Engineering Design and Material Selection Lecture 11 PDF

Engineering Design and Material Selection Lecture 11 — Material Selection Prof. Dr. Kristina Shea Dr. Tino Stankovic Prof. Kristina Shea 1  Course Schedule Week/ Topic...

Engineering Design and Material Selection Lecture 11 — Material Selection Prof. Dr. Kristina Shea Dr. Tino Stankovic Prof. Kristina Shea 1  Course Schedule Week/ Topic Case study Quiz Lecturer Dates 1 Introduction and Sketching 2 Introducing Engineering Design Health Prof. Dr. Kristina Shea 3 Technical Drawing: Projections and Cuts 4 CAD: Introduction and Modeling Operations 5 CAD: Features and Parametric Modeling Future Mobility 6 CAD: Freeform Modeling Dr. Tino Stankovic 7 CAD: Assemblies and Standard Mechanical Parts X (45 min) 8 Technical Drawing: Dimensioning Health 9 Sustainability in Engineering Design 10 Materials and their Properties 11 Material Selection Sustainable Materials Prof. Dr. Kristina Shea 12 Manufacturing Processes with Focus on Additive Manufacturing 13 Review and Q+A X (75 min) Prof. Kristina Shea Engineering Design + Computing Laboratory 2  Learning Objectives ▪ Learn about factors influencing material selection during the design process ▪ Learn the concept of Ashby charts, how to create them and how to use them for material selection ▪ Learn strategies for material selection and be able to apply them to examples Prof. Kristina Shea Engineering Design + Computing Laboratory 3  Oval Handle Fins Ergonomics: optimum shape for Oxo Goodgrips Ergonomics: allow for comfortable grip hand grip and comfort with thumb and index fingers Peeler Aesthetics: oval shape was a very popular shape at the time; does not Aesthetics: overall shape of the curve echoes the oval shape of the handle. Thin show dirt or oils parallel ribs make handle appear lighter Manufacturing: holding tolerance of fin Manufacturing: shape (and thickness challenges structural integrity of material Santoprene) is easy to Santoprene mold Cross section of handle Material - Santoprene: polymer Countersunk Hole Ergonomics: allows owner to guide that behaves like rubber product onto a holder post (elastomer) but is processed like a thermoplastic, for Aesthetics: the counter sunk hole is more subtle than a hole with example using molding consistent diameter Manufacturing: the hole reduces amount of Santoprene, reducing cost Prof. Kristina Shea Engineering Design + Computing Laboratory 4  Oxo Goodgrips Peeler Material - Santoprene: polymer that behaves like rubber (elastomer) but is processed like a thermoplastic, for example using molding Shape Function Material Process Prof. Kristina Shea Engineering Design + Computing Laboratory 5  Challenge: There are many suitable materials! Source: https://www.jans.com/alpine-ski-anatomy (20.06.2022) Styrofoam Textiles Metamaterial PU Foam There are too many materials to choose from as a novice designer. Wood ➔ How can we make a good choice? PVC Foam ➔ Is there a general strategy? Prof. Kristina Shea Engineering Design + Computing Laboratory 7  When do we select materials? Phase 1: Phase 2: Phase 4: Phase 5: Phase 3: Phase 0: Planning Concept System- Testing & Production Detail Design Development Level Design Refinement Ramp-Up Basic Working Detailed General Layout Principles Structure Verification and Function 1. Choice of material family 3. Choice of single material 2. Choice of 4. Test the material material class implementation Prof. Kristina Shea Engineering Design + Computing Laboratory 8  When do we select materials? Phase 1: Phase 2: Phase 4: Phase 5: Phase 3: Phase 0: Planning Concept System- Testing & Production Detail Design Development Level Design Refinement Ramp-Up Basic Working Detailed General Layout Principles Structure