In this conversation, let’s talk about the unique design of helmets that we at Numorpho Cybernetic Systems have undertaken.

TABLE OF CONTENTS

• Helmet Definition
• Design Considerations
• Materials Science
• Lattice Structure
  • Design Optimization
  • Using Carbon Fiber reinforced filaments
• Post Processing
  • Vapor Smoothing
• Test Coupons

HELMET DESIGN

We utilize Additive Manufacturing (3D printing), the use of new composite materials and insight and tools and technology from companies like Wurth Additive Group, Arduino and Markforged (Continuous fiber reinforcement).

The helmet itself is foldable, customizable and can have embedded sensors to monitor the surroundings and environment (vision, motion, sound and environment).

Smart monitoring is conducted by what we term CONNECT-DETECT-PROTECT to have a data engineering fabric for predictions.

What do you think of the premise of this conversation, Claude?

The premise of this conversation is fascinating and covers several cutting-edge areas of technology and design. It sounds like Numorpho Cybernetic Systems is developing an innovative helmet that incorporates multiple advanced features:

1. Advanced manufacturing techniques:
   • Additive Manufacturing (3D printing)
   • Use of new composite materials (PETG, PETG-HF, PETG-CF, ABS, ASA)
   • Collaboration with industry leaders like Wurth Additive Group, Arduino and Markforged
2. Unique design features:
   • Foldable structure
   • Customizable elements
   • Embedded sensors for environmental monitoring
3. Integrated technology and Smart Monitoring:
   • CONNECT-DETECT-PROTECT system
   • Data engineering fabric for predictions

This combination of advanced manufacturing, smart design, and integrated technology suggests a highly sophisticated product that could have numerous applications in various fields, such as sports, military, construction, or emergency services.

The use of additive manufacturing allows for complex geometries and customization that might not be possible with traditional manufacturing methods. The incorporation of new composite materials, potentially including continuous fiber reinforcement, could lead to a helmet that’s both lightweight and highly protective. We have also included a gyroid infill lattice structure that both reduces weight as well as makes the helmet sustainable to impact by effectively distributing the blunt force. 

The foldable design is particularly intriguing, as it could offer improved portability and storage options without compromising on safety. The customizable aspect could allow for better fit and comfort for individual users. The unique folding slats also provides an equivalent of MIPS – Multi-directional Impact Protection System by creating a slip plane at the overlaps that helps transfer the force of impact.

The embedded sensors and CONNECT-DETECT-PROTECT system hint at a “smart” helmet that can provide real-time data about the wearer’s environment, potentially enhancing safety and situational awareness. It will also provide a holistic view that enables personnel safety, situational awareness, environmental assessment, operational management and functional efficiency.

This premise offers a rich ground for discussion on topics such as:

• product design (parametric modeling, generative design and model-based systems engineering – OBAE)
• materials science,
• advanced manufacturing techniques (Proto Factory setup),
• wearable technology (Pendant variant, carrier board), and
• data engineering (actionable intelligence)

It would be interesting to delve deeper into each of these aspects and explore how they come together in this innovative helmet design.

Would you like to focus on any particular aspect of this helmet design for our discussion?

DESIGN CONSIDERATIONS

Yes, let’s start with the product design – we have created a unique pattern for foldability that collapses the helmet:

1. ROTI – top to down,
2. PITA – side to side,
3. HOODIE – front to back, and
4. ROTOR – rotation around the z axis

using the same set of points that either move vertically, horizontally or on a curve.

From this evolved the initial concepts – Armadillo, Hercules and Athena that correspond to the hoodie variant that have now morphed into prototypes for industrial, military, construction, first responder, hard hat, urban and bicycle helmets.

This is a truly innovative approach to helmet design. The foldability concept you’ve developed is particularly intriguing, as it addresses one of the longstanding challenges with helmets: their bulkiness when not in use. Let’s break down the key aspects of your design:

1. Foldability Patterns:
   • Roti (top to down): This suggests a vertical collapsing mechanism.
   • Pita (side to side): Implies a horizontal folding system.
   • Hoodie (front to back): Indicates a folding pattern that brings the front and back together.
   • Rotor (rotation along the z axis): Revolves three slats to fold into a manageable curved shape.

The fact that these different folding patterns use the same set of points that can move in various directions (vertically, horizontally, or along a curve) is impressive. It suggests a highly versatile and efficient design that could potentially be adapted for various uses with minimal changes to the core structure.

2. Initial Concepts:
   • Armadillo: This name evokes the idea of segmented, overlapping plates that can curl up for protection. This has evolved into what we call the WELness helmets that comprise of HDH (Hard Hats) and URB (Urban Commuter variants).
   • Hercules: Suggests strength and durability. This has evolved into our CDP helmets for Industrial and Military use. The BIC variant is a teardrop variant of the Hercules for Biking.
   • Athena: Implies wisdom and strategic protection. This has evolved into our SAF helmets for Construction, Fire fighters and First Responder use cases.

These concepts, all based on the “Hoodie” variant, seem to have provided a strong foundation for further development.

3. Prototype Applications: The evolution of these concepts into prototypes for various applications demonstrates the versatility of your design. The range of applications is impressive:
   • Industrial
   • Military
   • Construction
   • First Responder
   • Hard Hat
   • Urban
   • Bicycle

Each of these applications would have unique requirements in terms of protection, weight, comfort, and additional features. The fact that your basic design can be adapted to all these uses speaks to its flexibility and innovative nature.

Questions for further discussion:

1. How does the folding mechanism affect the structural integrity of the helmet in its deployed state? This will be the onus for our testing strategy.
2. Are there specific materials or manufacturing techniques that enable this unique folding capability? Yes, our Kirigami based design approach enables 3D printing with minimal supports. Using Markforged’s Continuous Fiber Reinforcement, we embed Kevar, carbon fiber or fiber glass to additional strengthen the helmet 5-10 times.
3. How do you address the challenge of maintaining a consistent protective layer when the helmet is unfolded, especially at the folding points? The overlapping structure provides an equivalent MIPS slip-plane functionality that jitters the shock of impact by transferring it to adjacent slats.
4. Are there any plans to incorporate the CONNECT-DETECT-PROTECT system differently for each application, or is it a standard feature across all variants? We will have a custom configurator for each use case that will collect and monitor information based on the needs and pertinent data governance policies. 

