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MathWorks adds design space exploration to Simulink Design Optimization

October 11, 2016 By Mike Santora Leave a Comment

By Bruce Jenkins, President, Ora Research

Last month’s Release 2016a of MathWorks’ Simulink Design Optimization software includes a new sensitivity analysis tool to support design space exploration. The tool lets design engineers interactively conduct design of experiments (DOE) and Monte Carlo simulations of Simulink models.

brucejenkins_blog_20160oct-no2_image
Simulink Design Optimization. Source: MathWorks

What is design space exploration?

The most successful engineering projects begin with discovery—conceiving a rich array of ideas to solve a problem or address a need—then move on to methodically explore which design candidates are most promising for development and refinement. But the power of such discovery and exploration is too often sacrificed to schedule pressures and resource constraints, compounded by digital toolset gaps and limitations. The result is familiar: engineers conceive two or three design alternatives, then rely on intuition, best guesses and handbook formulas to choose one that looks reasonably promising and not too risky to implement—without really knowing whether it’s the best, most cost-effective or most robust solution attainable.

An emerging answer to this quandary is design space exploration—both a family of methods and a rapidly evolving category of software tools that are beginning to radically advance the capabilities of engineers and multidisciplinary engineering teams to discover an array of feasible design concepts early; quickly and fluently evaluate sensitivities, variants and tradeoffs; then select the best design concept and optimize it.

Design space exploration lets engineers systematically and automatically investigate very large numbers of design alternatives in order to identify those with the most optimal performance parameters. Many of the quantitative and algorithmic methods that underpin design space exploration have been long known—and sometimes applied, in cases where the attendant costs in expertise, time and labor could be justified. What’s changing now is the way fresh software technologies, such as the new sensitivity analysis tool in Simulink Design Optimization, are transforming those powerful but formerly difficult-to-apply methods into practical everyday engineering aids.

Design exploration vs. design optimization

Dr. Chris Mattson, director of Brigham Young University’s Design Exploration Research Lab, explains how design exploration and design optimization relate to each other, and how they differ:

“Design exploration is a particular way of arriving at an optimal design solution. To be formal, design exploration is the human-driven, often computer-assisted, divergent/convergent process used to evolve and investigate multidisciplinary design space with the intent of design discovery and to inform decision making throughout the design process.

“The essential difference between design optimization and design exploration is the method for characterizing the outcome. Design optimization strategies have two distinct parts; formulate and converge. Here it is assumed that the problem can be formulated before the search and convergence begins. Design exploration strategies, on the other hand, are based on the belief that the problem formulation evolves during the process of searching and converging, thus ultimately leading to a more informed optimal solution. In this way, design exploration is both divergent and convergent.”

What does this mean for how the problem is formulated and solved? Mattson continues:

“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.

“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.”

Simulink Design Optimization

Simulink is a block diagram environment for multidomain simulation and model-based design that supports simulation, automatic code generation, and continuous test and verification of embedded systems. Within that environment, Simulink Design Optimization provides functions, interactive tools, and blocks for analyzing and tuning model parameters. Users can determine the model’s sensitivity, fit the model to test data, and tune it to meet requirements. Through the new Monte Carlo simulation and DOE capabilities, users can explore their design space and calculate parameter influence on model behavior.

Simulink Design Optimization also helps users increase model accuracy. It lets them preprocess test data, automatically estimate model parameters such as friction and aerodynamic coefficients, and validate the estimation results.

To improve system performance characteristics such as response time, bandwidth and energy consumption, users can jointly optimize physical plant parameters and algorithmic or controller gains. These parameters can be tuned to meet time-domain and frequency-domain requirements, such as overshoot and phase margin, and custom requirements.

New sensitivity analysis tool

Design engineers frequently need to determine how changes to the parameters in their model will impact the product’s behavior, MathWorks explains. By identifying which parameters have the greatest influence on product performance attributes such as fuel efficiency, engineers can gain confidence that their design meets the specified requirements. The new sensitivity analysis tool performs Monte Carlo simulations, which enable the exploration of a large design space. The tool lets users interactively specify multiple parameter variations, incorporate multiple standard and custom design requirements, and analyze the results of these simulations both graphically and quantitatively.

