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By Jean Thilmany

Right now, your CAD program doesn’t have much sense of style. Not like you do. Imbibing it with style won’t necessarily give it better taste, but could speed CAD design. A program outfitted with a trend-seeking algorithm would find intuitive links between geometries and shapes that differ in structure, but are similar in style.

Humans perceive similar shapes in a manner that goes beyond the shape’s structure and function. You can tell that a bed and a dresser are both Mission style, for example; your CAD program cannot.

That’s the point three computer science professors who presented recently at ACM Siggraph 2015, the August computer graphics conference held in Los Angeles. Rather than grouping geometries within the same class, the researchers’ algorithm analyzes and groups them by perceived similarity, much as humans do.

The researchers are Alla Sheffer, a professor of computer sciences at University of British Columbia; Evangelos Kalogerakis, an assistant professor of computer sciences at University of Massachusetts, Amherst, where researcher Zhaoliang Lun is a computer science Ph.D. student.

To allow an algorithm to translate and group shapes by style rather than grouping them with similar shapes, researchers first needed to quantify in geometric terms what it is that makes geometric elements be perceived as similarly shaped.

Interestingly, the researchers turned to writings about art history for this, where buildings and objects from similar time periods and places are referred to by their recurring shape, proportion, and line. Think about a Baroque building versus an Asian temple for example. You intuitively know the buildings’ shapes, proportions, and lines classify their styles and make one type of building much different than the other. But your CAD software doesn’t understand what makes them different.

But, turning to modern methods, then they went on to crowdsource for answers by asking 2,500 people determine which of two object most resembled an initial object. They gathered 50,000 responses and used them as inputs that measure distance between elements within a shape and the shape’s proportion and lines.

The algorithm can successfully group Asian temples, Byzantine churches, Gothic cathedrals, Russian churches, and Baroque-era buildings by type, Kalogerakis says.

The algorithm has obvious use for architects, who could use CAD programs that run the algorithm to quickly and easily match building features and styles, and for interior designers who might be looking for a certain style sofa to place in a CAD design.

And it can make design engineers jobs easier too, as they could more speedily find the geometries and parts from parts libraries they seek for their designs and assemblies.

In fact, one day a computer graphics program could be able to tell—after a quick search of online fashions shoots–whether that shirt you plan to wear to work fits with current trends. Or not.

Guest blog by Patrick Gannon

There are various methods for top down modeling in CAD design. From a high level, this is the use of a central file to define an overall design. This central file can come in the form of an assembly, part, or a design table (excel file). Top down methods allow an engineer or designer to control the design in one location. Instead of needing to open all files within the design to perform updates, all dimensions can be changed in the one file, updating the entire assembly with ease. In contrast, when using the bottom up method, you model each component of the design independently. Matching features can be verified in the assembly model once the parts are inserted, but changes need to be made to each part individually.

Assembly
The use of an assembly file as your central file involves the creation of adaptive parts within the assembly file. A blank part can be created within the assembly—and references from the assembly used to create the sketches that define the features in the part files. The workflow is relatively simple, but this method can create complex relationships. This is particularly troublesome if you need to change components used as references in other components. The result can be unwanted crashes of the features or the software itself.

Parts
Part files can be used in a couple of different ways. Two of the main methods using part files are skeletal modeling and multi-body modeling.

Skeletal model
The part file in the skeletal modeling method contains all the base geometry (sketches, work features, and parameters) needed to define the design. The file is used as a reference within the individual components, keeping the relationship between the parts of the overall design. The skeleton can be derived into each part file contained within the design. In this way, any the sketches, work features, and parameters contained in the skeleton (see Figure 1) can be used in the part file.

Multi-body part
The multi-body method goes a little bit further. Instead of just the base geometry (as in the skeletal model), the multi-body model can contain all the ‘physical’ geometry of the design components. This allows matching features within an assembly to reference the same sketches (see Figure 2). The geometry is no longer created in separate files. It is created using separate bodies within a single part file, eliminating the need to create multiple files then referencing specific features within that file. Instead, the process is more automated within the multi-body part to create the assembly from the separate bodies.

Design table
A design table is used to define parameters within files. The benefit of the external spreadsheet file is the ability to reference it in multiple files as well as the abilities within the spreadsheet itself. You can manage complex functions to define relationships between dimensions within the parts. If you know that the length of a gusset (Dimension A) within the design will always be 1/8” shorter than the form it fits within (Dimension B) to allow for welding tolerances, you can define Dimension A as [Dimension B-1/8). This is clearly a simple example, but succeeds in demonstrating the point.

