CAD modeling

Developing a Parametric Model of a Bicycle and Human

January 25, 2021 By Leslie Langnau Leave a Comment

A general-purpose parametric SolidWorks model has been created to enable rapid evaluation of novel bicycle concepts. It can be used for ergonomics, checking clearances, aerodynamics, kinematic and degrees of freedom studies, and product visualization.

Dr. Jody Muelaner, PhD CEng MIMechE

For this study in ergonomics, a set of anthropometric models from 3D Human Model were used as the starting point. Key bicycle geometry was defined using reference geometry in individual part files, located using distance and angle mates. This geometry includes the contact points at the saddle, handlebar grips and pedals, as well as the steering axis and wheel positions. Anthropometric models were then configured to fit to these contact points, with a few additional angle and distance mates that allow further adjustment of the rider position. The included models represent 5^th percentile females, 50^th percentile males and 95^th percentile males. The model is stored as an assembly template file, enabling it to be easily reused as a layout for different design concepts. It was created for the BriefBike project, which is developing a new class of folding bicycle that will effortlessly fold into a roller-case.

Key bicycle geometry
There are three ways that references can be parametrically defined within an assembly. Using a sketch or creating reference geometry (point, axis or plane) is often more straightforward and, in the case of a sketch, allows multiple parameters to be defined in a single model tree feature. However, parameters defined in this way cannot be animated or adjusted using the Mate Controller. The Mate Controller is particularly useful within this model as it allows parameter sets for different riding positions to be stored independently of configurations. It is, therefore, possible to apply different standard riding positions to any configurations that a are created. In order to provide this greater flexibility, parameters must be defined using distance and angle mates. This means the reference geometry is first defined with a part file, and the part is then mated in the assembly file.

The parametric bicycle geometry definitions are:

Planes parallel with the Right plane, defining the width of each pedal from the centerline (Right plane).
The crank length part three axes – the bottom bracket axle and the two pedal axles. The crank length is defined by different configurations of the part file. The part is mated on the right plane with the bottom bracket axis on the Front plane and at a distance from the Top plane, representing the bottom bracket height from the ground. This leaves the pedals free to rotate.
Pedal thickness parts contain a pedal axis and a plane representing the top surface of the pedal. They are mated to the pedal axes in the crank length
A Seat part contains planes representing the seat tube angle and the top surface of the saddle. The seat tube angle is mated coincident with the bottom bracket axis and at an angle from the top plane. The top surface is mated at a distance from the bottom bracket axis.
A Bar part contains an axis to represent the handlebar. It is mated at a vertical and horizontal distance from the bottom bracket axis.
The orientation the handlebar grips is defined in terms of two sequential Euler rotations – the backwards and downwards sweep. A separate part is used to define each rotation.
Grip_Sweep parts are mated to define the position and backwards sweep, with the following constraints:
- Two translations by mating the origin coincident with the Bar axis
- The remaining translation, the width of the grips, is defined by a distance mate between the part’s origin and the assembly Right plane.
- Two rotations are constrained by mating the part’s Top plane parallel with Top in the assembly.
- The backwards sweep is the only remaining degree of freedom. It is defined with an angle mate between the part’s Front plane and Front in the assembly
Grip parts are then mated relative to the Grip Sweep part, defining the downwards sweep.
- All three translations are constrained by mating the origin coincident with the origin of Dum_Grip_Sweep
- Two rotations are constrained by setting the Front plane parallel with Front in Dum_Grip_Sweep
- The downwards sweep is the only remaining angle. It is defined with an angle mate between the Top plane and Top in Dum_Grip_Sweep
Wheels contain an axle axis and a ground plane set at the wheel radius. Different configurations are used for different wheel sizes. Certain configurations may also include basic solid geometry to visualize the tire. The wheels are mated with the ground plane on the assembly Top plane and at distances forwards and rearwards of the bottom bracket axis.
A Steering Axis part contains a plane to represent the steering axis angle and an axis to represent the line where this plane intersects with the ground. The axis is mated coincident with the Top plane of the assembly. A distance mate between this axis and the Front plane of the front wheel defines the steering trail. The steering axis angle is then set with an angle mate.

Care must be taken when using angle mates. The direction in which the angle is defined can flip when changes are made to the model, causing assembly rebuilds to fail or result in unexpected behavior. These issues can usually be avoided by defining a reference entity for each angle mate, which is not defined by default. This is normally just a one click operation, by selecting Auto Fill Reference Entity.

Kinematics of the Human Models
The human model has parts or sub-assemblies for hands, lower arms, upper arms, clavicles, head, neck, thorax, abdomen, pelvis, upper legs, lower legs and feet. These 19 rigid bodies have 114 degrees of freedom (DoF) without any joints or other constraints. Joints between the body parts are either spherical, removing the three DoF for translation, or revolute which also removes two rotations, constraining a total of five DoF.

When all of the joints are added to the body parts, the human model still has 43 DoF. Considering the kinematics of the model as a whole is, therefore, overly complicated. Luckily, it can be broken down into smaller kinematic chains that behave independently. For example, each leg forms a kinematic chain which also includes the crank, and each arm forms a kinematic chain between the shoulder joint and the handlebar grip.

An understanding of how a person should be positioned on a bike was provided by Mike Veal, who created the DIY Dynamic Bike Fitting guide.