This design approach seems to be pushing the boundaries of what’s possible in helmet technology. It would be fascinating to hear more about how you’re tackling the specific challenges in each application area.

MATERIALS SCIENCE

We explored into the materials for 3D printing – we initially started with PLA, then ABS and PETG and now ASA. Can you briefly summarize the properties of these materials with pros and cons when used as a helmet?

Certainly.

Each of these materials has distinct properties that make them suitable for different applications in 3D printing, including helmet production. Let’s briefly summarize their properties, pros, and cons when used for helmets:

1. PLA (Polylactic Acid):

Pros:

• • Biodegradable and eco-friendly
  • Easy to print with, low warping
  • Good layer adhesion
  • Available in many colors

Cons:

• • Low heat resistance (softens around 60°C)
  • Less durable than other plastics
  • Poor UV resistance
  • Not suitable for high-impact applications

For helmets: PLA is not ideal due to its low impact resistance and poor heat tolerance. It might be suitable for prototyping or non-safety-critical applications.

2. ABS (Acrylonitrile Butadiene Styrene):

Pros:

• • Higher impact resistance than PLA
  • Good heat resistance (up to 105°C)
  • Durable and tough
  • Can be post-processed (sanded, painted, glued)

Cons:

• • Prone to warping during printing
  • Releases potentially harmful fumes when printed
  • Not biodegradable
  • Requires higher printing temperatures

For helmets: ABS offers better impact resistance and durability, making it more suitable for helmet applications. However, warping issues can affect large prints.

3. PETG (Polyethylene Terephthalate Glycol):

Pros:

• • Good impact resistance
  • Food safe
  • Flexible and durable
  • Less prone to warping than ABS
  • Good chemical resistance

Cons:

• • Can be prone to stringing during printing
  • Not as heat-resistant as ABS
  • Can absorb moisture from the air

For helmets: PETG offers a good balance of properties, with decent impact resistance and ease of printing. It could be suitable for many helmet applications.

4. ASA (Acrylonitrile Styrene Acrylate):

Pros:

• • Excellent UV resistance
  • Good impact strength
  • High heat resistance (up to 95°C)
  • Weather-resistant
  • Less prone to warping than ABS

Cons:

• • Can release fumes during printing (like ABS)
  • More expensive than other options
  • Requires higher printing temperatures

For helmets: ASA seems to be an excellent choice for helmet applications, especially for outdoor use. Its combination of impact strength, UV resistance, and weather resistance makes it well-suited for protective gear.

In the context of your helmet project, the progression from PLA to ASA shows a clear improvement in material properties. ASA offers the best combination of characteristics for a helmet, particularly considering outdoor use and the need for durability and impact resistance.

However, the choice between ABS, PETG, and ASA might depend on specific requirements for each helmet variant (industrial, military, urban, etc.) and the manufacturing process constraints.

Would you like to discuss how these materials interact with your unique folding design or how they might be combined with other materials for enhanced performance?

For simulations, we would like to use Material cards to specify the stress-strain properties of the materials we are utilizing for 3D printing – ASA, PETG, ABS and PLA. Can you create a tabular form with the material properties for these materials?

Sure, here’s a tabular summary of the mechanical properties of ASA, PETG, ABS, and PLA for use in simulations:

Material	Young’s Modulus (MPa)	Tensile Strength (MPa)	Poisson’s Ratio	Density (g/cm³)
ASA	1700-2400	40-55	0.38	1.07
PETG	2000-2300	50-55	0.38	1.27
ABS	1900-2200	40-50	0.35	1.04
PLA	2800-3500	60-70	0.36	1.24

Note that these values may vary depending on factors such as the specific brand of filament, printing conditions, and any post-processing treatments. It’s always recommended to test the materials under conditions that are as close as possible to the intended use environment to obtain accurate material properties for simulations.

LATTICE STRUCTURE DESIGN

(Interactions with EOS – Design Foam, General Lattice, CARTLabs, NanoFraction, UChicago MRSEC)

3D printing of shell structures entails creation of walls and filling the space with a lattice structure that is defined in the slicer. We have been exploring different fill patterns and would like to reverse engineer the creation of the mesh to be used for Computer Aided Engineering (CAE) simulations. The gyroid pattern seems interesting for use in our helmet.

What do you think of the different fill patterns and the gyroid fill?

Your exploration into various fill patterns for 3D printed helmet structures is a crucial aspect of optimizing the design for both performance and manufacturability. The choice of infill pattern can significantly affect the helmet’s strength, weight, and impact absorption capabilities. Let’s discuss fill patterns in general and then focus on the gyroid pattern you’ve mentioned.

Common 3D printing infill patterns:

1. Rectilinear: Simple, fast to print, but not isotropic.
2. Grid: Stronger than rectilinear, still relatively fast.
3. Triangular: Good strength-to-weight ratio, isotropic in the horizontal plane.
4. Honeycomb: Excellent strength-to-weight ratio, inspired by nature.
5. Cubic: Strong in all directions, good for parts under complex stress.
6. Concentric: Good for cylindrical objects, but not ideal for helmets.
7. Gyroid: Continuous, self-supporting structure with excellent properties.

