Scratch Resistance of PP as a Function of MFR and fiber content Jungsub Lee1, Ilhyun Kim1 and Byoung-Ho Choi1* 1School of Mechanical Engineering, Korea University, Seoul, Korea *Corresponding Author : bhchoi@korea.ac.kr

Ⅰ Ⅱ Ⅲ Ⅳ Ⅴ Contents Introduction Methods Materials and Experiments Results & Discussion Ⅳ Conclusion Ⅴ

Introduction Ⅰ

1. Introduction Recently, polymeric materials are widely used in many industrial fields. Unlike metal, material property data of polymers are insufficient. Especially, polymers have many differences in material properties with metals. Ref)http://www.nanowerk.com/spotlight/spotid=23934.php

1. Introduction Polymer materials have different structures and properties according to its monomer or additives Understanding microstructures and their properties are closely related. For example, melt flow index (MFI), number average molecular weight (Mn­) and weight average molecular weight (Mw) can be used to find the intrinsic properties of material. For these reasons, researches have been focused on evaluating intrinsic properties by using structural properties. Ref) Tim A. Osswald and Georg Menges, Material Science of Polymers for Engineers, In Material Science of Polymers for Engineers (Third Edition), Hanser, 2012

1. Introduction The relationship between mechanical properties and structural properties can be different as per the type of polymers Young’s modulus of semi-crystalline polymers (PP and HDPE) are sensitive to the change in density while that of amorphous polymers are not. Processing is also an important factor determining mechanical properties of polymers

1. Introduction Scratch damages of polymer Scratch refers a surface damage caused by dynamic indentation on polymers and are closely related to the emotional quality of the product. It is mainly understood in aesthetical point of view but it can also cause functional failures. Increasing use of polymeric materials results in scratch damage to be observed more common. Various factors such as working environment, loading condition, material composition and mechanical properties should be considered to fully understand scratch behaviors. Scratch resistance can be evaluated by visibility criterion or morphology analysis

1. Introduction Objective Understand the structure of Polypropylene (PP) using various material characterization methods and construct the relationship with scratch properties Design of experiment (DOE) Construct statistical estimations Evaluate intrinsic properties of materials (DSC, GPC, Rheometer) Measure mechanical properties Compare the scratch properties to structural property parameters and draw major parameters affect mechanical properties Construct statistical empirical equations for major structural parameters and scratch properties using multiple regression and neural network models

Methods Ⅱ

Experimental design process 2. Methods Design of Experiment, DOE Experimental design process Ref) https://www.educatevirtually.com/blog/design-of-experiments-process

2. Methods Design of Experiment, DOE Principles of DOE 1. Randomization Conduct tests in random order to exclude unwanted effects except experimental factors 2. Replication & Repetition Repeat tests for multiple times to minimize R-square and increase reliability 3. Confounding (Fractional factorial design) Confound less important interaction effect and block effect for efficient experiment 4. Blocking Obtain uniform experimental environment by making blocks for entire tests 5. Orthogonality Array factors orthogonally to obtain higher resolution and more reliable approximation in same runs

2. Methods Multiple Regression Estimated (or predicted) value of Y Regression model with 2 or more independent variables Estimated (or predicted) value of Y Estimated slope coefficients intercept Random Error Evaluation of goodness-of-fit of regression model - R2 : coefficient of determination, 0 ≤ R2 ≤ 1 Percentage of response variable variation that is explained by its relationship with one or more predictor variables Evaluate how well the regression model explains the data - Adjusted R2 , 0 ≤ R2 ≤ 1 R2 for any model will always increase when a new term is added Adjusted R2 determines how well the model fits the data when adjusting for the number of predictors in the model It is always same or less than R2

2. Methods Neural networks Mimicking biological activities of neurons in brain (interacting with each other and learn from experience) High reliability Complex relationship between input parameter and output parameter Figure. Schematics of Neural network model and actual example of tensile properties estimation

2. Methods Scratch test Designed to* ; ASTM D7027 Standard Test Method for Evaluation of Scratch Resistance of Polymeric Coatings and Plastics Using an Instrumented Scratch Machine Designed to* ; Evaluate the scratch resistance of a particular material Compare the relative scratch resistance of different materials Determine the scratch coefficient of friction of materials Test mode* ; Applying increasing normal load from 2 to 50 N over a distance of 0.1 m at a constant scratch rate of 0.1 m/s Applying constant normal load of 30 N over a distance of 0.1 m at a constant scratch rate of 0.1 m/s *ASTM D7027-13, Standard Test Method for Evaluation of Scratch Resistance of Polymeric Coatings and Plastics Using an Instrumented Scratch Machine, ASTM International, West Conshohocken, PA, 2013, www.astm.org

2. Methods Evaluation of scratch resistances

Materials and Experiments Ⅲ

3. Materials & Experiment Material(resin) : PP copolymer (MFI 5, 27.5, 50) DOE(using JMP) 2 factor 3 level : PP copolymer (MFI 5, 27.5, 50) + glass fiber(GF, 0, 25, 50%) Sample No. PP co. MI (%) GF No.1 5 100 No.2 75 25 No.3 50 No.4 27.5 No.5 No.6 No.7

3. Materials & Experiment Tensile test condition (ISO527) Sample No. Run order MFI (g/10 min) Glass fiber (%) Crosshead speed (mm/min) Repetitions No. 1 4 5 5, 50, 500 No. 2 3 25 No. 3 7 50 No. 4 2 27.5 No. 5 No. 6 1 No. 7 6