The hip joints should align with the plane of the seat post angle.
The angle of the line between the hip and shoulder joints is typically between 45° and 55° from the horizontal. 45° to 50° is usual for a road bike and 50° to 55° is typical for a more relaxed upright position. Dutch bikes can be from 65° to 90°.
The angle of people’s feet relative to the floor is quite personal but a typical value is 15°.
The leg does not completely straighten at the bottom of the pedal stroke. Typically, the angle between the upper and lower leg does not exceeds 140°. Although some literature puts this angle closer to 150° this is due to static measurements with the foot parallel to the floor. When pedalling, the foot assumes a natural angle which reduces the extension of the leg.
Wrists allow three types of rotation and should ideally be in a neutral position for all of them:
- Flexion/Extension can be fixed at the neutral position, with the flat plane of the hand aligned with the forearm axis. It can be adjusted without significantly changing the position of the arms by rotating the hands around the axis of the grip.
- Deviation is sideways movement of the hand, towards the thumb is radial deviation and towards the little finger is ulnar deviation. The neutral position does not position a griped bar perpendicular to the axis of the forearm but rather that the third metacarpal bone is aligned with the forearm axis. One study found that a natural grip results in a mean angle of 65° between the grip axis and the third metacarpal, with the grip sweeping back as though the wrist was in 25° ulnar deviation. However, the standard deviation was 7°, due mostly to variation between individuals, suggesting significant adjustability may be desirable for this aspect of the grip position.
- Supination/Pronation: Rotation about the forearm axis is known as supination when the thumb is rotating towards the back of the hand and pronation when it is rotating towards the palm.

Kinematic chain from pelvis to head and shoulders

This section of the body is made up of the pelvis, abdomen, thorax, clavicles, neck and head. The pelvis is fixed at the saddle, but is free to rotate so that it tilts forwards. The clavicles are mated parallel with the front and top planes of the thorax, effectively forming one ridged body with the shoulders in a neutral position. Although the components could be mated in series, starting with the tilt angle of the hips and then setting the angle between each part, this would make it hard to set an overall lean angle. A reference part is therefore introduced with a plane that defines the lean angle. This part is mated with an axis through the hip joints and with an angle mate relative to the assembly Top plane.

A symmetry mate is used to apportion half of the forward lean to the pelvis tilt. The Front plane of the pelvis is the plane of symmetry. The seatpost angle and the forward lean planes are symmetric about it. The abdomen is mated parallel with the dummy lean plane, and the shoulder joints are set to be coincident with the forward lean plane.

Kinematic chain from hip joint to bottom bracket

Each leg can be considered separately, as a kinematic chain consisting of the crank, pedal/foot, lower leg, and upper leg.

These four bodies have 24 DoF, which are reduced by revolute joints at the knee, pedal axis and crank axle, and spherical joints at the hips and ankles, to leave three DoF:

Crank position is intentionally unconstrained to allow pedaling motion.
Foot angle from the floor varies between individuals but the toes are typically pointed downwards by an angle of approximately 15 degrees.
The spherical joints at the hip and ankle also allow the leg to rotate about an axis between these joints, so that the knee moves in a circular motion. Assuming the leg does not lock out into a fully extended position, this can be constrained by making a point on the knee joint coincident with the plane defining the pedal width.

Kinematic chain from shoulder to handlebar grip

The kinematic chain for each arm consists of the upper arm, lower arm and hand. These three bodies have 18 DoF, reduced to just two DoF by spherical joints at the shoulder and wrist, and revolute joints at the elbow and the grip of the hand around the bar. The remaining two DoF can be considered as:

Rotation of the hand around the handlebar
Rotation of the whole arm so that the elbow moves in a circular path about the axis between the shoulder and wrist joints

Setting the wrist to neutral flexion removes one DoF. This can be achieved by mating the hand’s top plane parallel with the forearm axis. There are several possible ways to remove the remaining DoF. It was found that the most practical and stable is with a distance mate between a point on the elbow joint and the Right plane of the assembly.

Adjusting and configuring the model

The assembly template contains three different anthropometric models, all configured as described above. These models represent a 5th percentile female, 50th percentile male and 95th percentile male. They can be activated by simply suppressing or suppressing the associated folders in the feature tree.

Different pre-configured body positions are also included. These are defined using a Mate Controller for each percentile model.

Conclusions

The bicycle and human model template enables the rapid evaluation of novel design concepts. Applications include ergonomic studies, mechanical clearance checks, aerodynamic simulation and product visualization. The simple parametric definitions allow easy adjustment of all the relevant variables in the bicycle geometry and rider position.

Extending the life of hip implants

September 17, 2020 By Leslie Langnau Leave a Comment

Using CAE technology to design durable implants and implement effective fatigue evaluation

Bone implant designs must be precise and durable to withstand significant load and pressure over time. One of the most frequent and severe joint inflictions, osteoarthritis, often requires administration of a Total Hip Replacement (THR). This implant, being significantly load-bearing, requires a complex design that is strong enough to withstand long durations of load.

Detroit Engineered Products (DEP) effectively tackled this challenge by developing 3D CAD models for implants and building an effective fatigue evaluation model to assess the life cycle of various implants. Through its proprietary CAE platform, MeshWorks, DEP used unique morphing and meshing tools to further understand non-standard hip implant geometries. This enabled DEP to carry out hypothetical test scenarios and estimate the fatigue life of implants, which resulted in significant benefits in extending the overall life of the implants.

DEP’s MeshWorks technology provides a platform that allows users to generate multiple, customizable designs based on customer variations, reduces design time compared to traditional methods of implant design, reduces design costs for companies and serves as a comparative tool for comparison with other competitive products.

With its in-house design team and CAE platform, DEP’s engineers calculate fatigue life cycles of implants and run multiple tests to help combat uncertainty once the implant is created and implemented.

DEP’s software introduces a faster time saving method of designing. In the conventional design process for an implant, the design is built as a 1D CAD data, then converted to a 3D CAD data and then into a 3D mesh data, on which virtual validation is conducted. This testing involves considerable back and forth with CAD data, which can be time consuming. In the new process, as soon as the 2D line data is available, it can be directly taken into MeshWorks and converted into a 3D mesh data. This 3D mesh is now parametric, and as virtual testing is conducted, changes can be done directly in the mesh data, and the time taken to and from design changes with CAD can be reduced.

