R. Pitchumani

A fractal geometry model for evaluating permeabilities of porous preforms used in liquid composite molding

Abstract Permeation of a net-shaped porous preform by a catalyzed thermosetting resin is an important step in the fabrication of fiber-reinforced composite materials using liquid molding techniques. Determination of the preform permeabilities is critical for an accurate analysis and design of these processes. The complex labyrinth of the preform pore structures, however, presents a major challenge to a quantitative description of the microstructures, and consequently, the evaluation of their permeabilities. Toward addressing this problem, a fundamental description of the disordered preform pore structures using fractal techniques is presented. A fractal permeation model is developed which relates the preform permeabilities to the actual microstructures in terms of two fractal dimensions—one relating the size of the capillary flow pathways to their population, and the other describing the tortuosity of the capillary pathways. The analytical model predictions are validated by experimentally-determined permeabilities for a wide range of preform and process parameters. The model development, experimental studies and the model validation are presented and discussed. The methodology presented may be extended to analyzing transport through porous media arising in other application areas as well.

Analysis of transport phenomena governing interfacial bonding and void dynamics during thermoplastic tow-placement

The thermoplastic automated tow-placement process offers the potential for cost-effective fabrication of composite parts via consolidation in situ, thus avoiding the costly autoclave consolidation. The degree of interfacial bonding between tow layers and the void content in the composite laminate directly affect the mechanical properties and performance of the products. Theoretical models for the physical phenomena governing interfacial bonding and void dynamics (growth and consolidation) during the process are presented, which constitute the core of a numerical process simulator. Simulation-based parametric studies are reported for the case of an AS-4/PEEK composite to illustrate the effects of several process conditions and placement head configurations on the resulting degree of bonding and final void content. The analysis provides valuable insight towards optimal process and placement head design.

Correlation of Thermal Conductivities of Unidirectional Fibrous Composites Using Local Fractal Techniques

The arrangement of fibers strongly influences heat conduction in a composite. Traditional approaches using unit cells to describe the fiber arrangements work well in the case of ordered arrays, but are not useful in the context of disordered arrays, which have been analyzed in the literature by statistical means. This work presents a unified treatment using the tool of local fractal dimensions (although, strictly speaking, a composite cross section may not be an exact fractal) to reduce the geometric complexity of the relative fiber arrangement in the composite. The local fractal dimensions of a fibrous composite cross section are the fractal dimensions that it exhibits over a certain small range of length scales. A generalized unit cell is constructed based on the fiber volume fraction and local fractal dimensions along directions parallel and transverse to the heat flow direction. The thermal model resulting from a simplified analysis of this unit cell is shown to be very effective in predicting the conductivities of composites with both ordered as well as disordered arrangement of fibers. For the case of square packing arrays, the theoretical result of the present analysis is identical to that of Springer and Tsai.

Closed-loop flow control in resin transfer molding using real-time numerical process simulations

Real-time feedback control of resin flow through a fibrous preform during the mold-filling step of resin transfer molding offers an effective means of eliminating fill-related defects in the composite products. Process simulation models have played an indirect role in the control, either through off-line determination of the control parameters to be used during the process, or in the training of artificial neural networks that act as proxy simulators online with the process. This paper explores, for the first time, the use of on-the-fly finite-difference-based numerical solution of the partial differential equations governing the mold filling process, in a closed-loop flow control. The numerical simulations are used to forecast system response in real-time, in order to determine the best combination of flow rates at the injection ports so as to steer the flow through a target schedule during the process. The performance of the controller implemented on a lab-scale resin transfer molding process is demonstrated on several preform configurations and desired fill patterns. The study paves the way for better utilization of the advances in process modeling and computational infrastructure toward an effective bridging of the science and practice of composites processing.

Intelligent model-based control of preform permeation in liquid composite molding processes, with online optimization

Manufacturing of quality products via liquid molding processes such as Resin Transfer Molding (RTM), calls for a precise control of resin progression through fibrous preforms during mold fill. Lack of an effective process control leads to formation of dry spots and voids that are detrimental to product quality. This study presents the use of physics-based process simulations in real-time, towards a generalized process control. The implementation of process simulations for on-line model-predictive control requires that the simulation time scales be less than the time scales of the process. An artificial neural network trained using data from numerical process models is used to provide rapid, real-time process simulations for the model-based control. A simulated annealing algorithm, working interactively with the neural network process model, is used to derive optimal control decisions rapidly and on-the-fly. The controller performance is systematically demonstrated for several processing scenarios.