3. Materials & Experiment DSC Test machine : TA Q20 N2 condition 10℃/min(heating and cooling) 1st Heating (30℃ to 200℃)  5min isothermal  Cooling (200℃ to 30℃)  5min isothermal  2nd Heating (30℃ to 200℃) GPC Test machine : HT-GPC agilent PL-220 Rheology Rheometer TA ARES-G2 Frequency Sweep at 210℃ Cross equation :

3. Materials & Experiment Modeling parameter MFI Talc content Scratch speed DSC Heat of fusion Crystallinity GPC Mn Mw Mz PD Rheology η0 η∞ K m

Figure. Schematics of scratch tester Kato tech. KK01 and scratch test. 3. Materials & Experiment Scratch test Parameter Value Scratch test Scratch speed 100 mm/s Scratch length mm Initial normal load 1 N Final normal load 40 Tip diameter (unit : mm) Figure. ISO 527 tensile test specimen. Figure. Schematics of scratch tester Kato tech. KK01 and scratch test.

Results & Discussion Ⅳ

4. Results & Discussion Tensile modulus – Regression model Speed-GF-Mw Speed-GF-Mn Speed-GF-Mw

4. Results & Discussion Tensile strength – Regression model Speed-GF-PD-K Speed-GF-PD

4. Results & Discussion Tensile strain – Regression model Speed-GF-MI Speed-GF-MI-PD-K Speed-GF-MI

4. Results & Discussion Tensile toughness – Regression model Speed-GF-MI Speed-GF-Mw

4. Results & Discussion Scratch critical loads – Regression model 1st critical load 2nd critical load GF-Crystallinity-Mn Mw-Heat of fusion

4. Results & Discussion 1st critical loads – Neural network model Speed-GF-DSC(Heat of fusion- Crystallinity

4. Results & Discussion 1st critical loads – Neural network model Speed-GF-GPC(Mn-Mw-Mz-PD)

4. Results & Discussion 1st critical loads – Neural network model Speed-GF-Rheology(η0, K, m)

4. Results & Discussion 1st critical loads – Neural network model Speed-GF- DSC-GPC-Rheology

4. Results & Discussion 2nd critical loads – Neural network model Speed-GF-DSC(Heat of fusion- Crystallinity

4. Results & Discussion 2nd critical loads – Neural network model Speed-GF-GPC(Mn-Mw-Mz-PD)

4. Results & Discussion 2nd critical loads – Neural network model Speed-GF-Rheology(η0, K, m)

4. Results & Discussion 2nd critical loads – Neural network model Speed-GF- DSC-GPC-Rheology

4. Results & Discussion 1st critical load (Neural network model, R2=0.9151749) Speed-GF-GPC(Mn-Mw-Mz-PD) (-3.25570820174488) + -5.83829622533634 * :H1_1 2 + 22.0208712519838 * :H1_2 2 + -4.07074978802704 * :H1_3 2 H1_1 TanH(0.5 * (1.53748726469874 + -0.0793617137611032 * :GF + -0.0000103966789985637 * :Mn + 0.0000001892521095926 * :Mw + -0.0000004585317674298 * :Mz + 0.0406650576988515 * :PD)) H1_2 TanH(0.5 * (0.838756467011836 + -0.0449986629803308 * :GF + -0.0000092576311826422 * :Mn + 0.000001239241992187 * :Mw + -0.0000001117958183097 * :Mz + 0.0847893125917891 * :PD)) H1_3 TanH(0.5 * ((-3.50682049915361) + -0.0383427272999866 * :GF + 0.0000138631700344862 * :Mn + 0.0000022746173538648 * :Mw + 0.0000001886444922422 * :Mz + 0.131555021170727 * :PD)) 2nd critical load (Neural network model, R2=0.95307) Speed-GF-Rheology(η0, K, m) 60.938172227884 + 9.12365105778355 * :H1_1 3 + 19.0664633985984 * :H1_2 3 + 48.6485861651154 * :H1_3 3 H1_1 TanH(0.5 * ((-10.7593610255193) + 0.404894480160279 * :GF + -2.89522119010697e-10 * :Name("zero shear viscosity,η0") + 0.0000004145307745382 * :K + 8.06383773156311 * :m)) H1_2 TanH(0.5 * ((-6.42609296239263) + 0.0760357859566564 * :GF + 1.42671608313776e-10 * :Name("zero shear viscosity,η0") + 0.0000001637756250879 * :K + 9.07352625957274 * :m)) H1_3 TanH(0.5 * (12.3960986904927 + -0.164604948701038 * :GF + 0.0000000005288382168 * :Name("zero shear viscosity,η0") + 0.0000002610660400484 * :K + -26.179441243175 * :m))

Conclusion Ⅴ

5. Conclusion Scratch resistances of PP were obtained from ASTM standard test Critical loads and structural parameters (DSC, GPC, Rheometer) were statistically analyzed to evaluate correlation Empirical equations on scratch resistances in terms of major structural parameters were constructed using statistical modeling (Multiple regression and neural network model ) - Modeling input factors were GF content, scratch speed, DSC (crystallinity, heat of fusion), GPC (Mn, Mw, Mz, PD) and Rheology (zero shear viscosity, K, m). Based on R2, neural network models showed better reliability than regression models. It can be understood in that neural network model has advantages in non-linear over regression model and shows better results.

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