Speaking on the latest development by the team, the Chief Innovation Officer at DEP, Mr. Karthik Shankaran said, “While working closely with biomedical companies, we found the need for specific mesh modeling tools, morphing tools and optimization techniques. This tool from MeshWorks will not only help companies save time as they use it for meshing and virtual validation but will help in the area of preparing regulatory and non-regulatory virtual validation reports for FDA before clinical trials as well.”

Detroit Engineered Products
www.depusa.com

A new 3D approach to remote design engineering

August 20, 2020 By Leslie Langnau Leave a Comment

by Karl Maddix, co-founder and CEO of Masters of Pie

The coronavirus pandemic has forced almost every business to adapt to new ways of working. In many cases, conferencing services have saved the day – enabling remote teams to collaborate on projects when they can’t be in the same room. But two-dimensional (2D) conferencing is a poor substitute for engineers trying to work together remotely on complex 3D data to design the latest motor vehicle or jet engine.

And trying to untangle complex problems remotely from thousands of miles away is fraught with difficulties – even when using products like Microsoft’s Remote Assist. The expert often has to resort to waving their hands around on a screen to communicate to the technician which part of a machine they should be fixing – and which parts should be left alone.

Real-time immersive 3D collaboration is now adding a new dimension to such problem solving – users can share live, complex 3D files such as CAD data, interact with them and reveal ‘hidden’ parts deep within a machine that may be causing an issue. The technology also transforms day-to-day collaboration between remote engineering team members. Design reviews, for example, can be brought to life, with participants ‘walking through’ a model, no matter where they are in the world.

The fundamental problem at the root of many of these issues until now has been that enterprise teams have lacked the ability to effectively collaborate in real time using live, complex 3D data. The solution lies in purpose-built framework technology for integrating natively real-time collaboration and immersive device support directly into legacy enterprise software packages.

The key to enabling true real-time collaboration is to start where the data ‘sits’ and ensure that this original data ‘truth’ is the same for everybody when working together, no matter where they are located or what device they wish to use. This way, everyone in the team has the correct and most up-to-date information available.

Whether it is a CAD package, PLM software, an MRI scanner, robotic simulation software or a laser scanning system, many industries are becoming increasingly dependent on spatial data types and digital twins. These complex data formats are usually incompatible or just too cumbersome to use ‘as is’ in existing collaboration platforms such as Webex, Skype, Google docs or Slack – all built primarily for 2D data formats such as video, text or images.

Moreover, the legacy software generating the data itself is unlikely to have any in-built real-time collaboration functionality – forcing enterprise users to resort to one of two methods. One option is to manually export the data, carry out a painful and time-consuming reformatting process, then manually import the newly crunched data into some type of third-party standalone collaboration package. The alternative is to ignore the spatial nature of the data entirely and instead screen-grab or render out 2D ‘flat’ images of the original 3D data for use in a basic PowerPoint presentation or something similar.

Neither of these methods allows teams to efficiently collaborate using a live data truth – i.e. the original data itself instead of a reformatted, already out-of-date interpretation of it. So, both methods only compound the root collaboration problem instead of helping to solve it.

The latest generation of real-time immersive 3D collaboration technology is integrated directly into the host software, grabbing the original data at source before efficiently pushing it into a real-time environment which users can access using their choice of device (VR, AR, desktop, browser or mobile) for instant and intuitive collaboration. End-to-end encryption ensures that even the most sensitive data may be confidently shared across remote locations.

The integration into the host package provides not only a live connection to the data but also a bi-directional connection, meaning that users are still connected to the host software package running in the background. The advantage of this over standalone applications is that it still gives access to core features of the host package – enabling accurate measurement of a CAD model using vertex or spline snapping to the original B-Rep held in the CAD package, for example. All the underlying metadata from the host package is also available to the immersive experience – and annotations, snapshots, action lists or spatial co-ordinate changes can be saved back into the host package.

The new post-pandemic requirement to have a distributed workforce – in conjunction with the rollout and adoption of key technology enablers such as server-side rendering and high-capacity, low-latency connectivity – is set to accelerate the adoption and integration of real-time immersive collaboration solutions. In the future, 5G technology will also open up the potential to stream to immersive AR and VR devices – untethering the experience and facilitating factory-wide adoption of immersive solutions. For example, as ‘Industrial Internet of Things’ (IIoT) data streams from smart devices in the factory, it will be overlaid via AR glasses in the physical space. And as cloud service providers build out features such as spatial anchoring to support ever-larger physical spaces, these new services will be used within collaborative environments rich with real-time data.

Factory workers, for example, will have the ability to ‘dial an expert’ directly from a virtual panel on a smart factory device. This offsite expert will appear as a holographic colleague and bring with them live 3D data for that individual machine. Both users will have real-time IIoT data overlaid intuitively on the fully interactive 3D model to facilitate a more effective diagnosis and maintenance process.

Empowering shop-floor workers with hands-free AR and detailed 3D data will dramatically improve assembly line efficiency, with an intuitive environment where product data is fully interactive. Users will be able to move, hide, isolate and cross-section through parts, while using mark-up and voice tools to create efficient instructions for the assembly or disassembly of complex products. These instructions will be recorded and delivered as holographic guides via AR directly on the assembly line.

The next generation of real-time immersive 3D collaboration technology is even set to enable you to have a scaled-down hologram of your latest engine design sitting right in front of you on your desk. As you work on the design and refine it using your CAD software, the changes will be dynamically loaded into the hologram so that you can see the effects immediately and make any further necessary adjustments.

Meanwhile, digital sleeving – with 3D images overlaid on physical designs – will enable you to check how two parts of the engine come together, even when they are being designed by different teams in different locations. Similarly, you will be able to see how, for example, cabling will fit inside your latest aircraft seat design or where best to put the maintenance pockets for easy access.

This kind of approach adds a new dimension to the handoff between design and manufacturing. If adjustments need to be made to a fan assembly design, for example, the relevant part can be isolated within an immersive design review – and speech-to-text notes can be added to the part number and automatically become part of the change request. It’s all a far cry from endless design iterations, spreadsheets and printouts – or CAD screen shares using a 2D representation of a 3D problem.

