Process Prediction for Compound Screws by Using Virtual Measurement and Recognizable Performance Evaluation

Chang, Hanjui; Zhang, Guangyi; Su, Zhiming; Mao, Zhiping

doi:10.3390/app11041549

Cited by 5 publications

(5 citation statements)

References 27 publications

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“…In 2020, Chang [25] proposed a screw life prediction method based on a hybrid composite screw process-parameter method for dynamic iterative work. A combination of an automated virtual metering (VM) system and a recognizable performance evaluation (RPE) scheme was proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Sequence-Approximation Optimization and Radial-Basis-Function Network for Brake-Pedal Multi-Target Warping and Cooling

et al. 2022

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This paper uses a multi-objective optimization method to optimize the injection-molding defects of automotive pedals. Compared with the traditional automotive pedal material, aluminum alloy, the polymer pedal containing glass fibers not only reduces the aluminum pedal by at least half, but also improves the strength and hardness of the fibers by adjusting the orientation of the fibers in all directions. Injection factors include: filling time, filling pressure, melt temperature, cooling time, injection time, etc. For the optimization process influencing factors, herein, we focus on warpage analyzed via flow simulation, and setting warpage parameters and cycle time as discussed by setting different cooling distributions, pressures and temperature schemes. The multi-objective optimization design was mainly used to describe the relationship between cycle time and warpage, and the Pareto boundary was used for cycle time and warpage to identify the deviation function and radial-basis-function network. We worked with a small DOE for building the surface to run SAO programming—which improved the accuracy of the response surface by adding sampling points—terminating the time when the warpage value met the solution requirements, to find out the global optimal solution of the warpage value under different cooling times. Finally, the results highlighted four influencing parameters that match the experimental image of the actual production.

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Section: Literature Reviewmentioning

confidence: 99%

Using Sequence-Approximation Optimization and Radial-Basis-Function Network for Brake-Pedal Multi-Target Warping and Cooling

et al. 2022

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“…The thickness of the transition layer on the membrane side and the bubble marks on the surface of the part change in the opposite way. In 2021, Han-Jui Chang et al [ 19 ] proposed a method for predicting screw process parameters: using crystalline and amorphous polymers as molding materials to predict the injection of composite screws by converting sampling checks that measure delays from real-time and online routine inspections to automated rapid completion methods to create production targets. It can be seen that the recognizable performance evaluation has great advantages in obtaining and analyzing the characteristics of the relevant parameters.…”

Section: Literature Reviewsmentioning

confidence: 99%

Non-Dominant Genetic Algorithm for Multi-Objective Optimization Design of Unmanned Aerial Vehicle Shell Process

et al. 2022

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This paper uses Pareto-optimized frames and injection molding process parameters to optimize the quality of UAV housing parts with multi-objective optimization. Process parameters, such as melt temperature, filling time, pressure, and pressure time, were studied as model variables. The quality of a plastic part is determined by two defect parameters, warpage value and mold index, which require minimal defect parameters. This paper proposes a three-stage optimization system. In the first stage, the main node position of the electronic chip in the module is collected by the unified sampling method, and the chip calculation index of these node positions is analyzed by the mold flow analysis software. In the second stage, the kriging function predicts the mathematical relationship between the mold index and warpage value and the process parameters, such as melt temperature, filling time, packing pressure, and packing time. In the third stage, using LHD sampling and non-dominant rank genetic algorithm II, a convergence curve of warp value is found near the Pareto optimal frontier. In the fourth stage, the fitting degree of Pareto optimal leading edge curve points was verified by analytical experiments. According to experimental verification, it can be seen that the injection molding factors are pressure and pressure time, because the injection molding time and pressure time are completely positively correlated with the mold indicators, the correlation is the strongest, the mold temperature and glue temperature are not the main influencing factors, and the mold temperature shows a certain degree of negative correlation. In this experiment, the die index is mainly improved by injection time and pressure, optimal injection parameter factor combination and minimum injection index, the optimization rate of the die index is up to 96.2% through genetic algorithm optimization nodes and experimental verification, the average optimization rate of the four main optimization nodes is 91.2%, and the error rate with the actual situation is only 8.48%, which is in line with the needs of actual production, and the improvement of the UAV IME membrane is realized.

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“…The corresponding theoretical formula can be used to effectively quantify and compensate for the measurement of the residual stress of the product. In 2021, Han-Jui Chang [ 20 ] pointed out that in the evaluation of identifiability, as it helps to make the product’s characteristics more prominent. It is not easy to evaluate the reference value due to the influence of different factors, but a single variable can become the dependent variable, the main influencing factor.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting of Process Parameters for Theoretical Concentrated Stress of Fatigue Notch Coefficient of Auto Parts Using Virtual Recognizable Performance Evaluation Research

Chang

Sun

et al. 2022

Polymers

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This paper analyzes the structure of the key parts of the car belt guide, and the average stress of the vulnerable parts is simulated by analysis software. The theoretical stress of the section is calculated. The theoretical stress concentration factor (Kt) is given. The relation between the gap radius and the notch coefficient (Kf) was studied according to a previous Kf calculation formula. The tensile tests of real products are used as reference data. The results showed that Kf and Kt are linear in most cases, but there are also cases of non-compliance. The relationship between the fatigue notch coefficient Kf and the theoretical stress concentration coefficient Kt was closely related to the service life and fatigue strength of the product. In addition, we found that the size and direction of warpage improved significantly with the increase of fillet size, which was not consistent with the effect of adding glass fiber material. The rounded corners of ordinary PP materials usually displayed forward warping, but the addition of glass fiber into PP materials made the degree of warping smaller, or even led to reverse warping. The size of rounded corners is an important optimization parameter. The relationship between Kf and Kt was studied from the perspectives of virtual measurement (VM) and recognizable performance evaluation (RPM). According to abnormal filling pressure, these relationships were compared with filling data to generate a fracture initiation control model. Based on a large amount of normal process data and quality inspection data, the historical data (causes) and quality inspection data (results) were combined.

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Process Prediction for Compound Screws by Using Virtual Measurement and Recognizable Performance Evaluation

Cited by 5 publications

References 27 publications

Using Sequence-Approximation Optimization and Radial-Basis-Function Network for Brake-Pedal Multi-Target Warping and Cooling

Using Sequence-Approximation Optimization and Radial-Basis-Function Network for Brake-Pedal Multi-Target Warping and Cooling

Non-Dominant Genetic Algorithm for Multi-Objective Optimization Design of Unmanned Aerial Vehicle Shell Process

Predicting of Process Parameters for Theoretical Concentrated Stress of Fatigue Notch Coefficient of Auto Parts Using Virtual Recognizable Performance Evaluation Research

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