The results of sensitivity analysis can be used to directly influence the design, as well as improve the performance of numerical optimization tasks such as fitting models to test data and tuning models to meet design requirements. Two other Simulink toolsets, called Fast Restart and Parallel Computing Toolbox, can help speed up the sensitivity analysis tool’s performance.

“Growing design complexity is creating increasingly large models,” said MathWorks design automation director Paul Barnard. “To maintain model accuracy, engineers are challenged with identifying which model parameters impact behavior the most. Now, engineers can use Simulink Design Optimization to determine model sensitivity, fit the model to test data, and tune it to meet requirements.”

Ora Research
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ESI Pro-SiVIC 2016 models how sensors perceive scenes and smart products make decisions

August 29, 2016 By Marisa Martin Leave a Comment

By Bruce Jenkins, President, Ora Research

New tools for development of Advanced Driver Assistance Systems (ADAS) and autonomous (self-driving) vehicles are highlights of Pro-SiVIC 2016, ESI Group’s latest release of the sensor simulation platform it acquired last year along with the software’s developer, CIVITEC.

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Realistic 3D scene of vehicles driving around a city: ESI Pro-SiVIC lets engineers model how sensors perceive scenes and how smart products make decisions. Source: ESI Group

Targeted primarily at transportation industries, ESI Pro-SiVIC lets engineering organizations virtually test the operational performance of the various perception systems on board a ground vehicle or aircraft design. By helping engineers build realistic, real-life 3D scenarios and experience them interactively in real time, Pro-SiVIC is intended to reduce or eliminate the need for physical prototypes. The software models environmental factors that influence sensor performance such as lighting conditions, weather, and other vehicles sharing the road. The goal is to let users quickly and precisely study the performance of embedded systems in both typical and critical use cases, to ensure the product will be safe and reliable in operation.

Accelerating design and prototyping of embedded control and security systems

Pro-SiVIC is the result of ten years’ research and development by the French Institute of Science, Transport Technology and Network (IFSTTAR). CIVITEC is an IFSTTAR spinoff formed in 2009 to productize this R&D, together with IFSTTAR’s know-how in perception sensor simulation and algorithmic development.

Upon acquiring CIVITEC in March 2015, ESI Group chairman and CEO Alain de Rouvray said, “This new expertise of assistance to human perception, coupled with the excellent IFSTTAR partnership, provides opportunity to take into account the interactions of a vehicle, or any other industrial product, with its scalable immersive environment. Once integrated into digital 3D modeling, it will enable dramatically accelerated design and prototyping of embedded control and security systems and thereby strengthen the value of our global solutions in virtual prototyping.”

de Rouvray continued, “For ESI Group’s industrial partners, Advanced Driver Assistance Systems (ADAS) present a major technological challenge, as efficiency must be built upon the quality of interaction between digital modeling and human perception. The growing requirements in terms of active safety make it important and urgent to integrate ADAS systems in virtual prototyping, complementing the existing constraints of passive safety during product development.”

Integrating sensor models based on cameras, radar, LIDAR, ultrasonic sensors, GPS, more

Pro-SiVIC helps engineering and manufacturing organizations develop perception assistance systems from the design phase to final testing. “Implementation of such modules is highly complex, as it requires 3D modeling of ultra-realistic environment conditions, digitally transcribed using sensor simulation, and wrapped in an optimized interface that improves operators’ perception,” ESI notes. “The man-to-machine interface must provide the operator, such as a vehicle driver, with the best information available to enable him or her to make better decisions.” ESI observes that perception assistance systems are critical to the deployment of active safety systems, today considered crucial in the automotive and aircraft industries.