In the post-pandemic remote world, conferencing is bringing people, video and documents together. Collaboration is now adding the fourth dimension of 3D immersive experience to complete the picture.

Volume Graphics version 3.4 CT-Software includes Scan-to-CAD reverse engineering capabilities

July 9, 2020 By Leslie Langnau Leave a Comment

Digital analysis and physical testing are increasingly integrated with the pursuit of optimized product design and development across a wide swath of industries. With the release of its VGSTUDIO MAX version 3.4 software, Volume Graphics has added and augmented important functions that help designers and manufacturers capture and interrogate product data to improve final quality.

When there’s no 3D CAD model of an object available, VGSTUDIO MAX 3.4’s Reverse Engineering Module provides a suite of capabilities in one automated package. The Module can generate surfaces from a CT scan, or any voxel model converted from a closed mesh/point cloud scan, using an auto-surface function that is fast and accurate. This new function allows manually generated design models to be available digitally—without the need for a CAD designer or reverse-engineering specialist.

The new Reverse Engineering Module in VGSTUDIO MAX 3.4 makes it easy to convert CT scans into CAD models that can be used in any CAD system without the need for a CAD designer or reverse engineering specialist.

An important benefit is the ability to generate and archive 3D CAD models of legacy parts, as well as update those models in which the actual part or tool deviates from the master CAD model. This automates the creation of digital twins of individual parts and allows for validation of the model-to-part relationship. The recreated or newly validated CAD model can be exported as a STEP file to any CAD system. The software also enables CAM systems to mill on CAD instead of meshes.

Compare components or samples over time to detect damage and calculate strains with DVC
Measuring strain and quantifying and visualizing defects in material samples due to external loads are key tasks for materials scientists. The new Digital Volume Correlation (DVC) Module in VGSTUDIO MAX 3.4 helps users to quantify displacements and strains simply and intuitively between multiple states over time. It enables a precise insight into the material at hand, e.g., to detect cracks and to measure the local strain.

Displacement between two datasets acquired during an in-situ test on a fiber-reinforced polymer, as visualized in VGSTUDIO MAX 3.4 software. Data provided by the Institute of Applied Materials at Karlsruhe Institute for Technology (KIT) in Germany.

This is particularly useful for gaining a deeper understanding of foams, fiber composite materials, or additively manufactured (AM, aka 3D-printed) porous samples or components. Voxel-based, three-dimensional volumes are automatically correlated by the software, allowing for before-and-after comparisons of in-situ experiments. Results are visualized in extreme detail, making it easy to pinpoint exactly where defects or damage have occurred.

The user can quantify and visualize problems like cracks and pores, which can be missed by the naked eye, by comparing datasets at different states over time with the initial undamaged data. Results are visualized via color overlay, vector fields or strain lines. The equivalent strain or single components of the strain tensor can be shown as a color overlay and mapped directly on a volume mesh to validate the results of FEA simulations.

VGSTUDIO MAX also allows for mapping microstructure information such as fiber orientation, fiber-volume content, or matrix porosity on the same mesh that is used for the FEA. This allows the user to consider all significant microstructure information within a mechanical model and validate it by comparing FEA and DVC results.

VGSTUDIO MAX 3.4 provides stunning visualizations that show a spatial impression of the displacement field.

DVC is not only helpful in the laboratory—it is also a powerful tool to detect internal damage for maintenance of composite materials, like those in a helicopter blade, by comparing a scan acquired after manufacturing with another scan of the same part after several years of use.

Other enhancements to version 3.4
In addition to the new reverse engineering and volume comparison enhancements, VGSTUDIO MAX 3.4 also includes:

–New visualization options for deviations of geometric tolerances to answer questions such as: Where exactly are the highest deviations located? How are the deviations distributed on a surface? Which areas of the surface were actually evaluated?
–Subvoxel-accurate defect detection with VGEasyPore to differentiate between gas pores and shrinkage cavities.
–Stress tensor export in a .csv file of stress fields calculated using the VGSTUDIO MAX Structural –Mechanics Simulation Module, e.g., for fatigue analysis.
–New, more intuitive Tool Dock that reduces mouse travel needed to navigate.
–Support of 4K displays for a crisper, sharper, and scalable graphical user interface.

Volume Graphics software provides a visual, comprehensive, and easily understandable representation of a part’s geometrical deviations. Depending on the toleranced element, certain methods for visualizing the actual deviations can be activated, e.g., a colored and scaled deviation vector for position tolerances (above), while simultaneously showing entire patterns of position tolerances.

Many of the enhanced capabilities released in this latest version of Volume Graphics’ software have been developed with direct input from customers. The result is a high-performance, automated package of CT-scan data analysis and visualization tools that supports design and development engineers, and manufacturers, in producing the highest-quality, most-competitive products possible.

Volume Graphics
www.volumegraphics.com

2020 & beyond: 6 more megatrends to watch in engineering modeling & simulation

June 8, 2020 By Leslie Langnau Leave a Comment

Bruce Jenkins | Ora Research

Early last year we reviewed in this blog post eight megatrends in engineering modeling and simulation that dominate the thinking and decision-making of engineering organizations and their technology providers today, and that we believe will continue to do so well into the years ahead. We wrote then:

This second decade of the twenty-first century is witnessing an explosion of invention and innovation in digital engineering technologies unrivaled since the 1980s, when so many foundational tools and methods were either created or brought to practical fruition. Here are eight megatrends that we believe will drive generational leaps forward in engineering modeling and simulation technologies, methods and work processes through 2020 and well beyond:

Simulation-led, systems-driven product development.
Democratization of engineering modeling and simulation.
Simulation app revolution.
Design space exploration.
Topology, materials and process optimization for additive manufacturing.
Simulation for the Industrial IoT and Industry 4.0.
Big-data analytics in simulation.
Cloud HPC for simulation.