Verification and Function Material selection is also a “design problem” within the product development process. ➔ It is an iterative, but convergent process ➔ Pay attention to your biases ➔ Always consider the previous choices. If something in the current step does not work, is there an alternative? ➔ Always check if the current choices are still in the scope defined in the design requirements Prof. Kristina Shea Engineering Design + Computing Laboratory 9  Material Selection Strategies – General Approach Translation Screening Ranking Validation Reinterpret the design Derive attribute limits Order the viable Seek documentation for requirements in terms from the constraints and candidates by a criterion the top-ranked of function, applying these to isolate that maximizes or candidates, exploring constraints, a subset of viable minimizes some their established uses objectives, and free materials. measure of and behavior in relevant variables. performance. environments until a detailed picture is built up to make a final choice. Adapted from: Ashby, 2011 Prof. Kristina Shea Engineering Design + Computing Laboratory 10  Material Selection Strategies – General Approach Function Constraints ▪ Support Load ▪ Fixed Dimensions Translation Design ▪ Transmit Heat ▪ Temperature Resistance Requirements ▪ … ▪ … Reinterpret the design requirements in terms Objectives Free Variables of function, Requirements on the final product, constraints, also called product specifications, ▪ Low Cost ▪ Non-fixed Dimensions objectives, and free that are precise, measurable ▪ Lightweight ▪ Material variables. quantities of “what” the product ▪ … ▪ … will do. High Level (Abstract) Low Level (Definite) Prof. Kristina Shea Engineering Design + Computing Laboratory 11  Material Selection Strategies – General Approach ▪ Consider every possible material as a potential Screening candidate to prevent biased Feasible selection Derive attribute limits Materials from the constraints ▪ Eliminate candidates not and applying these to able to provide the function isolate a subset of based on the constraints feasible materials. defined in the ‘Translation’ ▪ Always consider “availability” when screening the material Prof. Kristina Shea Engineering Design + Computing Laboratory 12  Material Selection Strategies – General Approach Strategy ▪ All remaining data after screening is valid Objectives (Weights, (Variables to ▪ In the ranking, data is ordered (rather than yes dominant optimize) or no classification). Ranking objective, …) ▪ Need quantifiable data that measures how well a candidate that has passed the screening step Order the viable Ranking can perform: a performance index candidates by a (of all candidates criterion that ▪ If multiple objectives are considered, weights after screening) maximizes or can be assigned to the objectives to quantify minimizes some their overall importance measure of performance. Performance Indices Selection (keep most ▪ Single properties (density ρ, strength σf, … or a combination of them) performant materials) ▪ Allow ranking of the property of interest Prof. Kristina Shea Engineering Design + Computing Laboratory 13  Material Selection Strategies – General Approach ▪ Check feasibility and unknowns of The top-ranking candidate is not always selected materials through, for suitable for the target use case. example, an internet search, e.g. Validation suppliers Seek documentation for ▪ Compare different use-cases the top-ranked candidates, exploring ▪ Check history of the materials, for their established uses Price increase expected example in terms of its failure and behavior in relevant (Shortage, Demand, …) history environments until a detailed picture is built Image in