Evaluation of Additive Manufacturing Complexity Metrics : CFR-PEEK in Fused Filament Fabrication (FFF)

1. Evaluation of Additive Manufacturing Complexity Metrics : CFR-PEEK
in Fused Filament Fabrication (FFF)
201401215 김규동
201701349 노희나
201701371 진승원
< 시스템종합설계 >
적층제조 복잡성 평가에 대한 고찰 : CFR-PEEK 소재의 FFF 프린팅 중심으로

2. Introduction001
▪ Complexity for Traditional Manufacturing
• As the manufacturing system becomes more complex and sophisticated, the complexity of the process increases (Park, 2015)
• Various product range and parts, unnecessary process cause uncertainty (Frizelle, 1996)
• Complex design product needs more information in manufacturing, it occurs extra costs for product production
(Creps-Varela, 2011)
• Complexity for Additive Manufacturing
• Definition and impacts of complexity are discussed qualitatively in terms of design for additive manufacturing (DFAM)
(Durakovic, 2018)
➢ Non-quantitative analysis for complexity in DFAM
• Impact on the complexity of the product design is compared with the performance in AM (Baumers, 2016; Pradel, 2017)
➢ Not including process in complexity
• Analysis results vary in complexity definition (Chase and Murty, 2000; Johnson, 2017)
➢ Various complexity definitions and metrics evaluation are needed for effective AM operation
Previous studies indicate metrics and impacts on manufacturing for complexity
(Huatuco, 2001; Sivadasan, 2006; Park, 2015; Efthymiou, 2016)

3. ▪ Fused Filament Fabrication (FFF)
• The most popular 3D printing method in AM techniques (Shih, 2019)
• Process that uses a continuous filament of a thermoplastic material
• Depositing material layer by layer
• Available for manufacturing complex structure
➢ Suitable for customization
• Applied to the manufacturing of patient-specific medical and industrial parts (Cwikla, 1992)
Introduction001
▪ CFR–PEEK (Carbon Fiber Reinforced Polyether-Ether-Ketone)
• High stiffness
• Good wear resistance
• High temperature resistance
• Lightweight
• Good fatigue resistance
➢ Available for various industrial fields (i.e. aircraft, medical, aerospace
(Schwitalla, 2015; Apiumtec)

4. Literature Review002
▪ Previous Studies
• Evaluating complexity of the manufacturing system quantitatively
(Frizelle, 1995; Suh, 1999; Huatuco, 2001; Papakostas, 2009; Summers,
2010; Crepso-Varela, 2011; Park, 2015)
• Definition and qualitative assessment of complexity in AM
(Baumers, 2016; Pradel, 2017; Durakovic, 2018)
• Evaluation shape complexity in CAD
(Chase and Murty, 2000; Johnson, 2017; Kwon, 2017)
• Using affordable materials (i.e. PLA, ABS)
(Miguel, 2016; Manogharan, 2015; Podrouzek, 2019; Huu, 2018)
Measure the complexity in terms of AM
Objectives
▪ Complexity of AM is defined and quantified in terms of various viewpoints
▪ It presents complexity as metrics
▪ It evaluates the impact of complexity on AM manufacturing performance
▪ This Study
Definition and quantitative assessment of
various complexity including process in AM
Using high performance material, CFR-PEEK

5. Development of complexity for additive manufacturing003
▪ Shape Complexity
• STL (Standard Triangle Language) is a file format native to the CAD software created by 3D systems
• A shape of CAD model is composed of triangle in STL
• The number of triangle in STL means geometric complexity for CAD model (Valentan, 2011)
➢ Shape Complexity = The number of triangle of the product in STL
▪ Volume Complexity
• Complexity takes the form a ratio defined by the product’s area or volume as related to another shape (Johnson,2017)
➢ 𝑽 𝑷 = The volume of the product
➢ 𝑽 𝑩 = The volume of the bounding box of the product
➢ Volume Complexity = −𝒍𝒐𝒈 𝟐(𝟏 −
𝑽 𝑷
𝑽 𝑩
)(Joshi & Ravi, 2013)