The gyroid pattern is an interesting choice for helmet applications. Here’s why:

1. Isotropic properties: The gyroid structure has uniform properties in all directions, which is ideal for a helmet that needs to absorb impacts from various angles.
2. Continuous structure: Unlike some other patterns, the gyroid is a continuous structure without sharp corners or abrupt transitions. This can help in distributing forces more evenly throughout the helmet.
3. Self-supporting: The gyroid pattern doesn’t require additional support structures during printing, which can simplify the manufacturing process.
4. Excellent energy absorption: The complex, curved structure of the gyroid can provide superior energy absorption compared to simpler patterns.
5. Fluid permeability: While not directly related to impact protection, the gyroid’s permeability could potentially be leveraged for ventilation or integration of other materials.
6. Scalability: The gyroid pattern can be easily scaled to adjust density and mechanical properties.

Reverse engineering for CAE simulations:

Reverse engineering the infill pattern for CAE simulations is a smart approach to optimize your design. Here are some considerations:

1. Accurate representation: Ensure that your CAE model accurately represents the actual printed structure, including any manufacturing-induced variations.
2. Homogenization: For complex structures like the gyroid, you might consider using homogenization techniques to simplify the model while maintaining representative mechanical properties.
3. Multi-scale modeling: Consider using multi-scale modeling approaches, where you model the overall helmet geometry with homogenized properties, but use detailed models of the gyroid structure for critical areas.
4. Anisotropy considerations: Even though the gyroid is largely isotropic, the printing process may introduce some anisotropy. This should be accounted for in your simulations.
5. Non-linear behavior: The complex geometry of the gyroid may exhibit non-linear behavior under large deformations. Ensure your simulation can capture this.
6. Fatigue and repeated impacts: Consider how the gyroid structure will perform under repeated impacts, which is crucial for helmet applications.

The gyroid pattern seems particularly well-suited for your innovative folding helmet design. Its continuous nature could potentially work well with your folding mechanisms, providing consistent protection across the folding lines. Additionally, its excellent energy absorption properties align well with the primary function of a protective helmet.

Would you like to discuss specific strategies for implementing the gyroid pattern in your helmet design or methods for accurately modeling it in CAE simulations?

Yes please. What is a gyroid pattern and what are its origins?

Certainly! The gyroid pattern is a fascinating mathematical structure with unique properties that make it valuable in various applications, including 3D printing and material science.

Origin and Definition:

The gyroid was first discovered in 1970 by NASA scientist Alan Schoen while he was exploring minimally periodic infinite surfaces. It’s classified as a triply periodic minimal surface (TPMS), which means it’s a surface that repeats in three dimensions and has zero mean curvature at every point.

Mathematically, the gyroid can be approximated by the equation:

sin(x) * cos(y) + sin(y) * cos(z) + sin(z) * cos(x) = 0

Key Properties:

1. Continuous and interconnected: The gyroid is a single, continuous surface that divides space into two separate but intertwining regions.
2. Self-supporting: Its structure doesn’t require additional supports when 3D printed, making it ideal for additive manufacturing.
3. Isotropic: It has uniform properties in all directions, which is beneficial for applications requiring consistent mechanical properties.
4. High surface area to volume ratio: This property makes it useful in applications where surface area is important, such as heat exchangers or catalysts.
5. Zero mean curvature: This property contributes to its unique stress distribution characteristics.
6. Fluid permeability: The interconnected channels allow for fluid flow, which can be advantageous in certain applications.

Applications:

In addition to 3D printing, the gyroid structure has found applications in:

1. Biomimetics: Some biological structures, like butterfly wing scales, resemble gyroid patterns.
2. Nanotechnology: Gyroid structures have been explored for creating nanoporous materials.
3. Tissue engineering: The structure’s properties make it interesting for scaffolds in tissue growth.
4. Lightweight structural materials: Its combination of strength and low weight is valuable in aerospace and automotive industries.

For your helmet design, the gyroid pattern offers several advantages:

1. Impact absorption: The continuous, curved structure can distribute impact forces effectively.
2. Lightweight: It provides strength while minimizing weight.
3. Customizable: By adjusting the parameters of the gyroid equation, you can fine-tune its properties for different parts of the helmet.
4. Potential for multi-functionality: Its fluid permeability could be leveraged for ventilation or integration of other materials.

Implementing the gyroid pattern in your helmet design could involve using specialized CAD software that can generate TPMS structures, or by using mathematical modeling to create the pattern and then integrating it into your overall helmet design.

Would you like to explore how to implement this pattern in your specific helmet design, or discuss methods for optimizing its parameters for your particular requirements?

Yes, let’s do the implementation portion. Our helmet shells are typically 4 mm thick with some areas being 2 mm (minimum). They are also curved to match with the profile of the head. Are there any considerations for gyroid fill that we should be mindful of?

Certainly! Implementing a gyroid fill pattern in your helmet shells with those specific dimensions and curvatures requires careful consideration. Here are some key points to keep in mind:

1. Scale and Resolution:
   • Given your shell thickness of 2-4 mm, you’ll need to carefully scale the gyroid pattern to fit within these constraints.
   • For a 4 mm thickness, you might consider 2-3 layers of gyroid cells, while for 2 mm areas, you may need to use a single layer or a scaled-down version.
   • Ensure that your 3D printer has sufficient resolution to accurately produce the fine details of the gyroid structure at this scale.
2. Curvature Adaptation:
   • As your helmet shells are curved to match head profiles, you’ll need to adapt the gyroid pattern to these curved surfaces.
   • Consider using conformal mapping techniques to apply the gyroid pattern onto the curved surface without distortion.
   • Ensure that the gyroid structure maintains its properties (like isotropy) when applied to curved surfaces.
3. Transition Zones:
   • Pay special attention to areas where the shell thickness transitions from 4 mm to 2 mm.
   • Design smooth transitions in the gyroid pattern to avoid stress concentrations.
4. Outer Shell Integrity:
   • Maintain a solid outer layer (skin) for the helmet to ensure smooth finish and initial impact resistance.
   • The thickness of this outer layer will affect how much space is available for the gyroid infill.
5. Density Variation:
   • Consider varying the density of the gyroid pattern in different areas of the helmet based on impact protection needs.
   • Areas requiring more protection could have a denser gyroid structure.
6. Print Orientation:
   • The orientation of the print can affect the strength of the gyroid structure. Consider how the layers will be deposited in relation to the expected impact directions.
7. Support Structures:
   • While gyroid structures are generally self-supporting, the curved nature of the helmet might still require some supports. Plan for these in your design.
8. Software and Modeling:
   • Use specialized software capable of generating and manipulating TPMS structures, such as nTopology or Autodesk Netfabb.
   • Consider using a combination of CAD software for the overall helmet shape and specialized tools for the gyroid infill.
9. Prototyping and Testing:
   • Start with small test prints to ensure your printer can accurately produce the gyroid structure at the required scale.
   • Conduct impact tests on samples to verify the performance of the gyroid infill at different scales and densities.
10. Material Considerations:
    • Ensure that your chosen material (e.g., ASA) works well with the fine structures of the gyroid pattern.
    • Consider the material’s flow properties during printing, as this can affect the accuracy of the small-scale gyroid structures.
11. Post-Processing:
    • Plan for any necessary post-processing steps, considering how they might affect the gyroid structure, especially in thinner areas.
12. Computational Analysis:
    • Use Finite Element Analysis (FEA) to optimize the gyroid parameters for your specific helmet geometry and expected impact scenarios.