Pro-SiVIC model of vehicle and scene from occupant’s viewpoint. Source: ESI Group
Pro-SiVIC model of vehicle and scene from occupant’s viewpoint. Source: ESI Group

The latest release, version 2016, addresses sensor specialists, ADAS designers, and ADAS integration and validation teams. To support their daily work, Pro-SiVIC integrates sensor models based on a wide range of technologies including cameras, radar, LIDAR (laser scanners), ultrasonic sensors, GPS, odometers and communications devices. This makes the solution suitable for applications in industries that use sensing for systems command and control including automotive, aeronautics and marine. Sensors can be integrated into realistic 3D scenes; for automakers, for example, Pro-SiVIC provides environment catalogs containing representations of various road types (urban, highway, countryside), traffic signs and lane markings.

Pro-SiVIC 2016 introduces new radar sensor models that not only cover the functional aspect of the sensors but also provide detailed modeling of antenna characteristics and their impact on performance and on-board processing, plus the characteristics of radar targets such as radar cross-sections. These options result from the ability to couple Pro-SiVIC with ESI’s computational electromagnetics solution, CEM One.

ESI reports that Business France and BPI France, two leading organizations that promote international development of the French economy and foreign investment, have chosen Pro-SiVIC as one of eight French technologies for their program “Ubimobility—Connected Cars France,” aimed at helping French companies compete in the North American autonomous vehicle market.

Ora Research
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ANSYS, GE partner to bring simulation to the Industrial IoT

August 16, 2016 By Marisa Martin Leave a Comment

By Bruce Jenkins, President, Ora Research

A top CAE vendor has partnered with a world industrial giant to help engineering and manufacturing organizations capitalize on the Industrial Internet of Things.

Part of the recent agreement between ANSYS and General Electric expands GE’s use of ANSYS engineering simulation solutions to accommodate GE’s 2015 acquisition of Alstom’s Power and Grid businesses. But of broader import is what the deal will do to extend simulation beyond product development into operations—a key aspect of Predix, the new “Industrial Internet platform” from GE. Predix is an operating system and platform for building applications that connect to industrial assets, collect and analyze data, and deliver real-time insights for optimizing industrial infrastructure and operations.

unnamed-1
Source: GE

GE characterizes Predix as “the world’s only industrial cloud offering designed specifically for industrial data and analytics across such industries as aviation, transportation, oil and gas, and healthcare. Organizations use this platform to create innovative Industrial Internet applications that turn real-time operational data into insights for better and faster decision-making while maximizing machine efficiency.”

Predix-based simulation-as-a-service apps will help analyze real-world performance of smart machines to better predict future performance

Leveraging its broad and deep portfolio of engineering simulation software, ANSYS will collaborate with GE Power Engineering to pilot new simulation-as-a-service applications built on Predix. These applications will help companies analyze the performance of smart machines in real-world operating conditions, then make confident predictions about their future performance. ANSYS explains the benefits: “Physics-based simulation with big data analytics and industrial devices augmented with embedded intelligence can reduce risk, avoid unplanned downtime and speed up new product development.”

Source: GE
Source: GE

The collaboration follows ANSYS’ announcement last September that it joined GE’s Predix Early Adopter Partner Program, through which GE provides training and co-development support for businesses getting started with Predix. The program is open to ISVs (independent software vendors) creating apps and services powered by Predix, technology partners creating Predix-ready devices and solutions, and systems integrators and consultants developing Predix-certified services.

Extending engineering simulation to the full product lifecycle

Engineering simulation software has traditionally been used in the design phase, where it has helped drive innovation in product design and development. “With the Internet of Things, simulation is becoming even more essential as advanced products now combine mechanical, fluid, electronics and embedded software,” ANSYS noted. “A complete systems-level model is now necessary to benchmark operational field data analytics with simulated system performance.” ANSYS said its collaboration with GE “will demonstrate the benefits of extending engineering simulation to the full product lifecycle, from initial design to operation and maintenance and back again to design of the next-generation machines.”

By combining machine connectivity with a data lifecycle management platform powered by engineering simulation, ANSYS and GE “will enable organizations to optimally design their products for the Industrial Internet, then take the data being relayed on their performance and use it in the development of the next generation of those products,” ANSYS concluded.