Now, here are six more large-scale trends that we think are already beginning to drive major technology developments and end-user investments aimed at fostering cost, schedule, quality, performance and innovation improvements in the engineering and production of discretely manufactured products (many will benefit process manufacturers and other AEC industry participants as well):

21^st– century data management: Separate the data from the data model (Aras, Onshape, Frustum…).
From PIDO to fully automated design space exploration: 20+ years of evolution—then a revolution.
New requirements-management technologies that automatically link—via new systems-level engineering technologies—requirements fulfillment during the course of system-level product design (MapleSIM, Simulink…) back to digitally captured system requirements definitions (SysML, Rational Rhapsody…), and automatically track and report divergences and convergences between the two as the design evolves.
Generative design design space exploration: What’s alike? What’s different? When is each appropriate?
AI and ML (machine learning) in engineering modeling and simulation—the next steps beyond big-data analytics for simulation.
Further breakthroughs in co-simulation: Simultaneous instead of step-wise simulation of multiple physical domains, and at multiple (mixed) fidelities—0D, 1D, 2D, 3D; also 4D and 5D in AEC.

Previews

21^st– century data management

Aras’s insight to separate the data from the data model makes them, a bit ironically, the technology vendor that will relieve legacy PLM vendors of the prohibitive burden of somehow hoisting their SQL-based PDM offerings into the cloud, yet still link them into today’s truly “open” world. Because Aras’s technology architecture has already done it for them, before them.

And without charging a penny for the technology. Aras makes its software available as freeware. Users can download, evaluate, and implement it in production usage—all for zero dollars. If users then need customization and integration to link into their unique multi-application environments, Aras is available to do this, for fair and reasonable service fees. An extraordinarily apt 21^st-century business model, we think.

From PIDO to fully automated design space exploration: 20+ years of evolution—then a revolution

This breakthrough was sparked by HEEDS from Red Cedar Technology, a paradigm-disrupting startup that commercialized university-developed technology under the leadership of Bob Ryan, and was subsequently acquired by Siemens PLM. HEEDS has set a new direction now being pursued by almost every major DSE technology provider.

Generative design vs. contemporary design space exploration

When early adopters of generative design technology discover what fully automated design space exploration technology can do, will they feel underserved by their generative-design technology providers putting forth tools that still require, today, an arguably unnecessary exertion of effort by engineers and designers in making decisions that the software should be making for them?

Or, instead, is generative design an entirely appropriate technology for disciplines and end-user markets where not all problems and sought-for outcomes can be expressed in an entirely determinative manner? The jury is still out on this one, we believe. After users gain more experience with both approaches—and with continuing maturation of both technology classes—time will tell.

Looking forward

Watching the answers to all these questions take shape is going to be fascinating. We greatly look forward to following and reporting here on these and many more game-changing technologies and trends as they unfold in the quarters and years to come. Stay tuned!

Selected background reading

Aras acquires Comet Solutions

HEEDS MDO

MapleMBSE from Maplesoft radically expands accessibility of model-based systems engineering

CosiMate from Chiastek: Co-simulation conduit for multifidelity systems modeling

Cloud-native CAD will disrupt the PLM platform paradigm

Look up in the sky, it’s CAD software

May 14, 2020 By Leslie Langnau Leave a Comment

A major CAD vendor is betting the modeling software’s future is in the cloud

By Jean Thilmany, Senior Editor

Onshape set off ripples across the computer-aided design community five years ago when it announced its computer-aided design software would exist completely in the cloud. Last fall, PTC acquired Onshape.

The purchase signals PTC’s conviction that engineering companies are ready to embrace CAD in the cloud. The SaaS model, while nascent in the CAD and PLM market, is rapidly becoming the industry’s best practice across most other software domains, said Jim Heppelman, PTC president and chief executive officer.

By bringing Onshape in-house, the software maker has placed itself ahead of the pack in what the engineering software maker sees as the inevitable industry transition to SaaS, Heppelmann said.

“Today, we see small and medium-sized CAD customers in the high-growth part of the CAD market shifting their interest toward SaaS delivery models, and we expect interest from larger customers to grow over time,” he said.

In the future, CAD sellers may reach unique arrangements with resellers to bring CAD in the cloud to a wider user base, according to one potential reseller.

But PTC isn’t going all-in with the cloud. It will continue to offer its on-premise CAD software, Creo.

With CAD in the cloud, designs reside on the software provider’s secure server —rather than on individual workstations. Because the software is accessed and managed online, engineers and designers can work on their models from any location and on any device. The SaaS refers to a provider’s capability to deliver everything needed to run CAD in the cloud—including the cloud infrastructure and the CAD software itself.

Though other CAD makers do offer some type of cloud capability, it’s generally the capability to check files into and out of an application on a cloud-based server; engineers don’t design directly with cloud-based software on other applications, said Jon Hirschtick, president of SaaS, PTC. Onshape differs in that its software exists fully in the cloud and can be used by multiple users in real-time, he added. (Hirschtick founded SolidWorks in 1993 and then went on to co-found Onshape with another former SolidWorks chief executive officer, John McEleny.)

The everyday cloud
You’re already using cloud technology. That’s almost certain. If you have an email account ending in gmail.com or yahoo.com, if you’ve checked a social media account from your desktop or mobile device, if you’ve streamed a movie via Netflix or Amazon or any other provider, you’re a cloud user. The email, social media, and streaming software exist on the software owner’s server (let’s say Google), as does your little piece of it—like your Gmail email address.

Though it’s been possible to run CAD as a SaaS for the past few years, CAD has always been slower than other large, graphic-intense and complex applications to pivot to new platforms, says Len Williams, content manager at designairspace, which gives engineering companies the capability to run any CAD system in the cloud.

“Last year’s acquisition is a very clear statement that vendors like PTC see cloud as a platform of the future for CAD and for all their other software,” Williams added, calling the acquisition a “Windows-level” move.

