Simultaneous gate and vent location optimization in liquid composite molding processes

A two-stage optimization methodology for gate and vent locations and distribution media layout for liquid composite molding process

Improvements in part quality and cost reduction are the primary objectives of CAE use in the injection molding industry. Engineers use advanced injection molding simulation software to analyze and verify their part designs. Traditionally, engineers have had to rerun simulations to verify the effects of changes in gate locations. For complex models, simulations are very time consuming. To reduce the design cycle time, a Design Optimization Module is developed by C-MOLD. One of the functions of this new software module is to automatically select optimal gate locations. This innovative technology is the result of close R&D collaboration between C-MOLD and LG-PRC in Korea. An overview of gate location optimization technology is presented in this paper, and several examples are also presented as illustration.

In liquid composite molding (LCM) processes, the resin is injected into the mold cavity, which contains preplaced reinforcement fabrics, through openings known as gates, while the displaced air leaves the mold through openings called vents. Under nominal conditions, the last points to fill are chosen as vent locations. However, due to imperfect preform cutting and placement, gaps and channels may form along the edges and curvatures in a mold, offering a path with less resistance for resin flow. The faster advance of resin through these gaps and channels, a common disturbance known as racetracking, will cause the last filled regions to vary, which complicates the vent selection process. In this study, probabilistic racetracking modeling is used to capture last-filled region distribution over the mold geometry. Success criteria for mold filling are defined in terms of dry spot tolerances, and vent fitness maps, which display potential vent locations, are created. Next, exhaustive search algorithm is coupled with vent fitness maps to determine optimal vent configurations. The map-based exhaustive search is demonstrated on three geometries and results are compared with existing combinatorial search results. The performance of the optimal vent configurations is evaluated in a virtual manufacturing environment. Sensitivity analyses are conducted to determine the influence of optimization parameters on the results.

Taking the remote controller back shell for example, the flow process of the injection molding including filling, packing, holding and cooling was simulated by using the process technology of Moldflow software, the best gate location and the information of weld lines, trapped gas, fill time, pressure and temperature were analyzed. At the same time, the product defects during the injection was predicted, the cause of defects and influence factors was analyzed, the mould design parameters were adjusted, the design of the mould was optimized. It realized mass production of the product.

The growing demand from the automotive industry for lightweight, high-performance, and advanced manufacturing techniques for efficient and cost-effective production has accelerated the adoption of fiber-reinforced polymer composites. However, considering the manufacturing complexity of these materials, design remains challenging due to the intricate and interdependent relationships between the process conditions, the part geometry, and the resulting microstructure and quality. This research utilized the Autodesk Moldflow Insight software to design an injection molding process for the manufacturing of discontinuous glass fiber-reinforced polymer parts through computer modeling. A geometrically complex car fender was used as a case study. The effects of various process parameters, particularly gate locations, on the injection-molded parts’ properties (such as the fiber orientation, volumetric shrinkage, and shear rate) were investigated. Multiple injection molding process configurations were designed and simulated, including three, four, and five gates at varying locations. Based on the optimal performance (i.e., low shrinkage, a consistent fiber orientation, and a controllable shear rate), an optimal configuration with four gates at appropriate locations (corresponding to the second gate location set) was identified based on multicriteria decision-making analysis, i.e., volumetric shrinkage of 8.5−2.2+1.4%, a fiber orientation tensor of 0.927 ± 0.011, and a stable shear rate < 74,324 (1/s). A reduced strain closure model (modified Folgar–Tucker model) was used to predict the glass fiber orientation. A multicriteria decision-making technique, based on similarity ranking with an ideal solution, was employed to optimize the gate location. The simulation results clearly demonstrate that the gate placement is crucial for material behavior during molding and for reducing common defects. The simulation-based injection molding process design for the manufacturing of discontinuous fiber-reinforced polymer parts proposed in this paper can improve the production efficiency, reduce trial-and-error rates, and improve part quality.