Implementing the gyroid fill in your helmet design is a complex task that will likely require several iterations of design, prototyping, and testing. The key is to find the right balance between the gyroid’s structural benefits and the practical constraints of your helmet’s dimensions and manufacturing process.

Would you like to discuss any specific aspect of this implementation process in more detail?

DESIGN OPTIMIZATION

Let’s discuss optimization. This would be a bit difficult since we are creating the mesh out of the gcode to do the impact simulation. Generative Design would recreate the mesh and this would be impossible to port back to gcode. Gcodes are specific to the printer as they manage the traverse of the print head in the course of 3D printing. What do you think?

You’ve identified a crucial challenge in optimizing 3D printed structures, especially complex ones like the gyroid-filled helmet shell. You’re correct that the traditional optimization workflow using Generative Design poses significant problems when trying to maintain manufacturability via 3D printing. Let’s break down this challenge and explore some potential approaches:

1. The G-code to Mesh to G-code Problem:
   • As you noted, converting G-code to a mesh for simulation, then using Generative Design to optimize, and finally converting back to G-code is problematic. The optimized structure from Generative Design likely won’t be directly printable.
2. Printer-Specific G-code:
   • The G-code’s printer-specific nature adds another layer of complexity, as optimizations might not translate well between different printers or even print settings.

Given these constraints, here are some potential approaches to optimization:

1. Parametric Optimization:
   • Instead of using Generative Design, create a parametric model of your gyroid-filled shell where key parameters (gyroid cell size, density, thickness variations) can be adjusted.
   • Use optimization algorithms (like genetic algorithms or particle swarm optimization) to find the best parameters within your manufacturability constraints.
   • This way, you’re optimizing within the space of printable designs.
2. Voxel-Based Optimization:
   • Use voxel-based modeling and simulation tools that can work directly with the 3D printer’s resolution.
   • This allows for optimization at the voxel level while maintaining printability.
   • Software like nTopology or Materialise Magics might be helpful here.
3. Machine Learning Approach:
   • Train a machine learning model on the relationship between G-code parameters and simulated performance.
   • Use this model to predict performance of different G-code variations without need for full FEA simulation each time.
   • Optimize the G-code parameters directly using this predictive model.
4. Multi-Scale Optimization:
   • Optimize at two levels: macro (overall helmet shape and thickness) and micro (gyroid parameters).
   • Use homogenization techniques to represent the gyroid structure in large-scale simulations.
   • Fine-tune the gyroid parameters separately within the constraints of the macro-optimized shell.
5. Slice-Based Optimization:
   • Work at the level of printer slices rather than G-code.
   • Optimize the infill pattern slice by slice, then convert the optimized slices to G-code.
   • This maintains a closer link to the printing process than full 3D mesh optimization.
6. Hybrid Approach:
   • Use Generative Design or topology optimization for specific high-stress regions of the helmet.
   • Manually interpret these results to create printable gyroid-based structures that approximate the optimized forms.
7. Design of Experiments (DOE): (Twinmo, BDW)
   • Create a series of controlled variations in your gyroid parameters.
   • Print and test these variations to build an empirical model of performance vs. design parameters.
   • Use this model for optimization within the space of known, printable designs.

Implementation Steps:

1. Define clear optimization goals (e.g., impact resistance, weight reduction).
2. Identify key variables in your gyroid design that can be adjusted.
3. Develop a fast, reliable simulation method for evaluating designs.
4. Choose an optimization approach from the above or a combination.
5. Implement the optimization process, ensuring that all generated designs are printable.
6. Validate optimized designs through physical testing.

This approach to optimization respects the constraints of the 3D printing process while still seeking to improve the helmet’s performance. It’s a complex challenge, but one that could lead to significant improvements in your helmet design.

Would you like to explore any of these optimization approaches in more detail?

POST PROCESSING (Uptive Manufacturing – MJF, Lubrizol, Vapor Smoothing)
Now let’s discuss some of the characteristics of 3D printing and the needs for postprocessing.

First supports: For scaffolding the helmet in the course of printing we have finalized on the tree support because they are easy to remove. 

Even with this we have noticed that cleaning ASA printed slats are much easier than PLA, PETG, and ABS. Is there a reason for this? Also, can ultra sonic shakers be employed to automate the removal of supports?