society ➔ Make a final decision based to make a final choice. (Safety, Sustainability, on your research etc.) Image: https://innovation.engie.com (30.11.2022) Conformal to habits (Color, Haptics, …) Prof. Kristina Shea Engineering Design + Computing Laboratory 14  How can we search such a vast space of materials? Computer-Aided Material Selection ▪ Computer-aided material selection using software. The schematic shows the three types of selection window. They can be used in any order and any combination. The selection engine isolates the subset of materials that passes all the selection stages. All materials Selected materials Prof. Kristina Shea Engineering Design + Computing Laboratory 15  One-Dimensional Material Selection Screening Ranking Approach: 1. Sort all materials in a database according to the required property 2. Limit the materials to those fulfilling the requirements, e.g. Young’s modulus  1 GPa Young’s modulus  1 GPa Notes: ▪ Materials don’t just have a fixed value, but ranges ▪ The ranges can depend on many things, such as “grades” and processes used. ▪ One way of presenting such data is Ashby, 2011 grouped bar charts (left) Prof. Kristina Shea Engineering Design + Computing Laboratory 16  Two-Dimensional Material Selection – Ashby Charts Screening Density ≤ 2000 kg/m3 Approach: Feasible Materials 1. Display materials in a 2D plot. 2. The feasible materials are those fulfilling the given requirements for either or both properties, or fulfilling a Young’s Modulus ≥ 10 GPa given relation between the two. Notes: ▪ One property is plotted against another property using log scales, allowing display of more information in the same space. ▪ Each material class occupies a characteristic field ▪ Limiting two properties at once limits the material selection significantly Prof. Kristina Shea Engineering Design + Computing Laboratory 17  Two-Dimensional Material Selection – Ashby Charts Screening Approach: Density ≤ 2000 kg/m3 1. Display materials in a 2D plot. 2. The feasible materials are those fulfilling the given requirements… …for either or both properties …for a given relation between the two. Notes: Water usage per kg of material ≤ 1000 L/kg ▪ One property is plotted against another property using log scales, allowing display of more information in the same space. ▪ Each material class occupies a Feasible Materials characteristic field ▪ Limiting two properties at once limits the material selection significantly Prof. Kristina Shea Engineering Design + Computing Laboratory 18  Two-Dimensional Material Selection – Performance index Ranking Approach: 𝑃2 > 𝑃1 1. Formulate the performance index as 𝑦 𝑃1 Best materials 2 material 𝑦 𝟏/𝜶 𝑃= properties 𝑥 1. Display guidelines on the Ashby chart (log scales) 2. Rank materials based on the performance index Notes: ▪ The goal is to maximize the Worst materials performance index. ▪ If the goal is to minimize a quantity, 𝑥 e.g. the mass 𝑚, take: 1 𝑃∝ Guidelines - lines with constant 𝑃: 𝑦 = 𝑥 𝛼 ⋅ 𝑃𝑖𝛼 𝑚 Prof. Kristina Shea Engineering Design + Computing Laboratory 19  Two-Dimensional Material Selection – Log scales? Linear scale Logarithmic scale 𝑦 Y = log 𝑦 Best materials Slope 𝛼 𝑥 X = log 𝑥 𝑦 = 𝑥 𝛼 ⋅ 𝑃𝛼 log 𝑦 = 𝛼 ⋅ log 𝑥 + 𝛼 log 𝑃 Benefits of log-log scales: Exponent 𝑌 =𝛼⋅ 𝑋 + 𝛽 ▪ Wider range of magnitudes within a single chart (e.g., 10-4 to 104) Higher performance index = ▪ Power functions displayed as lines. guideline to the top left Prof. Kristina Shea Engineering Design + Computing Laboratory 20  Example: Minimum Mass Design – Stiffness – Beam in bending Problem statement: How to select the material of a beam with square cross section in three-point bending with prescribed stiffness, to minimize the mass? 