Prediction of Mechanical Properties
in Rotational Moulding of LLDPE Using
Machine Learning Model for the Given
Oven Residence Time
Akshay Kumar, PL. Ramkumar, Aman Shukla, and Nikita Gupta
Abstract Enhancing the mechanical property of rotationally mouldable product,
while sustaining the mouldability, becomes a strenuous task. Examining these properties on practically built product has always been laborious and economically challenging. Machine learning can consequently provide an ample assistance for such
exposition. Thus, a new approach has been developed based on machine learning in
the present study in order to predict mechanical properties of roto moulded product
on the basis of oven residence time using linear low density polyethylene (LLDPE).
The two machine learning models utilized for the investigation include linear regression model and polynomial regression model. Variation of mechanical properties of
linear low density polyethylene (LLDPE) roto moulded product with oven residence
time based on the existing statistics has been used as training data for these models.
Preliminary results show that polynomial regression model has given more precise
data than linear regression model. From economic prospective, machine learning
methods were able to achieve acceptable quality results, which are beneficial, since
obtaining the similar amount of data by developing a product practically can prove
to be expensive.*
Keywords Rotational moulding · Oven residence time · LLDPE · Machine
learning · Training data · Polynomial regression model
1 Introduction
1.1 Rotational Moulding
Rotational moulding (also known as rotational casting or roto moulding) is a polymer
processing technique mainly used to manufacture hollow parts, with simple and
complex structures. It is relatively a stress-free process compared with the other
A. Kumar (B) · PL. Ramkumar · A. Shukla · N. Gupta
Department of Mechanical and Aero-Space Engineering, IITRAM, Ahmedabad, India
e-mail: akshay.kumar.17m@iitram.ac.in
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
A. K. Parwani et al. (eds.), Recent Advances in Mechanical Infrastructure,
Lecture Notes in Intelligent Transportation and Infrastructure,
https://doi.org/10.1007/978-981-33-4176-0_1
3
in Rotational Moulding of LLDPE Using
Machine Learning Model for the Given
Oven Residence Time
Akshay Kumar, PL. Ramkumar, Aman Shukla, and Nikita Gupta
Abstract Enhancing the mechanical property of rotationally mouldable product,
while sustaining the mouldability, becomes a strenuous task. Examining these properties on practically built product has always been laborious and economically challenging. Machine learning can consequently provide an ample assistance for such
exposition. Thus, a new approach has been developed based on machine learning in
the present study in order to predict mechanical properties of roto moulded product
on the basis of oven residence time using linear low density polyethylene (LLDPE).
The two machine learning models utilized for the investigation include linear regression model and polynomial regression model. Variation of mechanical properties of
linear low density polyethylene (LLDPE) roto moulded product with oven residence
time based on the existing statistics has been used as training data for these models.
Preliminary results show that polynomial regression model has given more precise
data than linear regression model. From economic prospective, machine learning
methods were able to achieve acceptable quality results, which are beneficial, since
obtaining the similar amount of data by developing a product practically can prove
to be expensive.*
Keywords Rotational moulding · Oven residence time · LLDPE · Machine
learning · Training data · Polynomial regression model
1 Introduction
1.1 Rotational Moulding
Rotational moulding (also known as rotational casting or roto moulding) is a polymer
processing technique mainly used to manufacture hollow parts, with simple and
complex structures. It is relatively a stress-free process compared with the other
A. Kumar (B) · PL. Ramkumar · A. Shukla · N. Gupta
Department of Mechanical and Aero-Space Engineering, IITRAM, Ahmedabad, India
e-mail: akshay.kumar.17m@iitram.ac.in
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
A. K. Parwani et al. (eds.), Recent Advances in Mechanical Infrastructure,
Lecture Notes in Intelligent Transportation and Infrastructure,
https://doi.org/10.1007/978-981-33-4176-0_1
3