PTC

ThingWorx 8.5 coming in late summer 2019

June 21, 2019 By wpengine Leave a Comment

ThingWorx 8.5 combines new capabilities with domain-specific solutions to serve the digital transformation needs of industrial enterprises worldwide.

Among the new and enhanced capabilities are tools that simplify the composition and deployment of solutions. Customers will be able to configure applications faster and deploy them across multiple sites and geographies. For those who want more readymade solutions, ThingWorx also offers pre-built, pre-configured capabilities for engineering, manufacturing, and service. This latest version of ThingWorx brings enhanced integration with Microsoft Azure IoT and includes additional tools for rolling out solutions at an enterprise scale.

The new ThingWorx Navigate Digital Change Management Application enables internal and external users to actively participate in change management. The series of ThingWorx Navigate Contribute Apps will expand on NavigateView suite, which makes information easily available and facilitates collaboration across the enterprise. Powered by the digital thread, engineers have real-time access to connected product data in ThingWorx to drive the design of next-generation products.

Manufacturing
ThingWorx 8.5 is now compatible with Rockwell Automation’s FactoryTalk Analytics. Additionally, PTC will be releasing ThingWorx Kepware Edge to provide flexible deployment of reliable and secure connectivity for dispersed equipment.

Service
PTC’s service solution suite enables service organizations to transform from cost centers to profit centers and drive product and service innovation through new IIoT-enabled business models. The service optimization solution allows companies to improve margins by minimizing truck rolls and increasing first time-fix rates. With ThingWorx 8.5, digital
service delivery is enhanced through a deepened integration with Microsoft Azure,
enabling manufacturers to now use the ThingWorx Software Content Management App
with the Azure IoT Hub and Azure IoT Edge.

PTC
ptc.com

PTC adds AI and generative design to its CAD portfolio with acquisition of Frustum

November 20, 2018 By Leslie Langnau Leave a Comment

PTC announced that it has acquired Frustum Inc., an advanced generative design software company, for approximately $70 million. Frustum’s technology for generative design leverages artificial intelligence (AI) to generate design options. PTC says it is a transformative addition to the PTC Creo portfolio.

Based in Boulder, Colorado, Frustum offers patented desktop and cloud-based engineering software that enables designers and engineers to go beyond the limits of their personal experience by leveraging powerful AI capabilities that guide the discovery of high-performance, next-generation product designs.

“PTC is pushing the boundaries of innovation with this acquisition,” said Jim Heppelmann, president and CEO, PTC. “Creo is core to PTC’s overall strategy, and the embedded capabilities from ANSYS and, later, Frustum will elevate Creo to a leading position in the world of design and simulation. With breakthrough new technologies such as AR/VR, high-performance computing, IoT, AI, and additive manufacturing entering the picture, the CAD industry is going through a renaissance period, and PTC is committed to leading the way.”

Frustum complements PTC’s strategic relationship with ANSYS, which was announced at LiveWorx in June 2018, and will bring analysis upstream to the very start of the design process. With embedded Frustum and ANSYS capabilities, Creo will be able to recommend design approaches using generative design, guide the user through the iterative design process using ANSYS Discovery Live, and ultimately validate the full product design at scale using the broader ANSYS Discovery suite. With these capabilities embedded in Creo, engineers will have unmatched capabilities to rapidly drive product innovation.

“This acquisition is a natural step for PTC and its customers,” said Jeff Hojlo, program director, product innovation, IDC. “AI and machine learning (ML) are widely discussed as two of the most impactful technologies of the future. For design, engineering, and R&D, the potential positive impacts of complementing the development process with AI and ML are astounding: lowering cost of quality (which is currently 20-25 percent of annual revenue at the average manufacturer), improving product success rate (which remains very low with more than 80 percent of products failing), and improving time to market and time to revenue by meeting customer needs accurately the first time.”

PTC
www.ptc.com/en

The Self-taught design system

August 22, 2017 By Leslie Langnau Leave a Comment

Just where will artificial intelligence fit in with CAD software? Here’s a look at where developments stand now, and a preview of what might be coming.

