Formindske between ejection force and core geometry and surface texture in injection...

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Elucidating the dependence between ejection force and core geometry and surface texture in injection molding Poul-Erik Hansen

Transcript of Formindske between ejection force and core geometry and surface texture in injection...

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    Elucidating the dependence between ejection force and core geometry and surface texture in injection molding

    Poul-Erik Hansen

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    23 January 2020 2

    40 staff members

    Located in Hørsholm

    Established 1985

    DANAK accredited services

    ISO 9001, 17025 certified

    Appointed as Denmark’s National Metrology Institute (NMI)

    GTS institute (Godkendt Teknologisk Service)

    DFM in Short

    http://www.bipm.org/en/home/http://www.bipm.org/en/home/

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    What does DFM offer?

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    MeasurementsCharacterizationsData analysis

    In different areas of metrology

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    • Dimensional metrology • Surface characterization• Roughness• Material characteristics• Nano particles

    • Particle counters• Optical 3D (µm – cm)

    Todays talk is about Nanometrology

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    This work is a cooperation between

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    DFM: Analysis

    DTI: Coatings

    iRAP: Moulding

    AgieCharmilles: Structuring

    WMT: Cores

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    Introduction

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    Injection Molding

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    Profile roughness parameters - amplitude

    Ra/Rq:

    Global average of the roughness amplitude within a sampling length (lr)

    Averaging Ra/Rq for all sampling lengths

    No information on the lateral movement

    𝑅𝑞 =1

    𝑙න0

    𝑙

    𝑍2 𝑥 𝑑𝑥

    Ra

    mm

    mm

    mm

    mm

    𝑅𝑎 =1

    𝑙න0

    𝑙

    𝑍 𝑥 𝑑𝑥

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    Area roughness

    𝑅𝑎 =1

    𝑙න0

    𝑙

    𝑍 𝑥 𝑑𝑥 𝑆𝑎 =1

    𝐴න𝐴

    𝑍 𝑥, 𝑦 𝑑𝑥𝑑𝑦

    𝑅𝑞 =1

    𝑙න0

    𝑙

    𝑍2 𝑥 𝑑𝑥 𝑆𝑞 =1

    𝐴ඵ𝐴

    𝑍2 𝑥, 𝑦 𝑑𝑥𝑑𝑦

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    Autocorrelation and autocorrelation length

    Random Line profile Autocorrelation

    Non-Random Line profile

    Vary width of window for similar pattern

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    Areal autocorrelation and autocorrelation length

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    AIM of this work

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    Surface topography, Roughness and demolding force

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    ?

    ?:• Surface area. Correct but difficult to work with

    • Roughness: Works sometime but not always

    Work

    Demolding force

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    Hypotheses to be tested:

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    • Roughness works if we use• Area instead of line profile• The cores are sorted according to their surface topography

    Standard way

    All surface topography treated together

    New way

    Surface topographyType 1

    Surface topographyType 2

    Surface topographyType 3

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    Sliding friction for the Surface Topography groups

    23 January [email protected]

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    Surface StructureRandom mixed Directional

    AB

    AB A

    B

    Waking is a Friction process

    Going from A to B along height contour require less energy

    Autocorrelation -> contour lines

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    We select the surface topography groups from the autocorrelation

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    Random Mixed Directional

    Autocorrelation -> contour lines

    This approach require and image, Not possible with line profiles

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    Demolding force, surface topography and plastic material

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    Random

    Mixed

    Directional

    Time (ms)

    Force

    Random pattern require most workStick/slipping is most prominent on directional structure

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    Correlations between roughness parameter and initial demolding force

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    Much better correlation thanStandard method

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    Correlations between roughness parameter and initial demolding force

    Traditional surface parameters, like Sa (Ra), cannot in general be used to optimize the friction properties for injection molding. But they can be used to optimize surfaces with random texturing

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    The thing to remember from this talk

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    Area are much better than line profile

    Vs.

    Different Surface topography requires different treatments

    Standard method works fine for surface with random topography

    Lets discuss the role of Directional/Mixed roughness and Plastics material in the

    break

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    If you want to know more …

    23 January 2020 21

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    Thanks to our Sponsors

    23 January 2020 22

    This work is partly funded by the Innovation Fund Denmark (IFD) under File No. 6151-00004B

    This work is partly funded by the Danish Agency for Science, Technology and Innovation