 

By Bruce Jenkins, President, Ora Research

Simulation-based design exploration and optimization studies often produce large data sets for which it is useful to have software tools for post-processing the data—and presenting it in ways that facilitate visual discernment of patterns and trends in the data. Visual data mining and analytics tools allow plots and tables to be viewed, queried and operated on to better understand the design space, explore design sensitivities, visualize correlations and investigate tradeoffs. Many design space exploration and design optimization software products include these capabilities to a greater or lesser degree, as do most mainstream CAE product lines. At the same time, some of the best regarded technology comes from developers focused exclusively on this area.

One highly capable example is Tecplot Chorus from Tecplot Inc., which lets engineers and analysts explore design spaces in a unified environment that includes integrated CFD post-processing, field and metadata management, and an analytics tool. The software is designed to help users manage and analyze collections of CFD simulations, evaluating overall system performance by visually comparing tens or thousands of simulation cases in a single view. Customer use cases include design studies to modify the wake behind Formula racing cars to make passing easier, airframe design and optimization for a supersonic UAV, and others.

The company terms Chorus a “simulation analytics” tool that provides a framework for managing CFD projects requiring multiple simulation cases with tools to evaluate the resulting metadata. Tecplot usefully describes simulation analytics as “the application of visualization, data management, statistics, and data mining to related collections of datasets generated by computer-aided engineering (CAE) codes. In particular, simulation analytics involves the coupled analysis of the detailed field data and the associated meta-data for a related collection of datasets.”

Another respected product with a long history in this area is EnSight from CEI (Computational Engineering International) Inc., for visualizing, analyzing and communicating data from computer simulations and/or experiments. EnSight is used for CSM (computational structural mechanics such as FEA and crash), CFD and other CAE processes in automotive, aerospace, defense, combustion, energy production, high-tech manufacturing and other markets that require very high precision in computer-based physics modeling.

A recent, innovative entry is LIONsolver from LIONlab and Reactive Search srl. An integrated software package for data mining, business intelligence, analytics and modeling, LIONsolver originated from the founders’ research into “reactive search optimization” using self-tuning search schemes. Designed to let users build and visualize models to improve engineering and business processes, the software aids and enables decision-making based on data and quantitative models. Its architecture supports interactive multi-objective optimization, with a user interface for visualizing results and facilitating the solution analysis and decision-making process.

LION (for “machine Learning and Intelligent OptimizatioN”) Laboratory says its mission is to “foster research and development in intelligent optimization and reactive search optimization (RSO) techniques for solving relevant problems arising in different application areas, including marketing automation and e-commerce, telecommunication networks, ICT, mobile services, big data, cost management, social networks, clustering and pattern recognition in bio-informatics.” LIONoso is a version of the software available for nonprofit research and academic use.

Kenesto is looking to act as the Google Drive, Box, and Dropbox of the CAD world, Mike Payne, chief executive officer, told us. While cloud-storage solutions like Dropbox are seeing an explosion in popularity. But the typical cloud storage solutions aren’t ideal for CAD file management, Payne said.

Those who use CAD need a solution customized to handle continually updated CAD files and their multiple users, he said. The company’s Kenesto Drive stores CAD information and files online, which various, approved users can access.

The offering rivals other services like Autodesk’s A360 software, which allows design, engineering, and project teams to work collaboratively via a cloud-based centralized platform, and Onshape, a CAD system that exists entirely in the cloud and can also be accessed by multiple users.

Kenesto updated its drive this month to include features found on a company’s shared local area network (LAN) drive. These include capabilities Payne dubbed “work-safety features,” such as a feature that locks an in-use document so another same-time user can’t change it.

Engineering and design companies find several advantages when it comes to housing CAD files online, though the method also has its downside. Chief among the advantages: storage. Like popular offerings such as Dropbox, cloud CAD providers offer a great deal of capacity and as well as low-cost upgrades for even more.

Other advantages include the capability to access data over any Internet connection, including from mobile devices.

But cloud storage does carry some drawbacks. Engineers could lose access to their own data, should engineering companies choose not to renew or the provider go out of business. Should that happen, the engineering company could lose access to important legacy information and even to data currently in the works.

Of course with online storage comes online security concerns, though companies that offer cloud storage take great measures to secure information.

