Posts shared by Basant Pandole
https://www.linkedin.com/posts/basant-pandole-29b60117b_injectionmolding-plasticmanufacturing-manufacturingengineering-activity-7439131283521200129-oV4k?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAFdVngBlMiZ925dL49SAnzlYQ-9zYj5bhk
https://www.linkedin.com/posts/basant-pandole-29b60117b_injectionmoulding-plasticprocessing-manufacturingengineering-activity-7438788895900725248-gCok?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAFdVngBlMiZ925dL49SAnzlYQ-9zYj5bhk
https://www.linkedin.com/posts/basant-pandole-29b60117b_injectionmolding-plasticmanufacturing-manufacturingengineering-activity-7437950274578595840-r4Tv?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAFdVngBlMiZ925dL49SAnzlYQ-9zYj5bhk
https://www.linkedin.com/posts/basant-pandole-29b60117b_injectionmoulding-plasticmanufacturing-manufacturingengineering-activity-7436976659607642112-UYgi?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAFdVngBlMiZ925dL49SAnzlYQ-9zYj5bhk
https://www.linkedin.com/posts/basant-pandole-29b60117b_injectionmolding-plasticmanufacturing-polymerengineering-activity-7436603600207872001-9Lzr?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAFdVngBlMiZ925dL49SAnzlYQ-9zYj5bhk
https://www.linkedin.com/posts/basant-pandole-29b60117b_injectionmoulding-plasticmanufacturing-manufacturingengineering-activity-7435879012029276160-Hqa1?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAFdVngBlMiZ925dL49SAnzlYQ-9zYj5bhk
https://www.linkedin.com/posts/basant-pandole-29b60117b_polymers-polymerscience-plasticsengineering-activity-7435522908057198592-DXvo?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAFdVngBlMiZ925dL49SAnzlYQ-9zYj5bhk
https://www.linkedin.com/posts/basant-pandole-29b60117b_injectionmoulding-plasticmanufacturing-manufacturingengineering-activity-7433697720260771841-v2ZT?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAFdVngBlMiZ925dL49SAnzlYQ-9zYj5bhk
Research article
Optimizing injection molding parameters to reduce weight and warpage in PET preforms using Taguchi method and Analysis of Variance (ANOVA)
Rozeena Aslam, Awais Ahmed Khan, Hamza Akhtar, Sadia Saleem, Muhammad Sarfraz Ali
Next Materials
Volume 8, July 2025, 100623
Open Access
https://www.sciencedirect.com/science/article/pii/S2949822825001418
RECENT METHODS FOR OPTIMIZATION OF PLASTIC INJECTION MOLDING PROCESS –A RETROSPECTIVE AND LITERATURE REVIEW
International Journal of Engineering Science and Technology
Vol. 2(9), 2010, 4540-4554
P.K. Bharti
Assistant professor, Mechanical Engineering Department, Integral
University, Lucknow, 226023, India
M. I. Khan
Prof. and Head, Mechanical Engineering Department, Integral University, Lucknow, 226023, India
Harbinder Singh
Professor and Director, Bundel khand Institute of Engineering and Technology, Jhansi, India
Abstract:
Injection molding has been a challenging process for many manufacturers and researchers to produce products meeting requirements at the lowest cost. Faced with global competition in injection molding industry, using the trialand-error approach to determine the process parameters for injection molding is no longer good enough. Factors that affect the quality of a molded part can be classified into four categories: part design, mold design, machine performance and processing conditions. The part and mold design are assumed as established and fixed. During production, quality characteristics may deviate due to drifting or shifting of processing conditions caused by machine wear, environmental change or operator fatigue.
Determining optimal process parameter settings critically influences productivity, quality, and cost of production in the plastic injection molding (PIM) industry. Previously, production engineers used either trial-and-error method or Taguchi’s parameter design method to determine optimal process parameter settings for PIM. However, these methods are unsuitable in present PIM because of the increasing complexity of product design and the requirement of multi-response quality characteristics.
This article aims to review the recent research in designing and determining process parameters of injection molding. A number of research works based on various approaches have been performed in the domain of the parameter setting for injection molding. These approaches, including mathematical models, Taguchi method, Artificial Neural Networks (ANN),Fuzzy logic, Case Based Reasoning (CBR), Genetic Algorithms (GA), Finite Element Method(FEM),Non Linear Modeling, Response Surface Methodology, Linear Regression Analysis ,Grey Rational Analysis and Principle Component Analysis (PCA) are described in this article. The strength and the weakness of individual approaches are discussed. It is then followed by conclusions and discussions of the potential research in determining process parameters for injection molding
Ud. 16.3.2025
Pub. 9.7.2025
