Improving Composite Materials Manufacturing through Big Data Analysis Techniques. Youssef K. HAMIDI. Ecole Nationale Superieure des Mines de Rabat.
Improving Composite Materials Manufacturing through Big Data Analysis Techniques Youssef K. HAMIDI
Ecole Nationale Superieure des Mines de Rabat Agdal, Rabat, Morocco
Abstract In recent years, fiber-reinforced composites integrated key technological areas such as aerospace, defense and automotive industries [1-5]. The attractiveness of these man-made materials is mainly due to their lower costs and lower weight to strength ratios when compared to conventional materials. Another attractive feature that certain manufacturing techniques can generate near net shape, complex geometry composites [1-3]. However, composite materials suffer from a number of drawbacks that limit their usage in load-bearing components. Some of these drawbacks are manufacturing-related such as voids and dry areas as well as poor fiber-matrix adhesion that severely affect thermomechanical properties of composite parts [5-9]. Other drawbacks are inherent to composites such as moisture absorption or even nanofillers dispersion [10-12]. As the demand for fuel efficiency becomes a must in today’s reality, composite materials are more than ever coveted to play a greater part in the transportation field. Consequently, there is a pronounced need for developing enhanced composite materials with better mechanical properties, lower costs and longer life-span. Since mechanical properties and life-span of composite parts have been shown to depend heavily on processing parameters [1-14], optimizing the latter can play a key role in this endeavor. During the last five decades, a myriad of research articles have described composites mechanical properties and life-spans dependence on processing parameters. Moreover, aerospace and automotive companies have carried out countless experiments to optimize their manufacturing costs. These data can be congregated and used to better understand their effect on the manufactured part final properties as well as these properties variation during its life-span. Using big data analysis techniques to analyze this massive amounts of data and extract the best processing parameters for fabricating composite parts for specific applications has the potential of revolutionizing composite materials manufacturing. The final drive being a wider usage of such materials in load bearing component for aerospace and automotive industries, and thus higher overall fuel efficiency for all transportation modes.
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