Design and Optimization of a Thermoplastic Tow-Placement Process with In-Situ Consolidation

Thermoplastic tow-placement with in-situ consolidation offers the potential for rapid fabrication of composite parts for a variety of applications. Physical models and model-based and experimental analysis of the process phenomena have been previously reported by the authors and other investigators in the literature. This paper presents a methodology for practical design and optimization using the available theoretical process models. Processing windows are developed based on considerations of material degradation through weight loss, final void content, and dimensional change of the tows, all of which determine laminate quality and thus, part performance. Optimum line speed and heat input variations with composite thickness are identified based on parametric studies to maximize interfacial bond strength and minimize fabrication time, subject to constraints on the above-mentioned quality-related parameters. The processing windows and optimum profiles are presented in terms of the controllable process variables, which allows for a ready implementation of the results in practice.

Stochastic modeling of nonisothermal flow during resin transfer molding

Resin Transfer Molding (RTM) offers the potential to manufacture reinforced thermosetting composites of complex geometries cost-effectively, and at high throughputs. Strong uncertainties inherent in the process, however, stymie robust production of quality composites via this route. Although a number of numerical models have been developed over the years to describe the process, a thorough and systematic analysis of the parameter uncertainties has been the subject of little attention, and forms the focus of this study. This paper presents a stochastic model to investigate the effects of process and material parameter uncertainties on the nonisothermal filling process during resin transfer molding. The analysis is performed in terms of four dimensionless parameters that concisely represent the process physics, and provide for a generalized applicability of the study over a wide range of processing configurations. Parametric studies are conducted to identify optimum values of the dimensionless quantities that minimize the fill time, while simultaneously minimizing the output parameter variabilities. The results of the study provide valuable insight towards robust manufacture of composite materials.

Enhancement of flow in VARTM using localized induction heating

A critical step in the vacuum assisted resin transfer molding (VARTM) process is the permeation of a porous preform by the reactive resin. A real time flow control is often imperative to achieve complete preform saturation and void-free fill. Local permeability variations in the preform, however, challenge the flow control endeavor. Control strategies that use manipulation of the inlet port parameters are shown in the literature to be limited in the controllability of flow in regions of localized preform variability, particularly at locations away from the controlled injection ports. This paper explores an innovative scheme of using induction heating as a method of locally reducing the resin viscosity to counteract the effects of such localized low permeability regions within the preform. Toward this end, the paper presents a process model for nonisothermal flow during the VARTM process, in the presence of induction heating. The process model, validated with experiments, is used to conduct process simulations to investigate the effects of processing parameters such as induction heating location, induction heating power level, and vacuum level on three heterogeneous preform geometries with varying permeability ratios between the low permeability and high permeability regions. Results of these studies are presented in the form of processing windows and processing maps, which show that induction heating is capable of reducing void and dry spot formation during the VARTM process.

Application of genetic algorithms to optimal tailoring of composite materials

Abstract Composite materials tailoring refers to the concurrent manipulation of the materials composition and internal architecture to achieve the desired properties. Since the wide variety of material combinations, reinforcement geometries and architectures to choose from poses a bewildering task of selection, a systematic approach to optimal materials tailoring is of much practical importance in the engineering design of composites, and is addressed in this article. The study presents an optimal tailoring framework based on the combinatorial optimization technique of genetic algorithms in conjunction with a property model base consisting of analytical micro-structure-property relationships. Optimum designs are reported for case studies involving property requirements that are typical of selected practical applications. The present approach is systematically compared with the alternative approach based on the simulated annealing technique reported previously by the authors. The comparative studies are used to draw general inferences concerning the relative merits of both these approaches in the context of optimal composite materials tailoring.

An Analysis of Mechanisms Governing Fusion Bonding of Thermoplastic Composites

A number of mechanisms have been proposed in the literature as contributors to the strength development at the polymer-polymer interface during fusion bonding of thermoplastic composites. Of these, healing and intimate contact emerge as fundamental mechanisms governing bonding. Intimate contact refers to the development of the amount of surface area that is physically contacted at the interface at any time, and healing describes the migration of polymer chains across the interface in intimate contact. This work provides a new theoretical development of a coupled bonding model that accounts for variability in initiation time for healing due to growth in the area in intimate contact. The generalized coupled bonding model is valid for any set of processing conditions and reduces to the proper controlling mechanism as dictated by the process. Analysis revealed a key dimensionless group, Q, that captures the coupled nature of the mechanisms governing fusion bonding. By evaluating Q, which is a function of material and process parameters, one can determine the relative contributions of each mechanism. Experimental validation of the coupled model using two different processes, tow placement and resistance welding, is also presented. An evaluation of Q for the tow-placement process indicates that both mechanisms are controlling. For this case, the coupled model demonstrates better strength predictions than the conventional healing model alone. In contrast, the resistance welding process is shown to be intimate-contact controlled, in which case the coupled model reduces to a more simplified model. The ability to rigorously determine the controlling mechanisms is of critical importance to accurately model the strength development during fusion bonding processes.