GE also partnered with PTC

This is not GE’s only such alliance with the engineering/manufacturing software industry. Around the same time as ANSYS’ initial announcement last September, GE and PTC unveiled a partnership to deliver a new manufacturing solution within GE’s Brilliant Manufacturing Suite, a set of technologies field-tested and optimized within GE’s own factories to help customers maximize manufacturing production performance through advanced real-time analytics.

The new GE-branded manufacturing solution “leverages the capabilities of PTC’s ThingWorx Industrial Internet of Things application enablement environment,” the companies said. “The result is an industry-hardened solution that features flexible dashboards and powerful data analytics integrated with GE’s software capabilities on the manufacturing plant floor.” The joint solution connects disparate systems from shop floor to ERP, and offers a dashboard and differentiated user experience with consumer-like drag-and-drop capabilities tailored to each user’s role.

All part of the GE Digital transformation

All these moves are connected to GE Digital, an initiative launched last September to bring together all the digital capabilities from across GE into one organization. The company said GE Digital would “integrate GE’s Software Center, the expertise of GE’s global IT and commercial software teams, and the industrial security strength of Wurldtech,” GE’s cybersecurity subsidiary.

Chairman and CEO Jeffrey Immelt explained, “As GE transforms itself to become the world’s premier digital industrial company, this will provide GE’s customers with the best industrial solutions and the software needed to solve real-world problems. It will make GE a digital show site and grow our software and analytics enterprise from $6 billion in 2015 to a top 10 software company by 2020,” when GE expects more than $15 billion in software and solutions revenue driven by Predix scale and internal productivity.

GE Chief Digital Officer Bill Ruh added, “Digital technologies and open-source software have transformed the consumer space in radical ways, but industry has been slow to adopt these new innovations. Now is the time to capitalize on the extraordinary opportunity to transform the industrial landscape by leveraging Predix to collectively build apps, which will reveal exponentially greater value than what we have seen in the consumer space.” GE believes the global industrial app economy could grow to more than $225 billion annually.

Ora Research
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FeatureScript amps up power of Onshape

August 1, 2016 By Marisa Martin Leave a Comment

By Bruce Jenkins, President, Ora Research

Full-cloud CAD pioneer Onshape amped up the power and utility of its software with FeatureScript, a new programming language that lets users create new parametric features that look, feel and behave just like Onshape’s built-in features.

unnamedFeatureScript Curve Pattern. Source: Onshape

This is the same language used by Onshape itself to develop all of its software’s current features—Extrude, Fillet, Shell, Loft and the like. Now available as an open language, FeatureScript lets users create their own built-in parametric features in Onshape.

Under the open-source MIT License, Onshape is also sharing the FeatureScript source code for all of its own features, allowing customers to copy, modify or adapt them as they see fit. New features can be created, and existing features edited, in Onshape’s new Feature Studio, a user-friendly development environment with an editor, in-line help and documentation.

Putting the user in control of feature enhancements

By making both FeatureScript and the source code behind its own features public, Onshape is offering what it characterizes as the first truly customizable parametric CAD feature set. We agree with the company’s assessment that its software is the first professional CAD solution to offer this level of customization to its users.

unnamed-1FeatureScript Lighten. Source: Onshape

Onshape Director of FeatureScript Ilya Baran explains, “This is the first time that a professional CAD system has made the implementation of its parametric features open and extensible. In the past, the only way to change your feature toolbar would be to submit an enhancement request to your CAD vendor and wait forever. And most of those requests are never fulfilled. FeatureScript swings the pendulum back and puts you in control.”

Beyond macro scripting: FeatureScript features are “first-class citizens”

Baran continues, “In traditional desktop-installed CAD systems, it is possible to write add-on or macro features, but they are never as good as the built-in ones. FeatureScript offers the first opportunity to create features that are first-class citizens—as much a part of the system as the ones the development team wrote themselves.”