The Kenesto update makes cloud storage ever-more desirable to engineering firms that wish to have collaborative CAD file available but that want to ensure only the latest version is in use and that changes can’t be made to it by unauthorized users.

Overall, look for in-the-cloud CAD and CAD storage options to become more readily available in the same way services like Flickr and Google Drive have become widely available and popular in recent years.

By Bruce Jenkins, President, Ora Research

Design optimization is a powerful technology for automating the search for solutions to engineering problems. But before moving to optimize a chosen design, it can be useful to employ design space exploration—a family of quantitative methods that help engineers gain a better, more complete understanding of a new product’s potential “design space” by discovering which design variables will have the greatest impact on the product’s performance.

The numerical methods that underpin design space exploration have been long known—and sometimes applied, when the attendant costs in expertise, time and labor could be justified. What’s changing now is the rapidly maturing generation of software tools transforming these powerful but formerly difficult-to-use methods into practical everyday engineering aids.

Before diving into how design space exploration works, how does it relate to design optimization in a product development project? Dr. Chris Mattson, director of BYU’s Design Exploration Research Lab, offers this perspective: “Design optimization depends on a well-posed optimization problem formulation, which generally includes (i) a well-defined objective function, (ii) inequality and equality constraints, and (iii) the expression of stakeholder preference, all of which are likely to be multidisciplinary in nature. In an arguably real way, such a problem formulation predefines the optimum solution, thereby allowing the mathematical rigor of the optimization to lead to the optimum design by an iterative, computational search.”

Mattson continues: “Design exploration, on the other hand, assumes that the optimal design is initially unknown and initially uncharacterizable. The process of design exploration discovers design conditions and little by little (often through some form of experimentation) characterizes what an optimal design looks like. Once this is known, the final solution can then be found through a convergent design optimization algorithm.”

The essential quantitative method for design space exploration is design-of-experiment (DOE) studies. In a DOE study, an analysis model is automatically evaluated multiple times, with the design variables set to different values in each iteration. The results identify which variable(s) affect the design the most, and which least. This information allows variables that are not important to be ignored in subsequent phases of the design process, or set to values that are most convenient or least costly.

Concretely, a designed experiment is a structured set of tests of a system or process. Integral to a designed experiment are response(s), factor(s) and a model.

• A response is a measurable result—fuel mileage (automotive), deposition rate (semiconductor), reaction yield (chemical process).
• A factor is any variable that the experimenter judges may affect a response of interest. Common factor types include continuous (may take any value on an interval; e.g., octane rating), categorical (having a discrete number of levels; e.g., a specific company or brand) and blocking (categorical, but not generally reproducible; e.g., automobile driver-to-driver variability).
• A model is a mathematical surrogate for the system or process.
• The experiment consists of exercising the model across some range of values assigned to the defined factors.

In deciding what values to use—more precisely, in deciding on a statistical strategy for selecting values—the goal is to achieve coverage of the design space that will yield maximum information about its characteristics with least experimental (computational) effort, and with confidence that the set of points sampled gives a representative picture of the entire design space.

Numerous statistical sampling methods exist to do this. Which method to use depends on the nature of the problem being studied, and on the resources available—time, computational capacity, and how much is already known about the problem.

The results of a DOE sampling process are then used to generate an approximate model of the system being studied, called a response surface model (RSM). The RSM is generated by interpolating between the discrete DOE results to create a continuous surface map or model. The RSM is a convenient and efficient tool for visualizing the design space, examining relationships among design variables and their effects on key responses, and rapidly evaluating design alternatives—all without the need to perform additional expensive CAE evaluations or experiments.

Here are some of the leading design space exploration software solutions available today:

• Altair HyperStudy
• ANSYS DesignXplorer
• CD-adapco STAR-CCM+ /Enabling Optimate
• DATADVANCE pSeven
• DecisionVis ExplorerDV
• Dynamic Design Solutions FEMtools Optimization
• Dynardo optiSLang
• eArtius Pareto Explorer
• ESTECO modeFRONTIER
• Exa PowerFLOW Optimization Solution
• FRIENDSHIP SYSTEMS CAESES/FRIENDSHIP-Framework
• FunctionBay RecurDyn/AutoDesign
• iChrome Nexus
• InModelia Neuro Pex
• MSC Nastran Multi-run & Design Space Exploration
• Noesis Solutions Optimus
• OptiY GmbH OptiY
• Phoenix Integration ModelCenter
• PIDOTECH PIAnO
• PTC Creo BMX (Behavioral Modeling Extension)
• Red Cedar Technology HEEDS MDO
• Sigma Tech IOSO
• SIMULIA Isight
• Vanderplaats Research & Development VisualDOC

By Bruce Jenkins, President, Ora Research

Topology optimization and its seductive biomorphic shapes are what many think of when they hear the term “optimization” in engineering design. But topology optimization is just one kind of structural optimization—and that, in turn, is only one of five broad classes of optimization technology available to engineers today. Equally if not more valuable, depending on the nature of the design problem, are parameter optimization, multidisciplinary optimization (MDO), multi-objective or Pareto optimization, and robustness-and-reliability optimization.

