Methodology of designing Statistical Design of Experiment (SDE) to study wrinkles and delamination on composite panels

• Main Authors: Muhammad Iqbal, Muhammad Hussain, Dr., Zuraidah, Mohd Zain, Prof. Dr., Mohd Shuid, Salleh, Chang, Lawrence
• Other Authors: [e-mail]
• Format: Article
• Language: English
• Published: 2012
• Subjects: Composite manufacturing process; Wrinkles; Delamination; Statistical design of experiments; Process optimisation
• Online Access: http://dspace.unimap.edu.my/xmlui/handle/123456789/17689

Composites are two or more dissimilar materials, such as fibre and resin, working together to create a product with exceptional properties not present in the original materials. There are various technologies used in the process of molding this material such as resin transfer molding (RTM), liquid infusion molding (LIM) and autoclave molding (ACM). Many composite manufacturing systems are manual, and hence, due to difficulty in controlling manual processes, quality of the product is compromised. At a manufacturing plant in Kedah, there is a hand-lay-up manual system in the manufacture of a certain high technology composite product line. It has been observed that process control is rather difficult due to a high number of process parameters involved. Thus, a study using ‘statistical design of experiment’ (SDE) will be performed to aid process optimisation, which should result in getting minimum number of defects on the final product. In this paper, an overview of the process will be given, and the experimental strategies will be discussed in detail. The aim of the study is to reduce two dominant mode of failure on the product, namely delamination and wrinkling. This is done by optimising several controllable and uncontrollable variables such as temperature, pressure, soaking time, heat up rate, cool down rate, and the geometrical dimensions of the part. It is proposed that SDE is a useful technique for the investigation of shop floor problems and the setting up of the process parameters that ultimately lead to reduced variability in the final product. Instead of running many combinations of parameters in real life, SDE enables only a few combinations to be run before optimum process set-up can be determined. As a result, the time taken to determine optimum process set-up is greatly reduced. This also allows experimenters to get much more and much better data per experimental run. This in turn results in tremendous cost savings.