A review on numerical optimization in liquid composite moulding processes

Zade, Anita; Kuppusamy, Raghu Raja Pandiyan

doi:10.1016/j.matpr.2019.07.605

Cited by 7 publications

(4 citation statements)

References 31 publications

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“…Attributed to their exceptional specific strength, composite materials have seen widespread adoption across various applications and industries. In particular, the aerospace and automotive industries are eager to incorporate composite materials into their productions in an effort to make their products lightweight, while maintaining a superior mechanical performance [1][2][3][4]. One of the composite manufacturing processes frequently adopted by the industry is the Resin Transfer Moulding (RTM) process, due to its significant potential for high-quality, high-throughput composite production [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Composite Mould Filling Pattern Dataset for Process Modelling and Prediction

Chai,

Wang,

Dang

et al. 2024

J. Compos. Sci.

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The Resin Transfer Moulding process receives great attention from both academia and industry, owing to its superior manufacturing rate and product quality. Particularly, the progression of its mould filling stage is crucial to ensure a complete reinforcement saturation. Contemporary process simulation methods focus primarily on physics-based approaches to model the complex resin permeation phenomenon, which are computationally expensive to solve. Thus, the application of machine learning and data-driven modelling approaches is of great interest to minimise the cost of process simulation. In this study, a comprehensive dataset consisting of mould filling patterns of the Resin Transfer Moulding process at different injection locations for a composite dashboard panel case study is presented. The problem description and significance of the dataset are outlined. The distribution of this comprehensive dataset aims to lower the barriers to entry for researching machine learning approaches in composite moulding applications, while concurrently providing a standardised baseline for evaluating newly developed algorithms and models in future research works.

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Section: Introductionmentioning

confidence: 99%

Comprehensive Composite Mould Filling Pattern Dataset for Process Modelling and Prediction

Chai,

Wang,

Dang

et al. 2024

J. Compos. Sci.

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“…1 Thorough examinations of the mold-filling stage are imperative for refining the manufacturing process of industrial RTM composite parts. 2,3 The mold-filling phase involves the injection of pressurized resin through the appropriate injection ports until the fiber preform gets saturated. 4,5 Multiple injection ports and vents are inevitable for the complex and large composite parts to saturate the whole mold without dry spot formation before the resin gelation.…”

Section: Introductionmentioning

confidence: 99%

“…The resin transfer molding (RTM) method is a commonly employed process within the realm of liquid composite molding (LCM) for producing intricate and sizable composite components 1 . Thorough examinations of the mold‐filling stage are imperative for refining the manufacturing process of industrial RTM composite parts 2,3 . The mold‐filling phase involves the injection of pressurized resin through the appropriate injection ports until the fiber preform gets saturated 4,5 .…”

Section: Introductionmentioning

confidence: 99%

Simultaneous port number and location optimization of mold gates‐vents using in‐house coded stochastic multi‐objective optimization algorithm

Zade,

Kumar,

Kuppusamy

2024

Polymer Composites

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This research introduces multi‐objective stochastic optimization (MOSO) and non‐dominated sorting differential evolution (NSDE) in‐house coded algorithms for enhancing the optimization of the resin transfer molding (RTM) process during the mold‐fill phase for composite parts. Glass fiber‐vinyl ester‐based automotive bonnet and carbon fiber‐RTM6‐based aircraft wing flap parts were used as the case studies. Initially, the NSDE algorithm was developed for the simultaneous optimization of dry‐spot content and mold‐fill time by changing the locations of gates and vents with pre‐fixed port numbers. Then, the MOSO algorithm was designed for the concurrent optimization of the dry‐spot content, mold‐fill time and total number of ports by changing both the numbers and locations of gates and vents. The effect of race‐tracking was also investigated using higher permeability values at the composite part‐cut edges. When compared, the MOSO algorithm predicted less dry‐spot content, number of ports, mold‐fill time and uniform resin flow‐front progressions with lesser functional evaluations and computational time than the NSDE algorithm.Highlights The non‐dominated sorting differential evolution (NSDE) and multi‐objective stochastic optimization (MOSO) algorithms were developed for the optimization of the mold fill phase of resin transfer molded composite parts. The development of the NSDE algorithm aimed to concurrently optimize the dry‐spot content and mold‐fill time by changing the locations of gates and vents with pre‐fixed port numbers. The MOSO algorithm was designed to concurrently optimize dry‐spot content, mold‐fill time, and the total number of ports by varying both the numbers and locations of gates and vents. The MOSO algorithm predicted better‐automated mold‐fill phase optimalities for the resin transfer molded composite parts compared to the NSDE algorithm.

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“…Awareness of environmental and efficient issues in the last decades contributes to the important factors that encourage researchers to explore thermoplastic biocomposites [ 1 , 2 , 3 ]. In hybrid yarn-based preform, the thermoplastic resin is already hybrid with the reinforcing fiber into the continuous textile preform to form intermedia materials for a fast-manufacturing process.…”

Section: Introductionmentioning

confidence: 99%

Influence of the Unit Cell Parameters on the Thermomechanical Non-Symmetric In-Plane Shear Behavior of 2D Biaxial Braided Preform for Thermoplastic Biocomposites

et al. 2022

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The identification of thermomechanical in-plane shear behavior of preform is one of the most important factors to ensure the quality of the thermoplastic composites during the thermoforming process. In this present work, the non-symmetric in-plane shear behavior of flax/polypropylene 2D biaxial braided preform for thermoplastic biocomposites was characterized at elevated temperature chamber by using bias-extension test. Analytical models of a bias-extension test based on non-symmetric unit cell geometry for 2D biaxial braids were defined and applied; the thermo-condition-dependent experiments were conducted to study the temperature and displacement rate dependences. The influence of unit cell geometry parameters including braiding angle, tow waviness, and cover factor on the thermal in-plane shear behavior was deeply invested, experiments in both axial and transversal directions were performed for a complete study, and asymmetric scissor mechanisms for in-plane shear behavior were introduced and studied. Finally, a simulation of thermal impregnation distribution based on unit cell geometry was made to clarify the importance of the overall fiber volume fraction.

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A review on numerical optimization in liquid composite moulding processes

Cited by 7 publications

References 31 publications

Comprehensive Composite Mould Filling Pattern Dataset for Process Modelling and Prediction

Comprehensive Composite Mould Filling Pattern Dataset for Process Modelling and Prediction

Simultaneous port number and location optimization of mold gates‐vents using in‐house coded stochastic multi‐objective optimization algorithm

Influence of the Unit Cell Parameters on the Thermomechanical Non-Symmetric In-Plane Shear Behavior of 2D Biaxial Braided Preform for Thermoplastic Biocomposites

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