“CAD systems were originally based on UNIX running on silicon graphics workstations. Then Windows came along and people were laughing at the thought of using CAD on Windows,” he says. “Now most of the major CAD systems run only on Windows.”

Likewise, the way companies buy their CAD software has evolved, he said.

“We went from the old perpetual model, where you buy the software for a workstation, to today’s subscription-based model, where you rent the software,” Williams said. “The next step is when a CAD vendor is running it for you so don’t have to buy hardware or worry about upgrades.”

Large companies already run CAD in the cloud because of the benefits the delivery method offers, Williams added. The difference is, those companies—the French automotive manufacturer PSA Peugeot Citroen is one example—have the funds to build their own, private clouds. Designers, engineers, and suppliers at those companies can access CAD on the private cloud whatever their location: Tulle, France; Brussels, Detroit, or elsewhere.

Working remotely and sharing with suppliers
For the smaller guys, the cloud can bring the same benefits their larger counterparts already enjoy; mainly real-time working together and version management, Hirschtick said.

“Versioning” is built-in, which means file changes are tracked in a central database in real-time. Because any engineer with permission can access the software from any device with internet connection, engineers in different places can work together on a design, such as a power supply, for example. There’s only one power supply file; Onshape doesn’t copy it. But with cloud, everyone in the world accesses real-time single source of truth database. We’re not passing around copies all over the place, he added.

“If multiple engineers happen to be working on that file at the same time, it’s not a problem. If one engineer rounded a corner and another one drilled a hole, both changes get captured,” Hirschtick said. “If we’re both rounding a corner at the same time, you would see my hand there in real-time—at the same table—and a box around the corner would indicate that another engineer is editing that right now.”

The bigger the team is, the quicker the product development process, as everyone—even suppliers—has visibility into the real-time database rather than a copy of something emailed a week ago.

When the workflows are quicker, engineers have more design time and are more willing to innovate to try new things, he added.

Most cloud service providers automatically update their programs. Thus, IT staff can focus on other tasks and engineers know they are working with the latest version of the applications.

Also, engineers aren’t bound to their workstations. The software exists at one central server while engineers work from many. They can be globally dispersed and can work from home or other locations outside the office.

Smaller companies that scale their workforce and supplier base up and down as projects change also stand to benefit from SaaS CAD software. When suppliers move away from a project, the company can easily suspend their CAD license and use of the CAD system, Williams said.

When the coronavirus began making headlines in early 2020, engineering companies running one Onshape customer with offices in three major Chinese cities particularly welcomed the remote-work capability, Hirschtick said.

“Using Onshape analytics, they showed us where people were working before the virus situation in China,” Hirschtick said. As expected, employees worked at the offices in the three major cities.

“Then they showed a map of activity of first two weeks of virus quarantines and lockdowns in China,” he added. “This time there were 20 little circles in regions all over in China. They could see where their employees logged in remotely.

“It doesn’t matter if employees are caught at home with only an Android tablet, they can still do their work,” Hirschtick said. “Even with the phone they can even do some of their work.

There are cases where running CAD in the cloud just doesn’t make sense. Some companies may use CAD only a small amount of time and will do better essentially renting a CAD program, perhaps through the cloud, Williams said.

With cloud-based systems, issues of total cost of ownership and return on investment are generally murky because companies want to see how cloud applications compare to traditional on-site infrastructure.

“But there are so many intangibles wrapped up in the cloud that it makes it hard to put calculations on it,” said Andrew Sroka, CEO at Fischer International Systems, which helps companies manage identities for on-premise and cloud-based applications. “It’s important to factor in expenses like utility costs and power requirements.”

If you can’t buy, rent
With CAD in the cloud being not if, but when, designairspace has new ways to bring benefit to users and CAD makers; reselling vendor software in the cloud. The company would offer customers workstations with major CAD vendor’s programs already installed. Designairspace can track use, which allows the vendors to charge based on time spent using the programs.

“It would be just like mobile phone plans back in the day, where you buy a plan based on minutes. We can do this with CAD in the cloud,” Williams says. “Let’s say you buy a plan with 500 minutes. If you need it for only one or two days a week, you can buy it in a small, affordable plan that’s a small portion of what it would cost you to buy the software.

“Why limit CAD-in-the-cloud to large companies? In the olden days, only big companies could afford 3-D CAD systems. We want small companies to have cloud benefits,” he added. “They would still need to buy their own licenses, but at least they can run it in the cloud, like the big guys.”

The pricing model would benefit companies with project managers or suppliers who don’t design in CAD but log onto the program intermittently.

“These people use the software only a little bit at a time, so we can price it so it’s not so expensive for them,” Williams said. “This is a whole new market for major vendors.”

Becoming a CAD-in-the-cloud reseller and offering online training in those CAD programs is the “next step” for designairspace, he added. With the business model, potential customers can also receive on-the-spot, specialized training if needed and can test the software to see if it’s right for their needs before buying a priced plan, he said.

Or, engineering companies might choose to run hybrid CAD, in which they host some CAD systems at workstations on-premise and buy CAD in the cloud subscriptions for intermittent users, Williams said.

“That way you can gradually move more and more users to the cloud. You can move one or two and see if it works and if it does move more to the cloud,” Williams said. “The heavy users will never move to the cloud.”

That brings up another point. Workstations that run CAD will always be with us, he added.
Companies that do work for the defense department or for the military must do their engineering work on company workstations. They cannot work remotely, Williams said.
For his part, Hirschtick is dreaming big. He expects widespread cloud adoption for CAD as users begin to see the advantages.

“People think cloud tools don’t have the power or speed but that’s an old fable. Cloud tools have more advantage and speed, it’s not a fair fight,” he said. “With desktop tools, you have one CPU sitting on the desktop. With the cloud, we’re able to use unlimited amounts of CPUs. I gave a demo and opened up ten models with thousands of thousands of parts on Macbook in Chrome on a web browser. There’s no CAD workstation that could open them that fast, even the best desktop configuration.