6. Development of complexity for additive manufacturing003
▪ Process Complexity
• A measure of uncertainty in realizing the commonality of G – Code (𝑋, 𝑌) for building a product in additive manufacturing
• The more complex the product design, the more information and precise movement of the G-Code is needed
➢ 𝑵 = The number of total G – Code (𝑿, 𝒀) for building a product
➢ 𝒏𝒊= The number of same G – Code (𝑿, 𝒀) Ɐ 𝒊 = kind of G-Codes
➢ Probability (commonality) of being the same of each G – Code (𝑿, 𝒀) for building a product
𝑪𝒊= 𝒏𝒊/𝑵
➢ Individual information content of each G – Code (𝑿, 𝒀) for building a product
𝑰𝒊= − 𝒍𝒐𝒈 𝟐 𝑪𝒊
➢ Total information content of a product for additive manufacturing (G-Code complexity)
G-Code Complexity = σ𝒊 𝑰𝒊= σ𝒊(− 𝒍𝒐𝒈 𝟐 𝑪𝒊)
▪ Machine Complexity
• A measure of uncertainty for satisfying the maximum system level (Suh, 1999)
• If it increase in manufacturing system, the efficiency in machine would increase and it can reduce the uncertainty
for system or customer needs
➢ 𝑴 = Maximum extruded filament volume per unit time
➢ 𝒓 = Extruded filament volume per unit time
➢ Probability of reaching the maximum extruded filament volume per unit time
➢ 𝒇 =𝒓/𝑴
➢ Information content of extruded filament volume per unit time
➢ Machine Complexity = − 𝒍𝒐𝒈 𝟐 𝒇

7. Methodology : Case study for aircraft component004
▪ Products (20 units)
Bracket
A B C D
Door Hinge
A B C D
Engine Cover
A B C D
Fork Fitting
A B C D
Seat Buckle
A B C D
• CFR-PEEK is optimal material for aircraft applications
(J Ortega-Martinez, 2017)
• Aircraft components with CFR-PEEK are set to our
case study
• Virtual 3D Printer factory using CFR-PEEK is modeled through
Discrete Event Simulation
To identify manufacturing performance
in mass production
▪ Simulation
• 20 aircraft components is considered as samples
• Consider the following as manufacturing performance
- Lead Time
- Energy Cost
- Material Cost

8. Methodology : Case study for aircraft component004
1. Order
• The interarrival time for orders follows normal distribution (μ = 4 hours, σ 𝟐
= 0.8 hours)
• In the model, one of 20 order types arrives randomly and with equal chance (0.05)
• The order quantity of each order type follows a discrete normal distribution (μ = 15, σ 𝟐
= 3.3166)
2. Pre-Processing
• In order to print the products for orders, CAD file for each order type should be encoded
• Encoding processing time follows a constant value (5 minutes)
3. Processing
• In the model, the number of 3D printer is 15
• Before printing, the printer should be processed the bed calibration that processing time follows a constant value (15 minutes)
• A normal distribution (μ = build time for each order types, σ 𝟐
= 100 sec) and heating (4 minutes) and cooling (30 minutes) is
reflected in the printing time
4. Pro-Processing
• After printing a product, a product should be processed the after-processing that is remove the brim or supporter and make the
product smooth
• Each of the order types has same after-processing time (10 minutes)
▪ After all process, the product is stored and when the order quantity is fulfilled, the order is transported to customer
▪ In the model, virtual 3D printing factory operates 52 weeks (= 1year) and replicates 500 times

9. Methodology : Case study for aircraft component004
▪ Virtual 3D Printing Factory Simulation Model (Video)

10. Results005
▪ Calculation of proposed complexity metrics
Product
Bracket A Bracket B Bracket C Bracket D Door hinge A Door hinge B Door hinge C Door hinge D
Shape Complexity 10660 798 2246 1168 3650 15998 2176 1392
Volume Complexity 0.228592 0.619189 0.11859 0.582974 1.05036 0.681346 0.286003 0.759779
Process Complexity 25188.018 17338.92941 9332.82654 17889.49469 17576.03967 20156.13805 23744.78189 15448.90796
Machine Complexity 1.92430576 1.759369 1.889222259 1.848396 1.833129027 1.89898935 1.843318 1.791041
Engine cover A Engine cover B Engine cover C Engine cover D Fork fitting A Fork fitting B Fork fitting C Fork fitting D
Shape Complexity 22000 5662 1294 9000 3622 3056 10354 4832
Volume Complexity 0.566126 0.374333 0.206186 0.087827 0.415214 0.71792 0.14794 0.714453
Process Complexity 13539.70905 10620.70992 32642.65593 30341.31376 27375.04969 18718.97382 40523.36995 32411.75524
Machine Complexity 1.881807 2.4429732 1.912310432 2.037179541 1.898801942 1.815797406 2.090966522 1.783216519
Seat buckle A Seat buckle B Seat buckle C Seat buckle D
Shape Complexity 388 312 1268 3248
Volume Complexity 0.851337 0.930485 0.67151 0.250286
Process Complexity 18315.54793 21682.02943 20938.31124 18504.68481
Machine Complexity 1.83373621 1.808343392 1.411694 1.875769