𝛿 = 𝛿𝑡 (target) Relevant equations: Identification of the parameters 𝐹𝐿3 Objective to minimize: Mass 𝑚 ▪ Deflection: (1) 𝛿= 48𝐸𝐼 Type Category Symbol Name 𝑤ℎ3 𝑎 4 ▪ 2nd moment of area: (2) 𝐼= = Dimension 𝐿 Length 12 12 Fixed Load 𝐹 Force ▪ Mass: (3) 𝑚 = 𝜌𝑉 = 𝜌𝐿𝑎 2 Deformation 𝛿𝑡 Deflection Dimension 𝑎 Side length Eliminate Material properties Free Material 𝜌 Density Reformulate the mass: 𝑚 = 𝐶 ⋅ 𝑓(𝐸, 𝜌) properties 𝐸 Youngs modulus To minimize: Fixed Prof. Kristina Shea Engineering Design + Computing Laboratory 21  Example: Minimum Mass Design – Stiffness – Beam in bending Problem statement: How to select the material of a beam with square cross section in three-point bending with prescribed stiffness, to minimize the mass? Relevant equations: Eliminate the free dimension 𝐹𝐿3 𝐹𝐿3 𝐹𝐿3 ▪ Deflection: (1) 𝛿= 48𝐸𝐼 𝛿𝑡 = 𝛿 = = ▪ (2) → (1): 𝑎 4 4𝐸𝑎 4 𝑤ℎ3 𝑎 4 48𝐸 12 ▪ 2nd moment of area: (2) 𝐼= = 12 12 1/4 𝐹𝐿3 ▪ Mass: (3) 𝑚 = 𝜌𝑉 = 𝜌𝐿𝑎 2 ▪ Free variable to 𝑎= 4𝐸𝛿𝑡 eliminate: 3 1/2 5 1/2 ➔ Performance 1 𝐸 𝟏/𝟐 𝐹𝐿 𝐹𝐿 𝜌 𝑃∝ 𝑃= , 𝛼=2 ▪ Eliminate 𝑎 in (3): 𝑚 = 𝜌𝐿 4𝐸𝛿𝑡 = 4𝛿𝑡 ⋅ 𝐸1/2 index 𝑚 𝜌 Prof. Kristina Shea Engineering Design + Computing Laboratory 22  Two-Dimensional Material Selection – Ashby Charts Best materials: All materials on the guideline have the same mass, but this inevitably changes the beam geometry! CFRP composite 𝜌 𝐸 𝟏/𝟐 𝑚 = 𝑓𝑖𝑥𝑒𝑑 ⋅ , 𝑃= , 𝛼=2 Wood 𝐸 𝜌 ▪ The beam’s side length 𝑎 is a free variable. It was eliminated in the formula. ▪ For a given length, deflection and force, the side length changes for each material 𝑚 𝑚= 𝜌𝐿𝑎2 ➔ 𝑎= α = 2 units 𝜌𝐿 → x100 ➔ A wider beam is required for lower densities 1 unit CFRP Wood → x10 composite Prof. Kristina Shea Engineering Design + Computing Laboratory 23  Beyond 2D material selection? It is often not enough to only consider two parameters for material selection. Higher dimensions are harder to plot. Computers and material data banks can be used to find suitable materials. Cut Off’s Material 1 Parameter Relations Material 2 Parameters to optimize Material 3 … …. Greer, J. R., & Deshpande, V. S. (2019). Three-dimensional architected materials and structures: Design, fabrication, and mechanical behavior. MRS Bulletin, 44(10), 750-757. Prof. Kristina Shea Engineering Design + Computing Laboratory 24  Case Study: Ski Core Which material should be Requirements chosen for a ski core? ▪ The ski core must have a bending stiffness that is tuned for the skier without changing the overall geometry. ▪ The goal is to have a ski core that is light, inexpensive and has a low CO2 footprint. L = 1600mm W H W = 110mm H = 7mm Prof. Kristina Shea Engineering Design + Computing Laboratory 25  Translation Step 1 - Translation Requirements Function Constraints ▪ The ski core must have a bending stiffness ▪ Low bending stiffness ▪ 𝜀𝑒𝑙 ≥ 0.2% tuned to the skier.  Controlled bending stiffness = 𝐸𝐼 ▪ Shape according to ski ▪ 𝐸 ≤ 5 GPa ▪ The dimensions 𝐿, 𝐻 and 𝑊 are given geometry ➔ 𝐼 is fixed, such that ➔ 𝐸 must not be too high Objectives Free Variables ▪ The core must accept a certain bending.  The yield strain (elastic limit) 𝜀𝑒𝑙 must be ▪ Lightweight 𝑚 ▪ Choice of material sufficiently high. ▪ Low Price 𝑃 ▪ Low CO2 footprint 𝐶𝑂2 ▪ The goal is to have a ski core that is light, inexpensive and has a low CO2 footprint. Prof. Kristina Shea Engineering Design + Computing Laboratory 26  Screening Step 2 - Screening Feasible Materials a) Yield strain 𝜀𝑒𝑙 ≥ 0.2% (0.002) b) Young’s modulus 𝐸 ≤ 5 GPa Feasible Materials ▪ Foams (Majority) ▪ Elastomers (All) ▪ Polymers (All) ▪ Natural Materials (Part) ▪ Plywood ▪ Cork ▪ Leather a) ▪ Paper/Cardboard b) Prof. Kristina Shea Engineering Design + Computing Laboratory 27  Ranking Step 3 - Ranking Mean Parameters Per Volume Material Density 