DEVELOPMENT OF AN EFFECTIVE MULTI-OBJECTIVE OPTIMIZATION STRATEGY FOR THE MANUFACTURING OF RESIN TRANSFER MOULDED COMPOSITE PARTS

Abstract

The resin transfer moulding (RTM) technique is a widely used liquid composite moulding (LCM) process due to its advantages of uniform thickness and good surface finish for manufacturing complex composite parts. However, the RTM process is not practised widely due to the cost involved in developing mould design and process parameters. A proper mould design requires designing an effective injection strategy which contains the least number and appropriate positions of injection ports and vents that result in minimum mould filling time without dry spot content. As well, the judicious choice of mould heating time-temperature cycle is required for the effective curing process. In addition, the cognition on resin gelation-cure kinetics-rheokinetics and reinforcement mat permeabilities are the essentially required material parameters for the successful development of the RTM process. On the contrary, RTM being the closed moulding process, it is difficult to visualize resin flow and sense resin curing. Therefore, it becomes a hard task to analyse influential mould fill and cure process parameters through experimental trials and thus, the development of composite parts via the RTM process is confined. To address these challenges, this research proposes a simulation-based optimization framework utilizing supervised learning algorithms to automate and optimize the RTM process. In this framework, simulation packages are coupled with optimization algorithms to autonomously determine optimal design and process parameters. The study introduces a robust and cost-effective methodology to simulate and optimize RTM mould-filling and curing processes using an in-house coded multi-objective optimization (MOO) algorithm integrated with process simulation via multi-phase porous flow, transient heat transfer and resin cure kinetics models. This framework was implemented using COMSOL Livelink for MATLAB focusing on manufacturing a vinyl ester-glass fiber-reinforced automotive bonnet and an RTM6-carbon fiber-reinforced aircraft wing flap. Initially, vinyl ester and RTM6 resins were thermally characterized to develop the cure process windows through which the appropriate time-temperature cure cycles were identified for the curing of composite parts. From the thermal characterization of neat resins, the modified Kamal and Sourour model was effectively fitted to the experimental data of the degree of cure versus the rate of cure for both vinyl ester and RTM6 resins. Subsequently, the permeabilities of reinforcement fibre mats were measured using mould-filling experiments for their applicability in the mould-filling simulations. The effective permeability of 2.0×10-9 m2 and v 1.0×10-9 m2 were obtained using the mould-filling experiments for woven roving glass and carbon fibre mats, respectively. In the mould-filling phase, novel in-house coded Multi-Objective Stochastic-Optimization (MOSO) and Non-dominated Sorting Differential Evolution (NSDE) algorithms were developed and implemented to optimize the mould-fill phase. The NSDE algorithm was implemented for simultaneous optimization of two objectives namely, dry spot content and mould-fill time by changing the locations of gates and vents at the fixed input numbers of gates and vents. Consecutively, the MOSO algorithm was implemented for simultaneous optimization of three objectives namely, dry spot content, mould-fill time and total number of ports by simultaneously changing both the numbers as well as locations of gates and vents. The effect of race-tracking was also investigated using higher permeability values at the composite part-cut edges. Then, the efficacy of the proposed algorithms was examined with the trial and error process model simulations. From the comparative assessment, the trial and error process required more iterations with trials in numbering and positioning ports and manual efforts for obtaining a single optimal solution. Conversely, the MOO algorithms were automated and needed less manual effort and problem-specific experience to obtain the number of Pareto optimal solutions. In comparison to the NSDE algorithm, the MOSO algorithm predicted less dry-spot content, number of ports, mould-fill time and uniform resin flow-front progressions with lesser functional evaluations and computational time. In the curing phase, a novel in-house coded NSDE algorithm was developed and implemented for the simultaneous minimization of composite part thermal gradients and cure process time for both the studied composite parts. The efficacy of the proposed algorithm was examined with the in-house coded non-dominated sorting genetic algorithm (NSGA-II) and trial-error process simulations in terms of a thermal gradient, cure-time, and cure progression at the applied temperature cycles. From the results, the NSDE algorithm was found to be effective in achieving faster convergence with less cure process and computational time when compared to the NSGA-II algorithm. The NSDE algorithm performed effectively in terms of thermal gradient and cure-time with the automated predictions of the mould heating parameters when compared with the trial-error process for both the composite parts. This research significantly contributes to the field by introducing efficient and automated optimization algorithms for RTM composite parts by enhancing both manufacturing precision and time efficiency.