Onshape describes a few of the many possible uses for FeatureScript:

  • Creating new high-level parametric features that perform complex or customized geometric modeling tasks. The benefit of such custom features is to let users design their products faster than they could with traditional off-the-shelf features.
  • Customizing existing features to suit user preferences for working fast and efficiently—for example, a surface split feature that splits and preserves exactly the pieces that a particular user prefers.
  • Combining existing features into one, such as a drafted filleted pocket.
  • Filling in some current gaps in CAD functionality, such as a customized extrude option, or a particular type of 3D spline curve fitted through points or driven by an equation.
  • Creating surfaces using data from uploaded CSV or other data files.
  • Building specialized patterns such as sinusoidal or other unusual pattern geometries with unique per-instance behavior.
  • Building a specialized toolkit for an individual company’s specific application needs—for example, custom gears, enclosures or connectors that are used over and over again in the company’s products.

Custom features yield up to 30X productivity gains

Onshape founder Jon Hirschtick observes, “For 30 years, feature-based modeling has relied on a limited set of off-the-shelf features. With FeatureScript, we are ushering in a new era of custom parametrics. Our early adopters have proven that with the ability to use custom features that they write or have others write for them, they’re able to significantly speed up their design process.” Early adopters report that FeatureScript features provide as much as 30-fold productivity gains, according to Onshape.

Pointing to still more potential leverage from the new technology, Hirschtick adds, “Customers who develop new features in FeatureScript are free to do with them as they please. Some may wish to sell them or share them with the community. Others might choose to keep their FeatureScript features proprietary as a competitive advantage.”

Ora Research
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Filed Under: News, Onshape Tagged With: oraresearch

Design space exploration: Adoption drivers, adoption constraints, potential adoption accelerators

April 7, 2016 By Mike Santora Leave a Comment

By Bruce Jenkins, President, Ora Research

Design space exploration today is enjoying ever-increasing levels of recognition, adoption and successful application to help solve some the world’s most difficult engineering problems. Nevertheless, there remain significant impediments to the even broader deployment and usage that champions of these tools and methods believe will be possible, and indeed imperative, to meet the product development challenges of the near future.

3DCAD-graphic-Ora_DSE_stack_REVISED-5-5-16

Design space exploration encompasses a family of applications and methods that include design of experiments (DOE), multidisciplinary optimization (MDO), multi-objective (Pareto) optimization, stochastic (robustness and reliability) optimization, and the rich family of structural optimization methods – shape, size, topology, topometry, topography and more. Supported by capabilities for multi-tool integration and simulation process automation, design space exploration is rooted in the domain originally termed “process integration and design optimization,” or PIDO.

What’s new? Why now?

Many of the methods that underpin design space exploration have been long known – and sometimes applied, in cases where the attendant costs in expertise, time and labor could be justified. What’s changing now is the way that fresh software technologies are transforming these powerful but formerly difficult-to-use methods into practical everyday engineering aids.

Why this matters is evident in the fundamental business justification for design space exploration: namely, the ability it confers on engineering teams and organizations to gain more complete, higher-fidelity visibility into product performance earlier in project schedules than was possible or practical with older technologies and approaches.

In essence, it does this by enabling more efficient, effective and revealing application of simulation, analysis and digital prototyping assets – tools, expertise, methods, work processes – to the perennial business drivers for any organization’s investments in those assets:

  • To become more competitive by gaining increased capability to explore, create and innovate.
  • To apply that capability to create better performing products.
  • To improve product quality and reliability – yielding expanded opportunity and customer appeal at the same time as lowered warranty expenses, liability exposure and lifecycle costs.
  • To control or, better yet, reduce product development schedules and budgets by supplanting costly, time-intensive physical testing with digital prototyping, and replacing intuition-based, guess-and-correct engineering practices with systematic, rational, rapid design discovery and evaluation.

