Optimal Processing Parameters of Transmission Parts of a Flapping-Wing Micro-Aerial Vehicle Using Precision Injection Molding

Huang, Huei-Yu; Fan, Fang Yu; Lin, Wei Chun; Huang, Chih-Wei; Shen, Yung-Kang; Lin, Yi; Ruslin, Muhammad

doi:10.3390/polym14071467

Cited by 4 publications

(3 citation statements)

References 39 publications

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“…The results show that this technique effectively reduces shrinkage defects in products under different injection parameters. Huang et al [3] discussed the parameters that cause warping in transmission parts of Flapping-Wing Micro-Aerial Vehicles (FW-MAV), including injection temperature, mold temperature, injection pressure, and holding time. They used the Taguchi Methods to find the optimal process parameters and validated the results through CAE system analysis and simulation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Short-Shot Defects in Injection Molding by Transfer Learning

Zhou,

Yang,

et al. 2023

Applied Sciences

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For a long time, the traditional injection molding industry has faced challenges in improving production efficiency and product quality. With advancements in Computer-Aided Engineering (CAE) technology, many factors that could lead to product defects have been eliminated, reducing the costs associated with trial runs during the manufacturing process. However, despite the progress made in CAE simulation results, there still exists a slight deviation from actual conditions. Therefore, relying solely on CAE simulations cannot entirely prevent product defects, and businesses still need to implement real-time quality checks during the production process. In this study, we developed a Back Propagation Neural Network (BPNN) model to predict the occurrence of short-shots defects in the injection molding process using various process states as inputs. We developed a Back Propagation Neural Network (BPNN) model that takes injection molding process states as input to predict the occurrence of short-shot defects during the injection molding process. Additionally, we investigated the effectiveness of two different transfer learning methods. The first method involved training the neural network model using CAE simulation data for products with length–thickness ratios (LT) of 60 and then applying transfer learning with real process data. The second method trained the neural network model using real process data for products with LT60 and then applied transfer learning with real process data from products with LT100. From the results, we have inferred that transfer learning, as compared to conventional neural network training methods, can prevent overfitting with the same amount of training data. The short-shot prediction models trained using transfer learning achieved accuracies of 90.2% and 94.4% on the validation datasets of products with LT60 and LT100, respectively. Through integration with the injection molding machine, this enables production personnel to determine whether a product will experience a short-shot before the mold is opened, thereby increasing troubleshooting time.

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Section: Introductionmentioning

confidence: 99%

Prediction of Short-Shot Defects in Injection Molding by Transfer Learning

Zhou,

Yang,

et al. 2023

Applied Sciences

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“…CAE simulation technology has been used to replace the traditional continuous mode test method [ 23 ]. Combined with optimal design theory, it has been used in the optimization of injection molding process conditions, not only to improve the quality of plastic parts, but also to shorten the molding cycle [ 24 ]. Hakimian et al simulated and analyzed the micro-injection molding process of 0.6 mm- tooth-thickness micro-gear based on Moldflow and optimized the process of maximum warpage and shrinkage using the Taguchi method [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

An Effective Shrinkage Control Method for Tooth Profile Accuracy Improvement of Micro-Injection-Molded Small-Module Plastic Gears

et al. 2022

Polymers

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An effective method to control the non-linear shrinkage of micro-injection molded small-module plastic gears by combining multi-objective optimization with Moldflow simulation is proposed. The accuracy of the simulation model was verified in a micro-injection molding experiment using reference process parameters. The maximum shrinkage (Y1), volume shrinkage (Y2), addendum diameter shrinkage (Y3), and root circle diameter shrinkage (Y4) were utilized as optimization objectives to characterize the non-linear shrinkage of the studied gear. An analysis of the relationship between key process parameters and the optimization objectives was undertaken using a second-order response surface model (RSM-Quadratic). Finally, multi-objective optimization was carried out using the non-dominated sorting genetic algorithm-II (NSGA-II). The error rates for the key shrinkage dimensions were all below 2%. The simulation results showed that the gear shrinkage variables, Y1, Y2, Y3, and Y4, were reduced by 5.60%, 8.23%, 11.71%, and 11.39%, respectively. Moreover, the tooth profile inclination deviation (fHαT), the profile deviation (ffαT), and the total tooth profile deviation (FαT) were reduced by 47.57%, 23.43%, and 49.96%, respectively. Consequently, the proposed method has considerable potential for application in the high-precision and high-efficiency manufacture of small-module plastic gears.

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“…There has been significant work in wing design [6][7][8][9][10][11][12][13][14]. Some research focused on how to efficiently transmit rotary motion of the motors to flapping motions of wings [15,16]. Tail design and body design are also important research problems [1] Research has also explored how to design controllers for FWAVs [17][18][19][20][21][22][23][24][25][26].…”

mentioning

confidence: 99%

A Retrospective of Project Robo Raven: Developing New Capabilities for Enhancing the Performance of Flapping Wing Aerial Vehicles

Bruck,

Gupta

2023

Biomimetics

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Flapping Wing Air Vehicles (FWAVs) have proven to be attractive alternatives to fixed wing and rotary air vehicles at low speeds because of their bio-inspired ability to hover and maneuver. However, in the past, they have not been able to reach their full potential due to limitations in wing control and payload capacity, which also has limited endurance. Many previous FWAVs used a single actuator that couples and synchronizes motions of the wings to flap both wings, resulting in only variable rate flapping control at a constant amplitude. Independent wing control is achieved using two servo actuators that enable wing motions for FWAVs by programming positions and velocities to achieve desired wing shapes and associated aerodynamic forces. However, having two actuators integrated into the flying platform significantly increases its weight and makes it more challenging to achieve flight than a single actuator. This article presents a retrospective overview of five different designs from the “Robo Raven” family based on our previously published work. The first FWAVs utilize two servo motors to achieve independent wing control. The basic platform is capable of successfully performing dives, flips, and button hook turns, which demonstrates the potential maneuverability afforded by the independently actuated and controlled wings. Subsequent designs in the Robo Raven family were able to use multifunctional wings to harvest solar energy to overcome limitations on endurance, use on-board decision-making capabilities to perform maneuvers autonomously, and use mixed-mode propulsion to increase payload capacity by exploiting the benefits of fixed and flapping wing flight. This article elucidates how each successive version of the Robo Raven platform built upon the findings from previous generations. The Robo Raven family collectively addresses requirements related to control autonomy, energy autonomy, and maneuverability. We conclude this article by identifying new opportunities for research in avian-scale flapping wing aerial vehicles.

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Optimal Processing Parameters of Transmission Parts of a Flapping-Wing Micro-Aerial Vehicle Using Precision Injection Molding

Cited by 4 publications

References 39 publications

Prediction of Short-Shot Defects in Injection Molding by Transfer Learning

Prediction of Short-Shot Defects in Injection Molding by Transfer Learning

An Effective Shrinkage Control Method for Tooth Profile Accuracy Improvement of Micro-Injection-Molded Small-Module Plastic Gears

A Retrospective of Project Robo Raven: Developing New Capabilities for Enhancing the Performance of Flapping Wing Aerial Vehicles

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