𝝆 Price 𝑷𝒎 CO2 Footprint Price𝑷𝒗 CO2 Footprint Look up the data for the [kg/m3] [CHF/kg] 𝑪𝑶𝟐 𝒎 [kg/kg]* [CHF/m3] 𝑪𝑶𝟐 𝒗 [kg/m3]* remaining material. Pay Cork 200 7.33 0.8 1466 160 attention to reformulate the Leather 930 17.05 4.29 15857 3990 parameters whenever Plywood 750 0.53 0.65 398 488 needed. Paper/Cardboard 925 1.01 1.2 934 1110 Rigid Polymer Foam (MD) 123 13.65 5.15 1679 633 Comparing (per volume): Flexible Polymer Foam (MD) 93 2.41 3.17 224 295 Silicone Elastomers 1120 3.615 6.52 4049 7302 𝑃𝑣 = 𝑃𝑚 ⋅ 𝜌 Polyurethane 1200 1.25 3.21 1500 3852 𝐶𝑂2 𝑣 = 𝐶𝑂2 𝑚 ⋅ 𝜌 Natural Rubber 950 2.16 2.45 2052 2328 Butyl Rubber 930 1.70 4.45 1581 4139 25606 34937 Teflon 2170 11.80 16.1./m3./kg kg/m3 Polypropylene 902 1.20 2.87 1082 2589 ABS 1045 1.90 3.59 1986 3752 Nylon 1135 4.95 7.05 5618 8002 *Primary Production Data from Ansys Granta EduPack 2021 Prof. Kristina Shea Engineering Design + Computing Laboratory 28  Ranking Step 3 - Ranking Per Volume Material Density 𝝆 Price 𝑷𝒗 CO2 Footprint Objective Rank The ranking is performed by [kg/m3] [CHF/m3] 𝑪𝑶𝟐 𝒗 [kg/m3]* Function (𝒇) weighting the parameters Cork 200 1466 160 1682 3 according to: Leather 930 15857 3990 17185 13 A pre-defined importance Plywood 750 398 488 1196 2 Their relative range Paper/Cardboard 925 934 1110 1970 5 Rigid Polymer Foam (MD) 123 1679 633 1865 4 The objective function is: Flexible Polymer Foam (MD) 93 224 295 347 1 Silicone Elastomers 1120 4049 7302 5899 11 𝑓 = 1𝜌 + 1𝑃𝑣 + 0.1𝐶𝑂2𝑣 Polyurethane 1200 1500 3852 3085 8 Natural Rubber 950 2052 2328 3235 9 Butyl Rubber 930 1581 4139 2925 7 Teflon 2170 25606 34937 31270 14 Polypropylene 902 1082 2589 2243 6 ABS 1045 1986 3752 3406 10 Nylon 1135 5618 8002 7553 12 *Primary Production ➔ Seek documentation for top three candidates Data from Ansys Granta EduPack 2021 Prof. Kristina Shea Engineering Design + Computing Laboratory 29  Validation Step 4 - Validation Cork Soft Polymer Foam Plywood ▪ Main production in ▪ Synthetic Oil-based ▪ Locally produced (CH) Spain/Portugal product ▪ Renewable ▪ Renewable resource ▪ Oil is increasing in price ▪ Wood imperfections ▪ Biodegradable ▪ Not renewable mitigated by gluing ▪ Good reputation ▪ Production time very fast ▪ Good reputation ▪ Damping behavior (high throughput) ▪ Large sheets rarer ▪ Any shape possible Prof. Kristina Shea Engineering Design + Computing Laboratory 30  Question: Which material would you choose for the ski core? ▪ A. Cork ▪ B. Soft Polymer Foam ▪ C. Plywood Prof. Kristina Shea Engineering Design + Computing Laboratory 31  Material Selection – Wrap Up Material selection considers many aspects, such as the desired function, manufacturability, and sustainability. Generalized material selection processes reduce the number of suitable material by screening and ranking. Validation of the material choice is crucial to consider all relevant aspects. Ashby charts are a useful tool for material selection and evaluating material trends. Sustainability indices should be considered as one of the requirements and performance indices. Prof. Kristina Shea Engineering Design + Computing Laboratory 33  Reference For Block 3 the following book may be consulted for further information and explanation. It is free to download within the ETH domain. M.F. Ashby, Materials Selection in Mechanical Design, Elsevier, Fourth Edition, 2011. https://www.sciencedirect.com/book/9781856176637/materials-selection-in-mechanical-design Supplementary reading for Lecture 11: Chapter 5 Prof. Kristina Shea Engineering Design + Computing Laboratory 34  Exercise 11 Can you select the best materials for the following objects? Image Source: Granta Introductory Case Study LONGBOARD Prof. Kristina Shea Engineering Design + Computing Laboratory 35