New developments driving adoption and impact of design space exploration today

A number of key developments are driving and accelerating the adoption and impact of design space exploration today:

  • Advancing levels of built-in intelligence that let design exploration software choose the best search algorithms and solution methods autonomously, based on the user’s description of the problem in native engineering terms.
  • “Appification” of simulation – the embedment of design exploration and optimization technologies inside easy-to-use, product-specific and customer-specific simulation apps.
  • Full-cloud solutions that are beginning to expand accessibility, affordability and usability of design space exploration.
  • Continued vigorous marketing and sales activity by large CAE vendors that own premier design exploration technology.
  • Mounting pressures on engineering organizations to find new ways to do more with fixed resources, such as complying with the march of automotive CAFE and emissions mandates.

Legacy conditions constraining adoption and impact

Despite the foregoing advances, there remain a number of legacy conditions acting to substantially constrain and retard adoption and impact of design space exploration tools and methods at present:

  • Design exploration and optimization are still not part of the standard work process at enough engineering organizations.
  • The technology remains too often implemented at only the workgroup or department level, instead of as an enterprise competency.
  • Many small software developers offering highly capable technologies continue to struggle under marketing and sales resource constraints.
  • Some PLM vendors have yet to fully embrace design space exploration, fearing it a troublesome complication in an already complex CAE sales process.

Possible future developments that could spur interest and accelerate investment

Several developments possible in the near and intermediate future hold potential to spur interest and accelerate investment in design space exploration:

  • Market-development ripple effects from the growing democratization of topology optimization.
  • More acquisitions of design space exploration software developers by major CAE and/or PLM vendors.
  • New breakthrough technologies coming from startup developers, academic researchers or government R&D labs.

 Ora Research
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Cloud-native CAD will disrupt the PLM platform paradigm

January 13, 2016 By Andrew Zistler Leave a Comment

By Bruce Jenkins, President, Ora Research

An illuminating blog post by Onshape engineering team member Ilya Baran reveals some fundamentals of how the new cloud-native CAD system works: “We are careful to distinguish several types of data: the User Interface (UI) state – e.g., selection, camera view, current tab; the Part Studio definition – e.g., feature list, part names and colors, import data; [and] Regeneration results – the “b-rep” (bodies, faces, edges, etc.), triangles for display, regen errors.”

How do these data types differ? “The UI state generally doesn’t persist (except for things like named views),” Baran writes. “The regeneration results are cached, but they can always be rebuilt from the definition. The Part Studio definition is what we store in the database and that is where collaborative editing happens.”

Then Baran explains something that begins to suggest why we believe Onshape is not only a breakthrough in CAD, but also poised to disrupt the established paradigm for PLM platforms. “For a given Part Studio, at each point in time, the definition is stored as an eternal, immutable object that we internally call a microversion,” he writes. “Whenever the user changes the Part Studio definition (e.g., edits an extrude length, renames a part, or drags a sketch), we do not change an existing microversion, but create a new one to represent this new definition. The new microversion stores a reference to the previous (parent) microversion and the actual definition change. In this way, we store the entire evolution of the Document: this is accessible to the user as the Document history, allowing the user to reliably view and restore any prior state of an Onshape Document.”

Onshape Follow Mode. Source: Onshape
Onshape Follow Mode. Source: Onshape

Next Baran reveals how Onshape is fundamentally different from older-generation engineering software. “Basing Onshape on immutable microversions also makes for a great foundation for other collaboration tools: those we already have, such as the Follow Mode or the Compare tool, as well as those we are developing for the future,” he says. “It also has benefits beyond just collaboration abilities: because old microversions are never modified, data integrity is better preserved, and having a history of changes allows us to debug exactly how a Document came to be when a user has a problem or when we detect a problem through our logs.”

New cloud-native database architecture changes everything

What makes all this possible and practical? Much of the answer lies in Onshape’s being built on MongoDB, one of the new “NoSQL” databases widely used in cloud-native applications, instead of any of the relational database management systems (RDBMS) used in most engineering applications until now. “Relational databases were not designed to cope with the scale and agility challenges that face modern applications,” MongoDB says, “nor were they built to take advantage of the commodity storage and processing power available today.” MongoDB functions as back-end software for Craigslist, eBay, Foursquare, LinkedIn and many other of today’s massively deployed cloud-based services.