11. Results005
▪ Pearson Correlation Analysis between Complexity and Manufacturing Performance
<Pearson Correlation>
Shape complexity : 0.500
Volume complexity : 0.679
Process complexity : 0.094
Machine complexity : 0.700
<Pearson Correlation>
Shape complexity : 0.490
Volume complexity : 0.668
Process complexity : 0.084
Machine complexity : 0.690
<Pearson Correlation>
Shape complexity : 0.464
Volume complexity : 0.579
Process complexity : 0.112
Machine complexity : 0.177
<Pearson Correlation>
Shape complexity : 0.464
Volume complexity : 0.580
Process complexity : 0.112
Machine complexity : 0.179
Material Cost
Energy CostLead Time
Total Cost

12. Results005
▪ Single Linear regression analysis
• Shape Complexity
• Volume Complexity
• Process Complexity
• Machine Complexity
Lead Time Energy Cost Material Cost Total Cost
1. Regression Equation 𝑦 = 10.000 + 0.000085x 𝑦 = 77.85 + 0.001345x 𝑦 = 18585 + 0.259x 𝑦 = 18663 + 0.260x
2. R-sq 24.98% 24.06% 21.52% 21.57%
3. Test for𝛃 𝟎 𝑡 = 37.81, 𝑝 = 0.000** 𝑡 = 18.14, 𝑝 = 0.000** 𝑡 = 20.95, 𝑝 = 0.000** 𝑡 = 20.96, 𝑝 = 0.000**
4. Test for 𝛃 𝟏 𝑡 = 2.45, 𝑝 = 0.025** 𝑡 = 2.39, 𝑝 = 0.028** 𝑡 = 2.22, 𝑝 = 0.039** 𝑡 = 2.22, 𝑝 = 0.039**
𝑥= Shape Complexity, 𝛼 = 0.05**,𝛼 = 0.1*
Lead Time Energy Cost Material Cost Total Cost
1. Regression Equation 𝑦 = 7.084 + 4.70x 𝑦 = 31.5 + 74.6x 𝑦 = 10540 + 13134x 𝑦 = 10572 + 13208x
2. R-sq 46.09% 44.69% 33.52% 33.62%
3. Test for𝛃 𝟎 𝑡 = 8.13, 𝑝 = 0.000** 𝑡 = 2.22, 𝑝 = 0.040** 𝑡 = 3.32, 𝑝 = 0.004** 𝑡 = 3.32, 𝑝 = 0.004**
4. Test for 𝛃 𝟏 𝑡 = 3.92, 𝑝 = 0.001** 𝑡 = 3.81, 𝑝 = 0.001** 𝑡 = 3.01, 𝑝 = 0.007** 𝑡 = 3.02, 𝑝 = 0.007**
𝑥= Volume Complexity, 𝛼 = 0.05**,𝛼 = 0.1*
Lead Time Energy Cost Material Cost Total Cost
1. Regression Equation 𝑦 = 10.186 + 0.000012x 𝑦 = 81.1 + 0.000169x 𝑦 = 18936 + 0.0455x 𝑦 = 19017 + 0.0457x
2. R-sq 0.88% 0.71% 1.25% 1.24%
3. Test for𝛃 𝟎 𝑡 = 15.22, 𝑝 = 0.000** 𝑡 = 7.52, 𝑝 = 0.000** 𝑡 = 8.65, 𝑝 = 0.000** 𝑡 = 8.65, 𝑝 = 0.000**
4. Test for 𝛃 𝟏 𝑡 = 0.40 𝑝 = 0.694 𝑡 = 0.36, 𝑝 = 0.724 𝑡 = 0.48, 𝑝 = 0.639 𝑡 = 0.48, 𝑝 = 0.640
𝑥= Process Complexity, 𝛼 = 0.05**,𝛼 = 0.1*
Lead Time Energy Cost Material Cost Total Cost
1. Regression Equation 𝑦 = 3.52 + 3.682x 𝑦 = −25.2 + 58.5x 𝑦 = 14192 + 3048x 𝑦 = 14167 + 3106x
2. R-sq 48.98% 47.63% 3.12% 3.22%
3. Test for𝛃 𝟎 𝑡 = 2.10, 𝑝 = 0.050** 𝑡 = −0.92, 𝑝 = 0.368 𝑡 = 1.88, 𝑝 = 0.077* 𝑡 = 1.87, 𝑝 = 0.078*
4. Test for 𝛃 𝟏 𝑡 = 4.16, 𝑝 = 0.001** 𝑡 = 4.05 𝑝 = 0.001** 𝑡 = 0.76, 𝑝 = 0.456 𝑡 = 0.77, 𝑝 = 0.449
𝑥= Machine Complexity, 𝛼 = 0.05**,𝛼 = 0.1*