Use of medial axis to find optimal channel designs to reduce mold filling time in resin transfer molding

Injection molding is one of the most popular manufacturing methods for the cost-effective mass production of the plastic parts. Filling analysis of the molten polymer provides useful information to investigate the process conditions to ensure successful replication. To validate appropriate gate location for a NEEDLE COVER, flow fronts and flow stresses for four different gating options and three different design options are analyzed and compared to the field results. Based on the results, the optimum gate location for the minimum flow stresses and uniform fill patterns appears to be at gate 3. Thus it also provides minimum possibility of part warpage throughout PORT HOUSING and NEEDLE HOUSING. The results of analysis on the increased wall thickness, lower melt temperature, and longer injection time indicated that higher melt temperatures were recommended to achieve successful molding. Injecting the polymer at a longer time (1.2 second) leads to a significant increase in flow stresses throughout the part and the increase of wall thickness achieve successful replication of the parts.

Various factors affect the quality of a plastic product during the injection molding process. In this study, the quality engineering planning method, Taguchi Method, and grey relational analysis were used in this study to determine these factors and the Moldflow Plastics Insight (MPI) was used to conduct the moldflow analysis. By varying different types of operating conditions, the results obtained from the experiments were analyzed so as to verify the influence of each factor on the quality of the final product. The optimal processing parameters which can reduce the mold tryout time and the analysis cost were then determined. The plastic impeller of a computer cooling fan is selected as the case study and the goal is to resolve the warping problems. The deviations in the shear stress distribution as obtained by varying the S/N ratio of variance factors during the experiments are in agreement with the results of analyzing the grey information relational degrees. The most influential factor is the mold temperature, followed sequentially by the fill time, fill pressure, and melt temperature.

Resin transfer molding is one of the most common fabrication methods for composite materials. It is attractive due to its high volume, high performance, and low cost manufacturing of polymer composites. In this process, a dry preform of reinforcing fibers is placed inside a closed mold; then the resin is injected into the mold cavity. The composite part can be removed from the mold after the resin cures. This paper surveys current issues in the resin transfer molding process and focuses on mold filling and simulation. The Control Volume Finite Element Method is applied to simulate the process. The 2-D and 3-D computational methods are presented. The simulation program was developed with C language by the authors. The 2-D and 3-D models were integrated in the program. In real applications, the 2-D elements and 3-D elements can be applied simultaneously according to the specific requirements. MSC/PATRAN 3 software (MacNeal-Schwendler Corporation) was used to generate the finite element mesh and display the results. Some case studies are conducted to demonstrate the application of the computer simulation to RTM process design.

Injection molding is one of the most important processes for the mass production of plastic parts. In recent years, many researchers have focused on predicting the occurrence and intensity of defects in injected molded parts, as well as the optimization of process parameters to avoid such defects. One of the most frequent defects of manufactured parts is blush, which usually occurs around the gate location. In this study, to identify the effective parameters on blush formation, eight design parameters with effect probability on the influence of this defect have been investigated. Using a combination of design of experiments (DOE), finite element analysis (FEA), and ANOVA, the most significant parameters have been identified (runner diameter, holding pressure, flow rate, and melt temperature). Furthermore, to provide an efficient predictive model, machine learning methods such as basic artificial neural networks, their combination with genetic algorithms, and particle swarm optimization have been applied and their performance analyzed. It was found that the basic artificial neural network (ANN), with an average accuracy error of 1.3%, provides the closest predictions to the FEA results. Additionally, the process parameters were optimized using ANOVA and a genetic algorithm, which resulted in a significant reduction in the blush defect area.

