TY - GEN
T1 - Mathematical modeling of plastic injection molding parameters to study the product quality through mechanical property evaluation
AU - Venkatason, Kausalyah
AU - Sivaguru, Shasthri
AU - Paizar, Abdul Rauf
AU - Roseley, Nik Roselina Nik
N1 - Publisher Copyright:
© 2023 Author(s).
PY - 2023/6/22
Y1 - 2023/6/22
N2 - Injection moulding is an important polymer processing operation in the plastic industry. Several factors contribute to the quality of the plastics products' mechanical properties: the plastic injection moulding process parameter, materials of the specimen, mould, and part design. The research purpose of this paper is to identify the plastic injection moulding parameters that will influence the effect of quality for the polypropylene (PP) material in terms of the mechanical properties of the dog bone-shaped specimen. The design of experiment (DoE) sampling proposed for this research is the central composite design (CCD). The application of polynomial response surface methodology (RSM) will generate the mathematical model in terms of a quadratic polynomial equation and through this, the interaction between parameters and output response can be studied. The input parameters that will be focused on are the percentage of pigmentation, melt temperature, injection pressure, and cooling time while the output response studied are tensile strength and hardness. The analysis of variances (ANOVA) data obtained for the tensile test shows that the full model gives a low prediction error of RMSE value 0.071 and fitness value R2 is 85.3% compared to the reduced model where the RMSE is 0.084 and R2 is 69.6%. Meanwhile, for the hardness test, the ANOVA data obtained for the full and reduced model gives an RMSE of 0.12 and the fitness value R2 for the full model is 56.7% and 39.1 % for the reduced model. In a conclusion, the percentage of pigmentation (x1) is the most significant and influencing factor that affects the mechanical properties of the specimen for both tensile strength and hardness.
AB - Injection moulding is an important polymer processing operation in the plastic industry. Several factors contribute to the quality of the plastics products' mechanical properties: the plastic injection moulding process parameter, materials of the specimen, mould, and part design. The research purpose of this paper is to identify the plastic injection moulding parameters that will influence the effect of quality for the polypropylene (PP) material in terms of the mechanical properties of the dog bone-shaped specimen. The design of experiment (DoE) sampling proposed for this research is the central composite design (CCD). The application of polynomial response surface methodology (RSM) will generate the mathematical model in terms of a quadratic polynomial equation and through this, the interaction between parameters and output response can be studied. The input parameters that will be focused on are the percentage of pigmentation, melt temperature, injection pressure, and cooling time while the output response studied are tensile strength and hardness. The analysis of variances (ANOVA) data obtained for the tensile test shows that the full model gives a low prediction error of RMSE value 0.071 and fitness value R2 is 85.3% compared to the reduced model where the RMSE is 0.084 and R2 is 69.6%. Meanwhile, for the hardness test, the ANOVA data obtained for the full and reduced model gives an RMSE of 0.12 and the fitness value R2 for the full model is 56.7% and 39.1 % for the reduced model. In a conclusion, the percentage of pigmentation (x1) is the most significant and influencing factor that affects the mechanical properties of the specimen for both tensile strength and hardness.
KW - central composite design
KW - mechanical properties
KW - plastic injection moulding parameter
KW - polypropylene
KW - response surface methodology
UR - http://www.scopus.com/inward/record.url?scp=85166750814&partnerID=8YFLogxK
U2 - 10.1063/5.0116022
DO - 10.1063/5.0116022
M3 - Conference contribution
AN - SCOPUS:85166750814
T3 - AIP Conference Proceedings
BT - World Congress on Science, Engineering and Technology (WCOSET) 2021
A2 - Manurung, Yupiter Harangan Prasada
A2 - Mahmud, Jamaluddin
A2 - Abdullah, Sukarnur Che
A2 - Singh, Baljit Singh Bhathal
A2 - Venkatason, Kausalyah
A2 - Roseley, Nik Roselina Nik
PB - AIP Publishing
T2 - 2021 World Congress on Science, Engineering and Technology
Y2 - 8 March 2021 through 12 March 2021
ER -