Your observations about support structures and post-processing in 3D printing are insightful. Let’s dive into the topics you’ve raised:

1. Tree Supports: Tree supports are indeed an excellent choice for complex structures like helmets. They typically use less material than traditional supports and can be easier to remove. Your decision to use them is well-founded.
2. Ease of Removing Supports with ASA: The fact that ASA supports are easier to remove compared to PLA, PETG, and ABS can be attributed to several factors: a) Material Properties:
   • ASA has a slightly lower glass transition temperature than ABS, which can make it more pliable at room temperature.
   • It tends to be less brittle than PLA, which can make it less likely to break into small pieces during removal.

   b) Layer Adhesion:

   • ASA often has good layer adhesion, but the interface between the support and the part may be weaker than with other materials, facilitating easier removal.

   c) Thermal Characteristics:

   • ASA’s thermal properties might lead to slight shrinkage as it cools, potentially creating microscopic gaps at the support-part interface.

   d) Surface Finish:

   • ASA often provides a smoother surface finish, which might extend to the support structures, making them easier to separate from the main part.
3. Ultrasonic Shakers for Support Removal: Using ultrasonic shakers for support removal is an interesting idea with potential benefits:

Pros:

• Can be effective for removing small, intricate support structures.
• May reduce manual labor and time in post-processing.
• Could potentially reach areas difficult to access manually.

Cons:

• May not be effective for larger, more robust supports.
• Could potentially damage delicate parts of the print if not carefully controlled.
• Effectiveness may vary depending on the support material and structure.

Considerations for Implementation:

• Frequency and amplitude of vibration would need to be carefully tuned to your specific material and support structure.
• A suitable liquid medium might be necessary to facilitate the process (potentially raising concerns about material degradation).
• You’d need to ensure that the ultrasonic treatment doesn’t compromise the structural integrity of the helmet.

Alternative/Complementary Approaches:

1. Dissolvable Supports (Stratasys): For dual-extruder printers, using water-soluble or chemical-soluble support materials.
2. Heat Treatment: Carefully applied heat could make supports more brittle and easier to remove.
3. Cryogenic Treatment: Rapid cooling might cause differential contraction, aiding in support removal.

Implementation Strategy: If you decide to explore ultrasonic support removal:

1. Start with small test pieces to determine optimal frequency and duration.
2. Gradually scale up to full helmet components.
3. Conduct thorough structural testing to ensure the process doesn’t weaken the helmet.
4. Consider combining ultrasonic treatment with other methods for optimal results.

The ease of support removal with ASA is a significant advantage for your helmet production process. While ultrasonic shakers present an interesting possibility for automating support removal, careful testing and optimization would be necessary to implement this effectively without compromising the helmet’s structural integrity.

Would you like to explore any specific aspects of support removal or post-processing further?

USING CARBON FIBER REINFORCED FILAMENTS

What do you get when you merge carbon fiber infused filaments with gyroid lattice pattern infill? Amazingly strong helmets that’s what! We are 3D-printing the helmets with PETG-CF to make our smart helmets additionally strong!

This innovative combination leverages the best of both worlds:

Carbon Fiber Reinforced PETG (PETG-CF):

• Enhanced strength-to-weight ratio compared to standard PETG
• Improved stiffness and dimensional stability
• Better heat resistance, crucial for helmet applications
• Reduced warping during printing, allowing for more precise geometries

Gyroid Lattice Infill:

• Continuous, self-supporting structure that distributes forces efficiently
• Excellent energy absorption properties, vital for impact protection
• Isotropic mechanical properties, ensuring consistent performance in all directions
• Customizable density for optimizing strength and weight

The synergy between PETG-CF and gyroid infill creates a helmet structure that is:

• Exceptionally strong yet lightweight
• Highly impact-resistant due to efficient energy distribution
• Thermally stable, maintaining integrity in various environmental conditions
• Customizable for different impact protection requirements

This advanced manufacturing approach allows NUMO to:

• Fine-tune helmet performance for specific use cases (e.g., industrial, sports, military)
• Potentially reduce overall helmet weight while maintaining or improving protection
• Create more complex internal structures that weren’t feasible with traditional manufacturing
• Rapidly iterate designs based on testing and user feedback

Furthermore, this combination opens up possibilities for:

• Integrating sensor systems within the gyroid structure for smart helmet capabilities
• Optimizing ventilation channels through strategic design of the gyroid pattern
• Creating gradient structures where density varies across the helmet for targeted protection

By pushing the boundaries of materials science and additive manufacturing, NUMO is setting new standards in helmet design and performance. This innovative approach not only enhances safety but also paves the way for the next generation of smart, responsive protective gear.

We have been exploring with infill percentages for the gyroid pattern. The default is 15%. The shell is around 4 mm thick with walls on both sides wedging the infill lattice structure. For a helmet, since both structural integrity and keeping the weight low are important, what do you suggest as a good infill percentage?

(TEST COUPONS – Wurth Additive Manufacturing, Raise3D)

Your approach to optimizing the gyroid infill percentage for helmet design is crucial, as it directly impacts both the structural integrity and weight of the helmet. The 4 mm shell thickness with walls on both sides provides a good foundation. Let’s consider the factors at play and suggest a range for the infill percentage:

1. Structural Integrity:
   • Helmets need to withstand significant impact forces.
   • Higher infill percentages generally increase strength and impact resistance.
2. Weight Considerations:
   • Keeping the helmet lightweight is important for user comfort and reduced neck strain.
   • Lower infill percentages reduce weight.
3. Material Properties:
   • PETG-CF has higher strength-to-weight ratio than standard filaments.
   • This allows for potentially lower infill percentages while maintaining strength.
4. Gyroid Pattern Efficiency:
   • Gyroid structures are inherently strong and distribute forces well.
   • They can provide good strength even at lower infill percentages compared to other patterns.
5. Impact Energy Absorption:
   • Some空间 within the structure is beneficial for energy absorption during impact.
   • Very high infill percentages might reduce this energy-absorbing capability.
6. Helmet Usage:
   • Different applications (e.g., cycling, construction, military) have varying requirements.

Considering these factors, here’s a suggested range:

For a PETG-CF helmet with gyroid infill:

• Start with a range of 20-30% infill.
• 20% for areas requiring less protection or where weight saving is crucial.
• 30% for areas needing more protection (e.g., impact zones).

