine learning and games. Mach Learn 63(3):211–215
13. Kononenko I (2001) Machine learning for medical diagnosis: history, state of the art and
perspective. Artif Intell Med 23(1):89–109
14. Jahangiri A, Rakha HA (2015) Applying machine learning techniques to transportation mode
recognition using mobile phone sensor data. IEEE Trans Intell Transp Syst 16(5):2406–2417
15. Jain AK, Gupta BB (2016) Comparative analysis of features based machine learning approaches
for phishing detection. In: 2016 3rd international conference on computing for sustainable
global development (INDIACom). IEEE
16. Ayodele TO (2010) Types of machine learning algorithms. In: New advances in machine
learning. IntechOpen, London
17. Alpaydin E (2009) Introduction to machine learning. MIT Press, Cambridge
18. Lison P (2015) An introduction to machine learning. Language Technology Group (LTG), pp
1–35
19. Pedregosa F et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 2825–2830
20. Deo RC (2015) Machine learning in medicine. Circulation 132(20):1920–1930
21. Culkin R, Das SR (2017) Machine learning in finance: the case of deep learning for option
pricing. J Investment Manage 15(4):92–100
22. Wang J et al (2018) Deep learning for smart manufacturing: methods and applications. J Manuf
Syst 48:144–156
11
training data set. Using this model, value of mechanical properties at the given oven
residence time is obtained, and provided value of oven residence time is in the
range of the training data set. It is suggested that, a similar procedure of creating
a machine learning model can be used for the other materials for a range of oven
residence timings. Also, from a reverse approach, from the given mechanical properties, we can obtain the corresponding oven residence time required for that particular
roto moulded product. And this approach can also be used in various fields and
contribute significantly reducing human errors, reducing work time, making things
more economical.
References
1. Crawford RJ (ed) (1996) Rotational moulding of plastics. Research Studies Press, London, UK
2. Unkles PJ (2001) Rotational moulding process. U.S. Patent No. 6,180,203, 30 Jan 2001
3. Ramkumar PL et al (2015) Prediction of heating cycle time in rotational moulding. Mater
Today Proc 2(4–5):3212–3219
4. Shaker R, Rodrigue D (2019) Rotomolding of thermoplastic elastomers based on low-density
polyethylene and recycled natural rubber. Appl Sci 9(24):5430
5. Garg A et al (2019) Handling constraints and raw material variability in rotomolding through
data-driven model predictive control. Processes 7(9):610
6. Ramkumar PL et al (2014) Investigation of melt flow index and impact strength of foamed
LLDPE for rotational moulding process. Procedia Mater Sci 6:361–367
7. Torres FG, Aragon CL (2006) Final product testing of rotational moulded natural fibrereinforced polyethylene. Polym Test 25(4):568–577
8. Rasmussen CE (2003) Gaussian processes in machine learning. In: Summer school on machine
learning. Springer, Berlin
9. Harrington P (2012) Machine learning in action. Manning Publications Co., New York
10. Cevikalp H, Triggs B (2010) Face recognition based on image sets. In: 2010 IEEE computer
society conference on computer vision and pattern recognition. IEEE
11. Biran O, McKeown KR (2017) Human-centric justification of machine learning predictions.
IJCAI 2017:1461–1467
12. Bowling M et al (2006) Machine learning and games. Mach Learn 63(3):211–215
13. Kononenko I (2001) Machine learning for medical diagnosis: history, state of the art and
perspective. Artif Intell Med 23(1):89–109
14. Jahangiri A, Rakha HA (2015) Applying machine learning techniques to transportation mode
recognition using mobile phone sensor data. IEEE Trans Intell Transp Syst 16(5):2406–2417
15. Jain AK, Gupta BB (2016) Comparative analysis of features based machine learning approaches
for phishing detection. In: 2016 3rd international conference on computing for sustainable
global development (INDIACom). IEEE
16. Ayodele TO (2010) Types of machine learning algorithms. In: New advances in machine
learning. IntechOpen, London
17. Alpaydin E (2009) Introduction to machine learning. MIT Press, Cambridge
18. Lison P (2015) An introduction to machine learning. Language Technology Group (LTG), pp
1–35
19. Pedregosa F et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 2825–2830
20. Deo RC (2015) Machine learning in medicine. Circulation 132(20):1920–1930
21. Culkin R, Das SR (2017) Machine learning in finance: the case of deep learning for option
pricing. J Investment Manage 15(4):92–100
22. Wang J et al (2018) Deep learning for smart manufacturing: methods and applications. J Manuf
Syst 48:144–156