The power of all these numerical methods is their ability to rationally and rapidly search through design alternatives for the best possible design(s). Parameters in a design that can be varied to search for a “best” design are called design variables. Given these variables, design can be structured as a process of finding the minimum or maximum of some attribute, which is termed the objective function. For a design to be acceptable, it must also satisfy certain requirements, or design constraints. Optimization is the process of automatically changing the design variables to identify the minimum or maximum of the objective function while satisfying the design constraints.

Here’s how these methods work, and what kinds of problems they help engineers solve:

Structural optimization is optimization of a structure’s shape, size, topology, topometry or topography to satisfy operating limits imposed on the response of the structure, and by further limits on the values that the structural parameters can assume. Structural optimization methods apply optimization algorithms to solve structural problems by means of finite element analysis.

Shape optimization means optimizing structural shapes by adjusting the surface shape of a 2D or 3D solid to minimize volume while satisfying stress and/or displacement constraints (generically termed a cost functional).

Size optimization consists of modifying size-related properties of structural elements such as shell thickness, beam cross-sectional properties, spring stiffness and mass to solve the optimization problem.

Topology optimization denotes optimizing material layout within a given physical design volume, for a given set of loads and boundary conditions, so that the resulting layout meets prescribed performance targets. This is often used to identify a conceptual design that best meets specified design requirements, which is then refined for performance and manufacturability. It frequently yields biomorphic-appearing shapes best suited to additive manufacturing methods, before being modified for production by conventional subtractive manufacturing.

Topometry optimization is similar to topology optimization but applied to 2D elements, with the variables restricted to the element-wise thicknesses.

Topography optimization, like topometry optimization, is applicable only to 2D or shell elements, and aims at finding the optimum bead pattern in a component.

Parameter optimization, in some respects a more generalized version of structural optimization, is a procedure for finding values for any parameter(s) in a design—not just structural parameters—that are optimal in some defined respect, such as minimization of a specified objective function over a defined data set.

Multidisciplinary optimization (MDO) incorporates all relevant disciplines—structural (linear or nonlinear, static or dynamic, bulk materials or composites), fluid, thermal, acoustic, NVH, multibody dynamics, or any combination—simultaneously in an optimization problem. By exploiting interactions among disciplines, MDO can identify design solutions that are superior to those arrived at by optimizing each discipline sequentially, with substantially less expenditure of engineering time and labor.

Multi-objective or Pareto optimization is a method for numerically addressing the fact that real-world optimization problems usually have more than one objective, and these objectives often conflict or compete with one another. For example, in optimizing a structural design, the desired design will be both lightweight and rigid. Because these two objectives conflict, a tradeoff must be made: there will be one design that is lightest, another design that is stiffest, and an infinite number of possible designs that are some compromise of weight and stiffness. The set of tradeoff designs that cannot be improved on according to one criterion without harming another criterion is called the Pareto set, and the curve plotting the Pareto set is called the Pareto frontier. Once the Pareto frontier has been identified, the action of comparing these various Pareto-optimal solutions with one another in order to choose the preferred solution is based on exogenous factors (outside the computer model), and is carried out by human decision-makers.

Robustness and reliability optimization are methods for managing the fact that product designs are nominal, while manufacturing and operating conditions are real-world. Finite geometric tolerances, variations in material properties, uncertainty in loading conditions, and other variances encountered by a product either in production or in service can cause it to perform slightly differently from its nominal, as-designed values. Therefore, robustness and reliability as design objectives beyond the nominal design are often desirable. Performance of robust and reliable designs is less affected by these expected variations, and remains at or above specified acceptable levels in all conditions. To evaluate the robustness and reliability of a design during simulation, its variables and system inputs are made stochastic by being defined in terms of both mean value and a statistical distribution function. The resulting system performance characteristics are then measured in terms of a mean value and its variance.

