Reducing warpage in injection moulding processes using Taguchi method approach : ANOVA
Date
2012
Authors
Amer, Y.
Moayyedian, M.
Hajiabolhasani, Z.
Moayyedian, L.
Editors
Chen, B.
Khan, M.
Tan, K.
Khan, M.
Tan, K.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings of the IASTED International conference on engineering and applied science (EAS 2012), 2012 / Chen, B., Khan, M., Tan, K. (ed./s), pp.227-233
Statement of Responsibility
Conference Name
EAS 2012 - The IASTED International conference on engineering and applied science (27 Dec 2012 - 29 Dec 2012 : Colombo, Sri Lanka)
Abstract
Mould making is one important industry involved in our life, since moulded products contribute to more than %70 of components in products. However, there exist common defects in any moulding process particularly in plastic injection moulding such as: warpage, shrinkage, sink marks, and weld lines.
Also, appropriate parameters settings were determined using Taguchi's experimental design method. Based on data measurement sensitivity, through trial and error method the optimum alpha rate; 0.8 was chosen. Furthermore, p rate for each parameter (A, S, and C) and their combinations (AB, AC, and Be) were identified. As a result A, B, AB, and AC were significant factors, while C and AC were insignificant. The results obtained from ANOV A approach analysis with respect to those derived from MINIT AB illustrate, in former, A and B are the most significant factors which may cause warpage in injection moulding process. Moreover, ANOV A approach in comparison with other approaches like SIN ratio is much more accurate, resulting from the alpha rate, and also the interaction of factors is possible to achieve the higher and the better outcomes.
In this paper, two distinctive approaches of Taguchi method, to reduce the warpage defect of thin plate Acrylonitrile Butadiene Styrene (ABS), will be implemented in two levels; namely, orthogonal arrays of Taguchi and the Analysis of Variance (ANOVA). In order to improve this process, injection pressure (A), packing time (B) and cooling time(C) were identified as the most effective factors.