“In a few years, people will be saying ‘how did you ever do CAD on a desktop. How was it fast enough?’” Hirschtick says.

While the future remains cloudy, PTC is backing clearing skies for CAD in the cloud. With a large CAD maker backing SaaS, expect to see a flurry of news and updates. In other words, don’t delete your desktop program.

PTC
www.ptc.com

Designairspace
www.designairspace.com

Implicit modelling for complex geometry

February 3, 2020 By Leslie Langnau Leave a Comment

Implicit modelling is enormously powerful and offers huge potential for engineering design. It is currently ready to apply in specific modelling tasks involving complex geometry. It is not yet clear whether it will be possible to fully implement it within the familiar parametric design environment, with constraint-based sketches used to define geometry. If not, it may remain a specialized tool.

Dr. Jody Muelaner, PhD CEng, MIMechE, Muelaner Engineering Ltd

Additive manufacturing enables the production of highly complex geometries such as repeating lattice-like structures and organic shapes. This design freedom can improve performance in terms of weight, stress, fluid flow and heat transfer. However, modelling, editing and processing complex geometry files is challenging for the established CAD software. Let’s look at how implicit modelling can solve these problems as well as the challenges still remaining to integrate it into the design workflow.

The problem with current CAD software
If you’ve ever tried modelling complex lattices in a typical CAD program, such as SolidWorks, you’ll know this is extremely challenging. When models have large numbers of features, the file sizes and rebuild times increase exponentially. Conventional CAD programs can also have difficulty with apparently quite simple models if they have features such as two fillets that overlap. When high feature counts are combined with these types of challenging geometry, model rebuilds become painfully slow at best and all too often they simply fail.

The problem is the fundamental way that geometry is represented. All the major CAD programs use boundary representations (b-reps) to define solid geometry. B-reps use topology, such as vertices, edges and faces, which are defined by geometry such as points, curves and surfaces. For example, an edge is a bounded region of a curve and a face is a bounded region of a surface.

B-reps work fine for relatively simple geometry. Older CAD programs sometimes used Boolean operations to combine primitive objects such as cubes and spheres. B-reps are much more versatile, allowing profiles to be swept and lofted, solids to be shelled and so forth. However, when features are combined with fillets and blends, or large numbers of features are included, calculating the topology becomes exponentially more demanding on the computer. Many modelling operations involve combining simpler shapes with Boolean and blending operations. B-rep modelling has to calculate all of the new edges that are formed where faces intersect. For objects with planar faces the individual calculations are relatively simple, but the number of intersections increases by approximately the square of the number of faces. For intersections between curved surfaces the edges are complex splines, making the calculations considerably more complex. When faces are close to tangent, things get really difficult.

Another issue with B-reps can be determining which points in the model are inside the boundary – the solid material. The method is to shoot a ray from the point in an arbitrary direction. If the ray passes the boundary an odd number of times then the point is inside the boundary. If the ray passes the boundary an even number of times then the point is outside the boundary. Since floating point arithmetic is used, rounding errors may mistakenly count boundary crossings when the ray is close to the boundary. There are also ambiguities such as when a ray is tangent to a surface or passes through a vertex – it is not clear whether this counts as a boundary crossing, or as two crossings. Because of these issues, additional checks are required to ensure that b-rep modellers are robust. This makes them mathematically inefficient.

The mathematical simplicity of Implicit Modelling
Implicit modelling is a far more efficient way to model geometry. It doesn’t explicitly calculate any edges or vertices. Instead, a single mathematical function of x, y and z is used to describe a 3D solid. Consider the simple example of a cuboid centered on the origin.

A cuboid can be defined by the implicit function. Any point on the boundary gives a function value of zero, negative values are inside, and positive values are outside, which makes the determination of whether a point is inside or outside simple.

This cuboid can be defined by the implicit function:

F_A(x, y, z) = max(|x| – 4, |y| – 1, |z| – 1)

Any point on the boundary gives a function value of zero, negative values are inside, and positive values are outside. Determining whether a point is inside or outside is therefore very simple. There is no need to try to count how many times a ray crosses the boundary. Small floating-point errors will give the wrong answer only if the point is infinitesimally close to the boundary.

Performing Boolean operations to combine implicit geometry is also very mathematically simple. Consider two solids being added together in a Boolean union. For a point P to be within the combined solid, at least one of the two implicit functions must evaluate to less than zero. The combined implicit function is, therefore, simply the minimum of the two functions.

Imagine that a sphere, centered on the origin and with a radius of 2, is added to the cuboid.

Performing Boolean operations to combine implicit geometry is also mathematically simple. Consider two solids added together in a Boolean union. For a point P to be within the combined solid, at least one of the two implicit functions must evaluate to less than zero. The combined implicit function is, therefore, simply the minimum of the two functions.

The implicit function for the sphere is:
F(subsetB) (x,y,z) = √(x^2 + y^2+ z^2 ) – 2
The implicit function for the combined solid is simply the minimum of the implicit functions for the two shapes:

F_C(x, y, z) = min(F_A, F_B)

This simplicity of calculating geometry means that complex fillets and blends can be created reliably, and highly complex models rebuilt almost instantly. The below image shows fillets where a lattice joins a ring. The fillets overlap in a way that would probably fail in most CAD systems, but they have been easily created using nTopology implicit modelling software.

Implicit functions can also give a feature’s distance from the boundary.

Distance fields – a powerful feature
Another really useful property of implicit functions is that they actually give the distance from the boundary. A good way to understand this is to simply try some values in the function for a cuboid, used in the example above:

FA(x, y, z) = max(|x| – 4, |y| – 1, |z| – 1)

The smallest value that this function can take is -1. This occurs for points down the centre of the volume. There is no upper limit to the value it can take, since you can travel infinitely far once outside it.

