Robust Process Development and Scientific Molding

Kulkarni, Suhas

doi:10.3139/9781569905876.fm

Cited by 5 publications

(6 citation statements)

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“…One explanation for the difference in injection pressures is the intensification ratio (IR). The IR describes the pressure intensification between the hydraulic pressure applied and the specific pressure in the screw antechamber ( Figure 16 ) [ 31 ]. Usually, the applied hydraulic pressure affects a larger cross-sectional area inside the piston than the specific pressure in the screw antechamber.…”

Section: Resultsmentioning

confidence: 99%

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Analysis of the Machine-Specific Behavior of Injection Molding Machines

Knoll,

Heim

2023

Polymers

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The performance of an injection molding machine (IMM) influences the process and the quality of the parts manufactured. Despite increasing data collection capabilities, their machine-specific behavior has not been extensively studied. To close corresponding research gaps, the machine-specific behavior of two hydraulic IMMs of different sizes and one electric IMM were compared with each other as part of the investigations. Both the start-up behavior from the cold state and the behavior of the machine at different operating points were considered. To complement this, the influence of various material properties on the machine-specific behavior was investigated by processing an unreinforced and glass-fiber-reinforced polyamide. The results obtained provide crucial insights into machine-specific behavior, which may, for instance, account for disparities between computer fluid dynamic (CFD) simulations and experimental results. Furthermore, it is expected that the description of the machine-specific behavior can contribute to transfer knowledge when applying transfer learning algorithms. Looking ahead to future research, it is advised to create what is referred to as a “machine fingerprint”, and this proposal is accompanied by some preliminary recommendations for its development.

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Section: Resultsmentioning

confidence: 99%

“… Representation of the screw and ram areas for the computation of the intensification ratio, in relation to [ 31 ]. …”

Section: Figurementioning

confidence: 99%

Analysis of the Machine-Specific Behavior of Injection Molding Machines

Knoll,

Heim

2023

Polymers

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“…The variance of injection moulded products can be considered from multiple perspectives and depends on the level of reference [12]. In this work, it is separated into three levels:…”

Section: Part Property Variancementioning

confidence: 99%

“…The in-mould shrinkage can be minimised by forcing additional plastic into the cavity. This can be done from several machine settings such as, e.g., holding pressure [12]. Controlling the amount of added plastic to the cavity can be used to minimise the variance of the produced parts from cycle to cycle by adapting the moulding process dependent on the material.…”

Section: Shrinkage Of Amorphous Materialsmentioning

confidence: 99%

Cycle-Based Control of Injection Moulding Process in Presence of Material Dual Sourcing Using Mass Feedback

Hertz,

Therkelsen,

Kristiansen

et al. 2024

Polymers

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The low cost and precise tolerances of plastic injection moulded products are a major reason for the popularity of the manufacturing method. The tolerances are greatly influenced by the equipment, raw material and moulding process. One challenge is the raw material variation. This paper presents a production process using cycle based feedback of cycle mass, for control of part properties in the presence of material variation from dual sourcing. The part properties considered are part mass and outer dimensions. The process uses direct cycle mass feedback without additional process measurements in the proposed controller structure. The designed controller structure is tested in a multi-cavity mould while using raw materials from multiple vendors, encompassing five different grades. The results show a total decrease in part mass variance of approximately 50% and a decrease of length and width variance of approximately 40% compared to a moulding process with fixed settings.

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“…Process windows are designed to establish valid ranges of key parameters based on scientific molding principles. [28] It narrows the range of valid data for Taguchi experiments and improves computing efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent recommendation system of the injection molding process parameters based on CAE simulation, process window, and machine learning

Ma,

Wang,

Dang

et al. 2023

Int J Adv Manuf Technol

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In this research, a recommendation system was designed for optimizing the injection molding process parameters. The system incorporates the utilization of process windows, XGBoost, and genetic algorithms. CAE simulations were conducted to generate process window data and simulation data. Automatic hyperparameter optimization of the XGBoost was performed using grid search and cross-validation methods. The system employs 5 injection molding feature parameters as input and one product feature as output, and SEGA was used for predicting the optimal injection molding process parameters. The performance of the prediction model was evaluated using an RMSE of 0.0202 and an R 2 of 0.9826. The accuracy of the system was verified by conducting real production. The deviation of the product weight obtained from real production from the desired weight is 0.22%, which means that the prediction model achieves a correct rate of 99.78%. This recommendation system has a significant application value in reducing production costs and cycle time, as it can provide initial injection process parameter suggestions solely through the mold's digital data.

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Robust Process Development and Scientific Molding

Cited by 5 publications

References 0 publications

Analysis of the Machine-Specific Behavior of Injection Molding Machines

Analysis of the Machine-Specific Behavior of Injection Molding Machines

Cycle-Based Control of Injection Moulding Process in Presence of Material Dual Sourcing Using Mass Feedback

Intelligent recommendation system of the injection molding process parameters based on CAE simulation, process window, and machine learning

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