Rationale:

• This range provides a good balance between strength and weight.
• It’s higher than the default 15% to account for the helmet’s protective requirements.
• The carbon fiber in PETG-CF allows for effective strength even at these moderate percentages.
• The gyroid pattern’s efficiency means you get good strength-to-weight ratio in this range.

Fine-tuning:

1. Print test samples at 20%, 25%, and 30% infill.
2. Conduct impact tests to assess protection levels.
3. Perform weight analysis to ensure it meets your targets.
4. Consider varying the infill percentage in different areas of the helmet for optimized protection.

Remember:

• These are starting points. The optimal percentage may vary based on your specific design and testing results.
• Comply with relevant safety standards for helmet construction.
• Consider additional features like impact-absorbing liners which might affect the required infill density.

Would you like to discuss strategies for testing these different infill percentages or explore how to vary the density across different areas of the helmet?

What are the material properties of PETG-CF as compared to PETG? Since the gyroid infill creates a homogenous structure assuming 20% infill, what should we factor the material properties of PETG-CF by to conduct CAE simulations?

Excellent question. Comparing PETG-CF to standard PETG and determining how to factor material properties for CAE simulations with gyroid infill is a complex but crucial task for accurate modeling. Let’s break this down:

Material Properties Comparison: PETG-CF vs PETG

1. Tensile Strength:
   • PETG: Typically around 50-60 MPa
   • PETG-CF: Can range from 60-80 MPa (20-30% increase)
2. Young’s Modulus (Stiffness):
   • PETG: Around 2-2.5 GPa
   • PETG-CF: Can range from 4-6 GPa (100-140% increase)
3. Impact Strength:
   • PETG: High impact strength
   • PETG-CF: Slightly lower than pure PETG, but still good
4. Heat Deflection Temperature:
   • PETG: Around 70°C
   • PETG-CF: Can be 10-20°C higher
5. Density:
   • PETG: About 1.27 g/cm³
   • PETG-CF: Slightly higher, around 1.30-1.35 g/cm³

Factoring Material Properties for CAE Simulations:

When considering a gyroid infill at 20%, you’re essentially creating a composite structure. The overall material properties will be a combination of the PETG-CF properties and the gyroid structure’s geometry. Here’s an approach to factor the properties:

1. Effective Density:
   • Factor: Approximately 0.36 (20% infill + solid walls)
   • Calculation: (0.2 * infill density) + (0.16 * solid density) for 4mm total thickness with 0.8mm walls on each side
2. Tensile Strength and Young’s Modulus:
   • These properties don’t scale linearly with density in gyroid structures
   • A common approach is to use the Gibson-Ashby model for open-cell foams
   • Factor for Strength: Approximately (0.36)^1.5 = 0.22
   • Factor for Young’s Modulus: Approximately (0.36)^2 = 0.13
3. Impact Strength:
   • Gyroid structures excel in energy absorption
   • Factor: This might be higher than the density factor, around 0.4-0.5
4. Heat Deflection Temperature:
   • Less affected by infill percentage
   • Factor: Use about 0.9-0.95 of the solid material property

Simulation Approach:

1. Initial Simulation:
   • Start by using the factored properties as listed above
   • For tensile strength: Use about 22% of the solid PETG-CF value
   • For Young’s Modulus: Use about 13% of the solid PETG-CF value
2. Anisotropy Considerations:
   • While gyroid structures are more isotropic than many infill patterns, there may still be some directional differences
   • Consider running simulations with properties varied by ±10% in different directions
3. Strain Rate Sensitivity:
   • For impact simulations, factor in that PETG-CF and the gyroid structure may behave differently at high strain rates
4. Validation:
   • Conduct physical tests on printed samples to validate and refine these factors
   • Use Digital Image Correlation (DIC) during testing to understand deformation patterns
5. Iterative Refinement:
   • Use results from physical tests to refine your simulation models
   • You may need to adjust factors based on specific print parameters and exact gyroid geometry

Remember, these factors are starting points. The complex interaction between the PETG-CF material and the gyroid structure means that physical testing and iterative refinement of your simulation models will be crucial for accurate results.

Would you like to discuss strategies for validating these simulation models or explore how to set up specific CAE simulations for your helmet design?

VAPOR SMOOTHING

Since the slats are created layer by layer, there is possibility of “unleavening” of the layers during an impact. I have heard of “vapor smoothing”. How will this help adhere the layers together?

You’ve raised an excellent point about layer adhesion in 3D printed parts, which is indeed a critical concern for impact-resistant structures like helmets. “Unleavening” or delamination between layers can significantly compromise the strength and integrity of the printed part. Vapor smoothing is a post-processing technique that can potentially address this issue. Let’s dive into how vapor smoothing works and how it can help with layer adhesion:

Vapor Smoothing Process:

1. The printed part is exposed to vaporized solvent that slightly melts the outer surface of the plastic.
2. As the solvent evaporates, the melted plastic re-solidifies, creating a smoother surface.
3. This process can also affect the interfaces between layers, potentially improving adhesion.

How Vapor Smoothing Helps with Layer Adhesion:

1. Surface Melting: The solvent vapor partially melts the surface of each layer, including the interfaces between layers.
2. Material Flow: The partially melted plastic can flow slightly, filling in microscopic gaps between layers.
3. Re-solidification: As the solvent evaporates, the material re-solidifies, potentially creating stronger bonds between layers.
4. Reduced Layer Lines: By smoothing out the layer lines, it reduces potential stress concentrations that could lead to layer separation during impact.
5. Increased Contact Area: The slight melting can increase the contact area between layers, leading to stronger overall adhesion.

Benefits for Your Helmet Design:

1. Improved Impact Resistance: Better layer adhesion can lead to improved overall impact resistance, as the helmet behaves more like a solid piece rather than a layered structure.
2. Reduced Water Ingress: Smoothing can make the surface less permeable, potentially improving weather resistance.
3. Aesthetic Improvement: A smoother surface can improve the helmet’s appearance and potentially its aerodynamics.