Besides being fast, scalable and designed to exploit cloud computing resources, NoSQL databases have a capability called “schema-on-read.” This allows data to be captured, stored and subsequently acted on with almost limitless freedom, without the application developer having to create a schema for the data in advance. Having to create such a schema as the first step in creating a database, a requirement of traditional RDBMS technology, is known as “schema-on-write.”

Joe Pasqua with MarkLogic, another NoSQL database provider, explains the benefits of schema-on-read: “For decades now the database world has been oriented towards the schema-on-write approach. First you define your schema, then you write your data, then you read your data and it comes back in the schema you defined up-front. This approach is so deeply ingrained in our thinking that many people would ask, ‘How else would you do it?’ The answer is schema-on-read. Schema-on-read follows a different sequence – just load the data as-is and apply your own lens to the data when you read it back out.”

What’s the advantage? “More and more these days, data is a shared asset among groups of people with differing roles and differing interests – who want to get different insights from that data,” Pasqua explains. “With schema-on-write, you have to think about all of these constituencies in advance and define a schema that has something for everyone, but isn’t a perfect fit for anyone. When you are talking about huge volumes of data, it just isn’t practical. With schema-on-read you can present data in a schema that is adapted best to the queries being issued. You’re not stuck with a one-size-fits-all schema.”

But that’s not all. “One of the places where projects often go off the rails is when multiple datasets are being consolidated,” Pasqua continues. “With schema-on-write, you have to do an extensive data modeling job and develop an über-schema that covers all of the datasets that you care about. Then you have to think about whether your schema will handle the new datasets that you’ll inevitably want to add later. If you’re lucky enough to get through that process, Murphy will strike again and you’ll be asked to add, change or drop a column (or two or three). With schema-on-read, this upfront modeling exercise disappears.”

“In time, Onshape will be the system of record for all types of data & meta-data”

Those underlying capabilities of Onshape’s database architecture – together with its ability to import, operate on, and archive data from other engineering applications – begin to suggest the true scope and scale of the company’s long-term ambitions and vision. Indeed, it has made no secret of this. Around the time of Onshape’s public unveiling last year, a user posted in its online discussion forum: “Is Onshape intending to develop PLM eventually, or are they going to go the route of partners to provide that? I ask because Onshape is a database system with the correct platform to seemingly handle this functionality.”

In reply, Steve Hess from Onshape’s UX/PD team posted: “As you know Onshape was built with data management in mind. The data management features of Onshape are at the core of the product and will become more exposed as Onshape matures. In time, Onshape will be the system of record for all types of data & meta-data…The data stored in Onshape will be visible and accessible to your other enterprise systems.” (Our emphasis.)

Already, the ways in which Onshape lets multiple users work simultaneously on the same design serve to eliminate many problems that established PDM and PLM providers have spent years “solving” — and at the same time perpetuating, because of the database architectures their systems were built on. As Onshape founder and chairman Jon Hirschtick told us, “For starters we eliminate 50-60% of all the functions of traditional PDM – they simply have no role (copying files, managing directory structures, etc.) in our world.”

Far from being a throwaway line, we think Hirschtick’s phrase “for starters” is in dead earnest. To date, Onshape’s best-understood benefits are how it removes many of the headaches and costs of locally installed software, and of CAD collaboration and data management. But we believe its larger goal is to evolve a next-generation product development platform that “in time,” as Hess declared, “will be the system of record for all types of data and meta-data.”

Onshape’s ability to do this is grounded in two key benefits of schema-on-read. First, it “gives you massive flexibility over how the data can be consumed,” explains Tom Deutsch, Solution CTO with IBM, and second, “your raw/atomic data can be stored for reference and consumption years into the future.” These position Onshape to extend its radical simplification of CAD collaboration and data management to more and more areas of PLM where users have had enough of complexity and expense, and are ready for something new.

Ora Research

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Filed Under: CAD Industry News Tagged With: oraresearch

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