Cerro Pinto is a Pleistocene rhyolite tuff ring-dome complex located in the eastern Trans-Mexican Volcanic Belt. The complex is composed of four tuff rings and four domes that were emplaced in three eruptive stages marked by changes in vent location and eruptive character. During Stage I, vent clearing produced a 1.5-km-diameter tuff ring that was then followed by emplacement of two domes of approximately 0.2 km3 each. With no apparent hiatus in activity, Stage II began with the explosive formation of a tuff ring ~2 km in diameter adjacent to and north of the earlier ring. Subsequent Stage II eruptions produced two smaller tuff rings within the northern tuff ring as well as a small dome that was mostly destroyed by explosions during its growth. Stage III involved the emplacement of a 0.04 km3 dome within the southern tuff ring. Cerro Pinto’s eruptive history includes sequences that follow simple rhyolite-dome models, in which a pyroclastic phase is followed immediately by effusive dome emplacement. Some aspects of the eruption, however, such as the explosive reactivation of the system and explosive dome destruction, are more complex. These events are commonly associated with polygenetic structures, such as stratovolcanoes or calderas, in which multiple pulses of magma initiate reactivation. A comparison of major and trace element geochemistry with nearby Pleistocene silicic centers does not show indication of any co-genetic relationship, suggesting that Cerro Pinto was produced by a small, isolated magma chamber. The compositional variation of the erupted material at Cerro Pinto is minimal, suggesting that there were not multiple pulses of magma responsible for the complex behavior of the volcano and that the volcanic system was formed in a short time period. The variety of eruptive style observed at Cerro Pinto reflects the influence of quickly exhaustible water sources on a short-lived eruption. The rising magma encountered small amounts of groundwater that initiated eruption phases. Once a critical magma:water ratio was exceeded, the eruptions became dry and sub-plinian to plinian. The primary characteristic of Cerro Pinto is the predominance of fall deposits, suggesting that the level at which rising magma encountered water was deep enough to allow substantial fragmentation after the water source was exhausted. Isolated rhyolite domes are rare and are not currently viewed as prominent volcanic hazards, but the evolution of Cerro Pinto demonstrates that individual domes may have complex cycles, and such complexity must be taken into account when making hazard risk assessments.

Carbon fibre reinforced plastic (CFRP) manufacturing cycle time is a major driver of production rate and cost for aerospace manufacturers. In vacuum assisted resin transfer molding (VARTM) where liquid thermoset resin is infused into dry carbon reinforcement under vacuum pressure, the design of a resin distribution network to minimize fill time while ensuring the preform is completely full of resin is critical to achieving acceptable quality and cycle time. Complex resin distribution networks in aerospace composites increase the need for quick, optimized virtual design feedback. Framing the problem flow media placement in terms of reinforcement learning, we train a deep neural network agent using a 3D Finite Element based process model of resin flow in dry carbon preforms. Our agent learns to place flow media on thin laminates in order to avoid resin starvation and reduce total infusion time. Due to the knowledge the agent has gained during training on a variety of thin laminate geometries, when presented with a new thin laminate geometry it is able to propose a good flow media layout in less than a second. On a realistic aerospace part with a complex 12-dimensional flow media network, we demonstrate our method reduces fill time by 32% when compared to an expert designed placement, while maintaining the same fill quality.

11 - Compression resin transfer moulding (CRTM) in polymer matrix composites

The process of developing new products is complex, partly due to the wide areas of expertise that need to work together to achieve the best results. Whilst a broad range of software tools is now available to support specific areas of product development, and Product Lifecycle Management systems are starting to support information sharing, the provision of software environments that enable the sharing of information and knowledge across multiple domains of expertise is still a major issue. This paper utilises a set of engineering domain ontologies and investigates the development of mapping methods between them. The work focuses on the development of plastic injection moulded products and has used the particular domains of mouldability, mould cavity design and mould cavity manufacturing to explore the mapping methods across domains. It identifies the critical requirement for mapping methods as understanding the pre-conditional knowledge that constrains the domain boundaries. Experimental mapping methods based on these requirements have been constructed and are discussed in the context of case studies in information sharing for injection moulded product development.

Composite materials are of paramount importance in aeronautics, aerospace and automotive applications. Consisting of fibers and matrix keeping the fibers together, composite materials offer high strength and low weight. In one of the most common composite manufacturing method, Liquid Composite Molding (LCM) process, reinforcing glass, carbon or Kevlar fiber preforms are placed in a mold cavity and a liquid resin is introduced to cover the remaining empty space to form a composite by curing the resin. The primary objective in LCM processes is the successful filling of the preform with resin system. In order to obtain a desired composite product, the process needs to be modeled to have an insight about the filling and curing stages of the LCM process. The fiber preform permeability plays a key role in the filling pattern of the mold, which dictates if there will be any voids in the composites. Thus, to achieve a successful model of the LCM process, one needs to characterize the permeability tensor to quantify the resistance to resin flow through fibers. Besides various experimental and numerical techniques to characterize the permeability, this study offers a new numerical methodology to characterize the permeability of a unit cell of a fiber system. The study involves the characterization of the permeability of a unit cell under various shear strains and project a permeability map from the unit cell characterizations on the macro scale including the drape effect.