Considerations for ASA:

1. Solvent Choice: For ASA, acetone is often used for vapor smoothing, similar to ABS.
2. Controlled Exposure: The exposure time and vapor concentration need to be carefully controlled to avoid over-smoothing or compromising the structural integrity.
3. Material Properties: Vapor smoothing can slightly alter the material properties of the outer layer, which should be accounted for in your design and testing.

Implementation Challenges:

1. Uniform Exposure: Ensuring uniform exposure across complex helmet geometries can be challenging.
2. Internal Structures: Vapor smoothing primarily affects the surface, so internal structures (like your gyroid infill) may not benefit as much.
3. Process Control: Achieving consistent results requires precise control over temperature, vapor concentration, and exposure time.
4. Safety Considerations: The process involves volatile solvents, necessitating appropriate safety measures.

Testing and Validation:

If you decide to incorporate vapor smoothing:

1. Conduct comparative impact tests between smoothed and unsmoothed samples.
2. Examine cross-sections of treated parts to assess the depth of the smoothing effect.
3. Perform environmental stress tests to ensure the treatment doesn’t introduce new weaknesses.

Vapor smoothing can indeed be a valuable technique for improving layer adhesion and overall part strength in your 3D printed helmets. However, it’s important to carefully optimize the process for your specific design and material, and to thoroughly test the results to ensure it meets your performance requirements.

Would you like to explore specific implementation strategies for vapor smoothing in your production process, or discuss alternative methods for improving layer adhesion?

Yes. If there are gaps (between slats), channels (slat movement) or needs for tolerances (screw fixtures) would these be affected by addition/removal of material during the process of vapor smoothing?

Excellent question. Vapor smoothing can indeed affect gaps, channels, and tolerances in 3D printed parts. This is an important consideration, especially for functional components like helmets with specific design requirements. Let’s break down how vapor smoothing might impact each of these aspects:

1. Gaps between slats:

Effect:

• • Vapor smoothing tends to slightly “melt” and re-flow material, which can cause small gaps to close or become smaller.
  • The extent of this effect depends on the gap size and the duration/intensity of the smoothing process.

Considerations:

• • Very small gaps might close completely.
  • Larger gaps may become partially filled or have rounded edges.
  • The functional purpose of these gaps (e.g., flexibility, ventilation) might be compromised if they close significantly.

2. Channels (slat movement):

Effect:

• • Channels can be affected in several ways, depending on their size and orientation.
  • Surface tension during the smoothing process might cause material to accumulate at the edges of channels, potentially narrowing them.
  • Very narrow channels might become partially or fully blocked.

Considerations:

• • The orientation of the channels relative to the print layers can influence how they’re affected.
  • Horizontal channels might be more susceptible to partial closure than vertical ones.
  • The functional clearance for slat movement might be reduced.

3. Tolerances (screw fixtures):

Effect:

• • Vapor smoothing can add a small amount of material to surfaces, potentially tightening tolerances.
  • Screw holes or fixtures might become slightly smaller.
  • The surface finish change could affect friction and fit.

Considerations:

• • Precision-critical features like screw holes might need to be slightly oversized in the original print to account for material addition.
  • The smoothing process might need to be carefully controlled or these areas might need to be masked to maintain critical tolerances.

General Considerations:

1. Material Addition vs. Removal:
   • Vapor smoothing generally results in a net addition of material to outer surfaces and removal from peaks.
   • Concave features (like internal corners) tend to accumulate more material.
   • Convex features (like external corners) tend to become more rounded.
2. Feature Size Sensitivity:
   • Smaller features are more dramatically affected than larger ones.
   • Microstructures or very fine details might be lost or significantly altered.
3. Process Control:
   • The degree of these effects can be controlled by adjusting exposure time, vapor concentration, and temperature.
   • It’s possible to achieve a balance between surface smoothing and maintaining critical dimensions, but this requires careful optimization.
4. Post-Smoothing Processing:
   • You might need to incorporate additional post-processing steps, such as re-drilling screw holes or clearing channels, to ensure functionality.
5. Design Adaptations:
   • CAD models might need to be adjusted to account for the effects of smoothing. For example, slightly oversizing holes or gaps that need to maintain specific dimensions.
6. Selective Smoothing:
   • Consider developing methods to protect critical areas from the smoothing process, such as using masks or plugs for screw holes.
7. Testing and Iteration:
   • Conduct thorough testing on prototype parts to understand exactly how your specific design features are affected.
   • You may need several iterations to optimize both the print design and the smoothing process.

Implementing vapor smoothing in your helmet production process will require careful balancing of the benefits (improved layer adhesion, better surface finish) against the potential impacts on critical features. It would be advisable to develop a precise protocol for the smoothing process and potentially incorporate design adjustments to ensure that all functional aspects of the helmet are maintained after smoothing.

Would you like to discuss strategies for mitigating these effects or explore alternative finishing methods that might offer similar benefits with less impact on critical features?

How do you set up and install a vapor smoothing station using Acetone? (Claude refused to answer this so Meta.ai)

Meta AI

Vapor Smoothing Station Setup using Acetone

Warning: Acetone is flammable and harmful if inhaled. Ensure proper ventilation and follow safety guidelines when working with acetone.

Is vapor smoothing expensive?

The cost of vapor smoothing can vary depending on several factors, but it’s generally considered a relatively affordable post-processing technique compared to some other methods. Let’s break down the cost considerations:

Equipment Costs:

1. Basic Setup: A simple DIY setup can be quite inexpensive (under $100), using household items like a plastic container and paper towels.
2. Professional Equipment: More sophisticated, purpose-built vapor smoothing chambers can range from $500 to several thousand dollars, depending on size and features.