Implicit functions make it easy to calculate a distance field either inside or outside the boundary of any solid geometry. This makes it very easy to perform shell operations and you can even create shells with varying wall thickness.

Implicit functions make it really easy to calculate a distance field either inside or outside the boundary of any solid geometry. This makes it very easy to perform shell operations. It is even possible to create shells with varying wall thickness.

Because implicit geometry is defined as a field, it can be modified by other fields. This enables highly efficient merging between different geometries, to create features similar to fillets. It is even possible to modify a geometry field using some other property, such as a stress field generated from an FEA study. In the below example, nTopology has been used to modify the diameters of beams in a simple diamond lattice, with the diameter increasing as the stress increases. It is also possible to do this with shelled solids, so that the wall thickness depends on stress. This can be a powerful method of optimizing structures that, unlike generative design, works well with cast or injection moulded components, as well as additive manufacturing.

In this example of implicit geometry, the software program nTopology was used to modify the diameters of beams in a simple diamond lattice, with the diameter increasing as the stress increases. It is also possible to do this with shelled solids, so that the wall thickness depends on stress. This can be a powerful method of optimizing structures that, unlike generative design, works well with cast or injection moulded components, as well as additive manufacturing.

Implementation of Implicit Modelling

A number of commercial software companies are offering implicit modelling packages. The field is currently dominated by smaller specialized providers such as nTopology, which has a suit of capabilities for working with complex geometry. Another notable start-up, Gen3D is currently focused on modelling flow paths for applications such as manifolds and heat exchangers such as the one shown below. The big players in the CAD world, such as Autodesk and PTC have started acquiring start-ups in order to gain access to this technology. Autodesk recently announced their Volumetric Kernel for Fusion360 which, it is claimed, will enable implicit modelling to be fully integrated within a parametric design workflow.

A number of commercial software companies are offering implicit modelling packages. One notable start-up, Gen3D, is currently focused on modelling flow paths for applications such as manifolds and heat exchangers.

At the moment, however, it is not possible to work directly with implicit models within any mainstream CAD system. Specialized implicit modelling software is instead focused on creating the complex geometry that conventional CAD struggles with. For example, you might import an initial design from your standard CAD package and then perform operations such as creating complex lattices, shelling complex components or creating intersecting fillets.

The most obvious application is for light weighting structures using lattices for additive manufacturing. However, there are many other potential applications for complex geometry creation such as cast components with complex fillets and variable thickness shells. Many industrial designers are taking advantage of the capabilities to produce decorative grills. Implicit modelling software such as nTopology makes it extremely easy to create these types of geometry.

Stress analysis using Finite Element Analysis (FEA) can also benefit hugely from implicit modelling. One of the most time-consuming parts of analysis can often be cleaning up the geometry created by CAD software. Because b-rep modelling creates edges where any surfaces meet, there can often be thin sliver geometry at intersections and fillets. This can make even relatively simple parts difficult to mesh with high quality elements. Implicit models don’t have any of these issues giving the potential for painless creation of high quality meshes. This can, however, create its own issues as discrete surfaces may be needed to define boundary conditions. B-rep surfaces are therefore sometimes imported from CAD to enable boundaries to be defined.

Gyroids and Triply Periodic Minimal Surfaces
Triply periodic surfaces are an interesting type of geometry that is relatively new to the world of manufacturing, although they have been known to mathematics since the 19th century. These surfaces repeat in each of three dimensions, similar to conventional arrays. However, the arrays of intersections between each element are often problematic, with fillets at each interface quickly making models unmanageable. With triply periodic surfaces, there are smooth transitions between each repeating element, with the entire surface defined by a single implicit function.

Triply periodic surfaces are a type of geometry that is relatively new to manufacturing, although they have been known to mathematics since the 19th century. These surfaces repeat in each of three dimensions, similar to conventional arrays. However, the arrays of intersections between each element are often problematic, with fillets at each interface quickly making models unmanageable. With triply periodic surfaces, there are smooth transitions between each repeating element, with the entire surface defined by a single implicit function.

Within mathematics and the natural sciences, there is particular interest in triply periodic minimal surfaces (TPMS) which are minimal surfaces as well as being triply periodic. This means having zero mean curvature. Although structures used within additive manufacturing may be TPMS’, it is generally not important whether or not they are minimal surfaces.
Because triply periodic surfaces can be defined by a single implicit function, they can easily be combined and blended with other implicitly defined geometry. They can also be modified by other fields so, for example, they adapt to stress, fluid flow or heat transfer requirements. These properties can make them enormously powerful from a modelling perspective. They also have many interesting mechanical properties although understanding these properties is still very much an area of active research.

Robust modelling enables greater reuse
One of the benefits that nTopology is stressing is something they call their notebook. This is a way of recording the process used to create geometry, very much like the feature tree in parametric CAD. Variables can be changed and references to external files updated, before replaying the process. This can enable workflows and best practices for capture, reuse and refinement.

While nTopology’s notebook is conceptually similar to a parametric feature tree, the increased robustness of implicit models should make it more useful. While parametric model templates work well for simple standard parts, they are often unreliable for more complex products. This is often because topology such as vertices and edges are automatically created and assigned ID’s while the geometry is being constructed. When changes are made that affect the way features intersect, especially where fillets are involved, the number of vertices and edges may also change, resulting in different ID’s being assigned. Features that reference this topology therefore lose their references and the model fails. The designer is often faced with a choice between a lengthy debugging process and recreating the model from scratch. Implicit modelling doesn’t depend on these topology references and is therefore unlikely to fail when variables are changed. However, if b-rep geometry is referenced underlying topology may cause workflows to fail because of this dependence.

Thus, implicit modelling is enormously powerful and offers huge potential for engineering design. It is currently ready to apply in specific modelling tasks involving complex geometry with the potential to be fully implemented in the familiar parametric design environment.

Muelaner Engineering Ltd
www.muelaner.com