Here are some of the many optimization software choices available today:

• Altair HyperStudy, OptiStruct, solidThinking Inspire
• ANSYS Adjoint Solver, Optimetrics
• Autodesk Optimization for Inventor
• CD-adapco STAR-CCM+ /Enabling Optimate+
• Cenaero Minamo
• Collier Research HyperSizer
• COMSOL Multiphysics Optimization
• Concepts NREC TurboOPT II
• DATADVANCE MACROS, pSeven
• DecisionVis ExplorerDV
• Dynamic Design Solutions FEMtools Optimization
• Dynardo optiSLang
• ESI Group Virtual Performance Solution Optimization
• ESTECO modeFRONTIER
• Exa PowerFLOW Optimization Solution
• FEA-Opt SmartDO
• FRIENDSHIP SYSTEMS CAESES/FRIENDSHIP-Framework
• FunctionBay RecurDyn/AutoDesign
• iChrome Nexus
• LIONlab LIONsolver
• LSTC LS-OPT
• MSC Nastran Design Optimization
• NISA Software CSIL NISAOPT
• Noesis Solutions Optimus
• Optimal Solutions Sculptor
• OptiY GmbH OptiY
• Phoenix Integration ModelCenter
• PIDOTECH PIAnO
• PTC Creo BMX (Behavioral Modeling Extension)
• Quint OPTISHAPE-TS
• RBF Morph
• Red Cedar Technology (a CD-adapco company) HEEDS MDO, HEEDS NP
• Siemens PLM NX Nastran Optimization, Femap with NX Nastran Optimization, LMS Virtual.Lab Optimization
• Sigma Tech IOSO
• SIMULIA Isight, SEE, Tosca
• SolidWorks Simulation Structural Optimization
• Vanderplaats R&D GENESIS, DOT, BIGDOT, VisualDOC
• Virtualpyxis Virtual.PYXIS
• Within Technologies (an Autodesk company) Enhance

By Jean Thilmany

Today’s engineers are intrigued by CAD in the cloud but they seem to be letting others jump on the technology before they act.

More companies plan to adopt cloud-based CAD software within the next five year. But don’t look for this method of CAD delivery to become the industry’s next flavor of the year, according to Business Advantage Group, a B2B research group and consultancy based in the U.K. The group’s recent report looked at some key CAD trends for 2015 and predicted their future adoption.

The number of responding companies using CAD delivery method jumped only 1% between 2014 and 2015, from 7% of respondents to 8%.

But 19% of respondents plan to adopt the technology within the next five years. More than 635 CAD worldwide users and decision makers across a range of company sizes and industries (including architecture, building information maintenance, and manufacturing sectores) took part in the survey.

Why the interest? The companies looking at cloud-based CAD say they’re intrigued by its mobility and by the ease of updating the software. They’d also like to use it to reduce CAD costs and increase storage capacity.

More vendors, from Autodesk to Onshape, are making software available in the cloud (method of delivery is also called software-as-a-service, or SAAS).

Though the survey doesn’t tell us, smaller companies could be turning to the cloud to cut licensing costs.

Those companies could find the mobility, the reduced costs, and the increased storage they seek, but if CAD-in-the-cloud doesn’t come without drawbacks, more than 19% of companies would be considering adoption down the road.

The ever-growing options for cloud-based CAD market means companies must be particularly savvy at determining the costs, benefits, and return-on-investment they’re likely to see from the cloud. Under the cloud model, companies don’t own the CAD software and aren’t responsible for upgrades or maintenance. Instead, they subscribe to a CAD system located on a vendor’s servers.

When comparing the return-on-investment and the total cost of ownership of cloud to the on-premise model, companies must consider the costs, benefits, flexibility, and the risks of cloud against on-premise technology (the type the company owns outright). Problem is, the comparison isn’t apples-to-apples, which makes it difficult to arrive at a bottom-line savings, one way of the other.

A manufacturer might cut costs through doing away the cost of buying and licensing hardware but the cloud application does necessitate a recurring subscription fee.

And the enterprise will eventually own on-site infrastructure outright; they’ll never own the cloud-based CAD system. They essentially rent access to that system, while paying for bandwidth and security, accessibility and mobility.

But, just to throw another wrench into the works, any licensed CAD system will likely be due for another expensive infrastructure upgrade in about five years.

Any company’s mileage will vary of course. But, as the survey demonstrates, CAD-in-the cloud isn’t set to take over the industry just yet.