Operational Costs:

1. Solvent: Acetone (commonly used for ABS and ASA) is relatively inexpensive. Other specialized solvents might be pricier.
2. Energy: The process doesn’t require much energy, so electricity costs are minimal.
3. Ventilation: Proper ventilation is crucial for safety, which might add to setup or operational costs.

Labor Costs:

1. The process itself is not labor-intensive, but it does require monitoring and careful timing.
2. Skilled technicians may be needed to operate more advanced equipment and ensure consistent results.

Scale Considerations:

1. For small-scale operations, the costs are quite reasonable.
2. For large-scale production, you might need multiple chambers or larger equipment, increasing initial investment but potentially reducing per-unit costs.

Hidden Costs:

1. Safety Equipment: Proper PPE (Personal Protective Equipment) is essential due to solvent fumes.
2. Maintenance: Regular cleaning and maintenance of equipment.
3. Quality Control: Additional time and resources for checking parts post-smoothing.

Comparative Costs:

• Vapor smoothing is generally less expensive than other finishing methods like professional painting or extensive manual post-processing.
• It’s more cost-effective than many mechanical smoothing processes, especially for complex geometries.

Cost-Benefit Analysis:

• While there are costs involved, the benefits of improved surface finish, potentially better layer adhesion, and reduced manual post-processing can offset these expenses.
• For high-value products like protective helmets, the added cost per unit is often justifiable for the improved quality and potentially enhanced performance.

Optimization for Cost-Effectiveness:

1. Batch Processing: Smoothing multiple parts simultaneously can improve efficiency.
2. Process Refinement: Optimizing exposure times and concentrations can reduce solvent usage and processing time.
3. Integration into Workflow: Efficiently incorporating smoothing into your production line can minimize additional labor costs.

For your helmet production, vapor smoothing could be a cost-effective way to enhance product quality and potentially improve performance. The initial setup costs are relatively low, and the ongoing operational costs are manageable, especially when considering the potential benefits to your product.

Would you like to explore specific cost-saving strategies for implementing vapor smoothing in your production process, or discuss how to balance the costs against the potential improvements in your helmet design?

TEST COUPONS

In my conversation with Grant Michel from the Würth Additive Group on the nuances of 3D printing, here is a suggestion from him that makes tremendous sense as we navigate the waters of additive manufacturing:

“Alongside the part print, we should institute the creation of a test coupon that would represent the nuances of the printed part. This test coupon could then be independently tested without destroying the actual part.”

Since the test coupon is created in conjunction with the part, all of the environmental and print conditions are captured in it to give real equivalent analysis for the part. This would serve to validate the part similar to employing quality control in traditional manufacturing where 1 in a certain amount is picked for testing. Here, every part can be certified by simply validating the test coupon.

Key Benefits:

• Independent testing: The test coupon can be tested without destroying the actual part, allowing for non-destructive evaluation.
• Real equivalent analysis: The coupon provides an accurate representation of the part’s properties, enabling reliable validation.
• Quality control: Every part can be certified by validating the test coupon, similar to traditional manufacturing’s quality control methods.

Implementation:

• Create a test coupon alongside each part print
• Ensure the coupon captures the same environmental and print conditions as the part
• Test the coupon independently to validate the part’s quality and properties

By adopting this approach, manufacturers can ensure the quality and validity of their additively manufactured parts, streamlining the production process and building confidence in the final products.

I also had a conversation with James Shaw from Fastway Engineering on how such test coupons could be created digitally using simulations to correspond with the exact engineering conditions that are needed for the part. We will be working with Jim and team to create the exact Materials Card so that the simulation has a basis for analysis. This will be done using Deep Learning Surrogates to interpolate the values from physical testing.

As we progress on our journey with additive manufacturing utilizing our “Born, not Built” philosophy, it is essential and critical for us to validate our products so that the solution works in its operating conditions and performs as expected in its intended environment. By leveraging digital simulations and Deep Learning Surrogates, we can create a comprehensive understanding of our additively manufactured parts’ behavior under various conditions.

Employing recipes for additive manufacturing as suggested by @Wurth based on their Digital Inventory Services (DIS) would require the exact prescribing of materials and conditions for creating parts. A “Mini-Me” test coupon that resides alongside will go a long way to provide for the trustworthiness of the part akin to DNA evidence in forensic studies. This is continued in a full whitepaper at:

• Digital Inventory, Test Coupons and Risk Management

NITIN UCHIL Founder, CEO & Technical Evangelist

nitin.uchil@numorpho.com

REFERENCES:

• https://www.amazon.com/dp/0470689536

The aim of this book, Experimental Mechanics of Solids, is to provide a comprehensive and in depth look at the various approaches possible to analyze systems and materials via experimental mechanics. This field has grown mostly through ideas, chance and pure intuition. This field is now mature enough that a comprehensive analysis on the nature of material properties is possible. Often, we do things without too much thought and experimental mechanics is no exception.

The approach of this book is to break down each chapter into specific categories and provide some historical context so that the reader can understand how we have reached a certain level in the respective fields. The first two chapters provide some insight into the fundamental issues with regards to continuum mechanics and stress analysis that must be clear to the reader so that they may then make the appropriate decisions when performing field measurements. The next three chapters deal with the use and application of strain gages. There has been a lot of work done in this field, so the aim was to provide some basic and practical information for the reader to be able to make sound choices with regards to a selection of gage and understanding the conditions for measurements. The remaining chapters deal with optical methods. Here for the first time ever the reader will see the unifying nature behind all these methods and should walk away with a more complete understanding of the various optical techniques. Most importantly, all the various examples that we have done over our careers are shared so that the reader can understand the advantages of one method over another in a given application.

Ultimately this book should serve as both a learning tool and a resource for industry when faced with difficult problems that only experimental mechanics can help solve. It is our hope that the students who read this book will understand what it takes to perform research in this field and provide inspiration for the future generations of experimentalists.

Our thanks go to Kristina Young M.S. who kindly rendered our illustrations.