The Study of Optimal Molding of a LED Lens with Grey Relational Analysis and Molding Simulation

Wang, Min-Wen; Arifin, Fatahul; Vu, Van-Hanh

doi:10.3311/ppme.13337

Cited by 15 publications

(7 citation statements)

References 17 publications

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“…The best way to control the process is to use sensors in the mold [4,5]. In case of injection molding, melt temperature, mold temperature, injection rate, holding time, and holding pressure significantly affect the properties of injection molded parts [6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Effect of Processing Parameters and Wall Thickness on the Strength of Injection Molded Products

Horváth,

Kovács

2024

Period. Polytech. Mech. Eng.

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Polymer materials are getting more and more attention in key industries like the automotive, aerospace, and electrical industries. A long lifetime with sustainable recycling is expected; great results are possible thanks to modern material engineering. Finding a suitable material for the purpose and the appropriate methods to test the required functionality is essential. An important mechanical loading tests is the tensile test. Although the ISO 527-1:2019 standard uses different tensile specimen geometries, none of those consider the possible thickness variations of the injection molded parts. If thickness is reduced to 1 mm, the solid layer will be dominant, which makes the properties of the tensile specimen process-dependent instead of material-dependent. In today's industry, it is crucial to examine both the material and the details of the processing technology together, given the downsizing effect.

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Section: Introductionmentioning

confidence: 99%

Effect of Processing Parameters and Wall Thickness on the Strength of Injection Molded Products

Horváth,

Kovács

2024

Period. Polytech. Mech. Eng.

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“…Therefore, finding a relation between product quality and process parameters is a non-trivial task that requires a comprehensive approach, which often involves a combination of sensoring and information techniques [ 6 ]. Statistical analysis [ 7 , 8 ] and artificial intelligence (AI) [ 9 ], especially machine learning (ML) [ 10 , 11 , 12 ], are used more and more frequently today for the process optimization and quality control of industrial manufacturing processes. However, these methods are data-driven and often do not consider the physical aspects of injection molding.…”

Section: Introductionmentioning

confidence: 99%

Multiple In-Mold Sensors for Quality and Process Control in Injection Molding

Párizs

Török

Ageyeva

et al. 2023

Sensors

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The simultaneous improvement of injection molding process efficiency and product quality, as required by Industry 4.0, is a complex, non-trivial task that requires a comprehensive approach, which involves a combination of sensoring and information techniques. In this study, we investigated the suitability of in-mold pressure sensors to control the injection molding process in multi-cavity molds. We have conducted several experiments to show how to optimize the clamping force, switchover, or holding time by measuring only pressure in a multi-cavity mold. The results show that the pressure curves and the pressure integral are suitable for determining optimal clamping force. We also proved that in-channel sensors could be effectively used for a pressure-controlled SWOP. In the volume-controlled method, only the sensors in the cavity were capable of correctly detecting the end of the filling. We proposed a method to optimize the holding phase. In this method, we first determined the integration time of the area under the pressure curve and then performed a model fit using the relationship between the pressure integral and product mass. The saturation curve fitted to the pressure data can easily determine the gate freeze-off time from pressure measurements.

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“…Quality assurance (QA) steps are a vital piece of this process. The data gathered from assessments of the dimensions, shrinkage, warpage, and tensile stress are always useful indicators [19][20][21][22][23]. Conventionally, a correlation analysis of such parameters with the primary process parameters, such as the mold temperature, melt temperature, injection velocity, and packing pressure can provide insight into how the part can be manufactured with higher quality [24].…”

Section: Introductionmentioning

confidence: 99%

Impact of Injection Molding Parameters on Material Acoustic Parameters

Saeedabadi,

Lickert,

Bruus

et al. 2023

JMMP

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Understanding the relationship between injection molding parameters and the acoustic properties of polymers is crucial for optimizing the design and performance of acoustic-based polymer devices. In this work, the impact of injection molding parameters, such as the injection velocity and packing pressure, on the acoustic parameters of polymers, namely the elastic moduli, is studied. The measurements lead to calculating material parameters, such as the Young’s modulus and Poisson’s ratio, that can be swiftly measured and determined thanks to this method. Polymethyl methacrylate (PMMA) was used as the molding material, and using PMMA LG IG 840, the parts were simulated and injection molded, applying a ‘design of experiment’ (DOE) statistical method. The results indicated a correlation between the injection molding process parameters and the acoustic characteristics, such as the elastic moduli, and a specifically decreasing trend with increase in the injection velocity. Notably, a relative decrease in the Young’s modulus by 1% was observed when increasing the packing pressure from 90MPa to 120MPa. Similarly, a decrease in the Poisson’s ratio of 2.9% was observed when the injection velocity was increased from 16mm/s to 40mm/s. This method can be used to fine-tune the material properties according to the needs of a given application and to facilitate the characterization of different polymer acoustic properties essential for acoustic-based polymer devices.

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The Study of Optimal Molding of a LED Lens with Grey Relational Analysis and Molding Simulation

Cited by 15 publications

References 17 publications

Effect of Processing Parameters and Wall Thickness on the Strength of Injection Molded Products

Effect of Processing Parameters and Wall Thickness on the Strength of Injection Molded Products

Multiple In-Mold Sensors for Quality and Process Control in Injection Molding

Impact of Injection Molding Parameters on Material Acoustic Parameters

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