13. Results005
▪ Multiple Linear regression analysis
𝑥1= Shape Complexity, 𝑥2 = Volume Complexity, 𝑥3 = Process Complexity, 𝑥4 = Machine Complexity
𝛼 = 0.05**,𝛼 = 0.1*▪ Main effects analysis
• Effects for Lead Time : Process Complexity < Shape Complexity < Volume Complexity < Machine Complexity
• Effects for Energy Cost : Process Complexity < Shape Complexity < Volume Complexity < Machine Complexity
• Effects for Material Cost : Process Complexity < Machine Complexity < Shape Complexity < Volume Complexity
• Effects for Total Cost : Process Complexity < Machine Complexity < Shape Complexity < Volume Complexity
Lead Time Energy Cost Material Cost Total Cost
1. Regression Equation 𝑦 = 4.13 + 0.0533𝑥1 + 0.470𝑥2 − 0.0492𝑥3 + 0.787𝑥4 𝑦 = −15.2 + 0.841𝑥1 + 7.52𝑥2 − 0.86𝑥3 + 12.51𝑥4 𝑦 = 17037 + 246𝑥1 + 2114𝑥2 − 225𝑥3 − 1558𝑥4 𝑦 = 17022 + 247𝑥1 + 2121𝑥2 − 226𝑥3 − 1546𝑥4
2. R-sq 74.49% 72.37% 51.95% 51.99%
3. Test for𝛃 𝟎 𝑡 = 3.02, 𝑝 = 0.009** 𝑡 = −0.66, 𝑝 = 0.517 𝑡 = 2.76, 𝑝 = 0.014** 𝑡 = 2.75, 𝑝 = 0.015**
4. Test for 𝛃 𝟏 𝑡 = 2.22, 𝑝 = 0.042** 𝑡 = 2.09, 𝑝 = 0.054* 𝑡 = 2.28, 𝑝 = 0.038** 𝑡 = 2.28, 𝑝 = 0.038**
4. Test for 𝛃 𝟐 𝑡 = 3.05, 𝑝 = 0.008** 𝑡 = 2.90, 𝑝 = 0.011** 𝑡 = 3.04, 𝑝 = 0.008** 𝑡 = 3.04, 𝑝 = 0.008**
4. Test for 𝛃 𝟑 𝑡 = −0.65, 𝑝 = 0.523 𝑡 = −0.68, 𝑝 = 0.506 𝑡 = −0.67, 𝑝 = 0.516 𝑡 = −0.67, 𝑝 = 0.516
4. Test for 𝛃 𝟒 𝑡 = 2.61 𝑝 = 0.020** 𝑡 = 2.47 𝑝 = 0.026** 𝑡 = −1.15 𝑝 = 0.269 𝑡 = −1.13, 𝑝 = 0.274
▪ Lead Time & Energy Cost
• Lead Time and Energy Cost are manufacturing performance significantly affected by build time
• The higher machine complexity, the less the amount of filament extruded per unit time
➢ It increases build time (Influenced by build time)
➢ High influence on Lead Time & Energy Cost
▪ Material Cost
• Although machine efficiency is high, machine needs same amount of material
➢ Machine complexity does not affect material cost that much

14. Conclusion & Discussion006
▪ Impact of Shape ∙ Volume Complexity (Design Complexity)
• When the design factor is complicated, such as a curved line or a hole in the design of the product, FFF method generates support
unlike other additive manufacturing techniques such as SLA and EBM (Low design freedom)
• Shape ·volume complexity can be expressed as an element of design, and the occurrence of support negatively impacts on
manufacturing performance
➢ In additive manufacturing using FFF, the complex design impacts negatively on manufacturing performance
▪ Impact of Process ∙ Machine Complexity (Manufacturing Complexity)
• In additive manufacturing, the process of nozzle reflects the complex manufacturing of traditional manufacturing system and
nozzle of 3D printer is moved by G-code command
• Since the number of G- Code directly impacts on the manufacturing performance rather than the commonality of nozzle
movements according to G-Code, it was found that process complexity has relatively little effect
• The higher machine complexity is the less the amount of filament extruded per unit time and leads to increase build time which is a
high influence on lead time and energy cost
➢ In additive manufacturing, the negative impacts of complex manufacturing on manufacturing performance slightly increase
SLA∙EBM
FFF
FFF advantage
SLA∙EBM advantage
Manufacturing
Complexity
Design
Complexity

15. Conclusion & Discussion006
▪ Apply to Product Design & Development Phase for Additive Manufacturing
• When designing a product in additive manufacturing, we provide guidelines to help building the product within
acceptable operating costs by controlling complexity
• If the product can be designed freely, re-design can reduce the complexity of the product
• If re-design is not possible because the required product design is fixed, complexity can be reduced by adjusting
parameter settings
▪ Future research direction
• It is necessary to define more complex factors that may occur in additive manufacturing and to increase the reliability
of the analysis results through additional data collection
• In addition, the analysis results will show the efficiency of 3d printing compared to existing traditional manufacturing
systems