Jean Thilmany, Contributing Editor

Artificial intelligence has a place in the future of computer-aided design technology, but right now, the role AI will play isn’t clear. That’s the view of Jon Hirschtick, chief executive officer of Onshape, which makes cloud-based CAD software. While some CAD makers are delving into AI functionality, the marriage of AI and design software is in the early stages, he says.

“AI has great potential, but so far no one has illustrated how it will unfold,”

Hirschtick says in reference to CAD vendors. “I’m not saying developers are not working on ideas.”

CAD makers would be wise to consider how AI may fit into their software’s growth and expansion. AI should be a $16 billion industry by 2022, according to a projection from research firm Markets and Markets.

AI across industries

First, definitions are in order. Many terms have been bandied about of late, particularly in reference to Industry 4.0, which goes by a number of names, including smart factory and connected factory. Technologies like artificial intelligence, machine learning, big data, Internet of things (IOT), and deep learning will come together to help realize Industry 4.0.

Nvidia installed its Drive PX 2 AI supercomputing platform into a signature- green, self-driving racecar that will compete in the Roborace Champsionship, a global autonomous motorsports competition. Image credit Nvidia.

All these technologies are related in that they build upon each other, says Will Ramsey, director of marketing at Nvidia, which designs graphics processing units. The company developed GPU-based deep learning, which uses artificial intelligence to approach problems like cancer detection, weather prediction, and self-driving vehicles. Here’s how Ramsey defines pertinent terms:

“AI is a broad field focused on using computers to do things that require human-level intelligence. It’s been around since the1950s but was little used because it was limited in practical applications.”

“Machine learning enables AI by providing the algorithms that make the machines smarter and thus give AI a way to actually become more intelligent as time goes on.”

Machine learning is what Ramsey calls an approach to AI, meaning a way to use AI for practical applications. The approach uses statistical techniques to construct a model from observed data. It relies on inputs, or what Ramsey calls “extractors” set by the humans programming these machines.

“It’s like the bag-of-word analysis that made spam filters possible,” Ramsey says.

The filters could search for certain words (determined by humans) within messages, then flag those messages as unwanted spam.

Machine learning algorithms can sift through and find insights in large data sets. Combine AI and machine learning and the algorithms become more able to recognize patterns and specific issues, such as—when it comes to something like speech recognition software—accents.

But where does the data used by machine-learning algorithms come from?

Earlier this year, Nvidia revealed a self-driving car powered by its new AI supercomputer, Xavier, which learns to drive by observing a human driver. Nvidia installed the AI in an autonomous Lincoln vehicle to demonstrate its capabilities. Nvidia Drive PX is an open, artificial intelligence-driven, computing system that can be used as the technology platform for automated and autonomous vehicles. Nvidia developed its own self-driving vehicle to showcase the system.
Image credit Nvidia.

“Now with social media, sensors, the internet of things, we have all these data,” Ramsey says. “And we have the challenge of understanding and extracting insights from it.”

His company uses what it calls deep learning, a method that automatically extracts and makes sense of all that information, and continues to learn from it or “learns to think.”

“Using deep learning, the fastest growing segment of AI, computers are now able to learn and recognize patterns from data that were considered too complex for expert written software,” Ramsey says.

What about design?

Hirschtick believes CAD programs will make use of AI, but in a more limited way in the near future, by using information the designer has entered to offer suggestions about design parameters and inputs.

Future programs might offer to the design default values for a shape based on the objects that person has designed in the past. AI would essentially learn what types of products the designer mostly works on and the inputs he or she has regularly used for those products. The suggested values may appear on the user’s screen in a dialog box, Hirschtick says.

“Or AI could offer something like: ‘Gee, I noticed you’ve done pattern of activity several times in a row, do you want that or was that a mistake?’”

And AI could make engineers’ search for needed and necessary parts easier. Hirschtick envisions a program, much like that which appears for Amazon shoppers, in which engineers could type in information about a part they’re searching for “and the program says ‘a lot of times people looking for that part also look at this one,’” he says.

Today’s wind turbines, like this one installed in Traverse City, Mich., can be outfitted with a myriad of sensors and actuators that will return real-time turbine operating information through the Internet of Things. Image credit: bengarrison

In the future, CAD software users may also ask speech technology software, rather than the CAD-company’s tech-support operators, questions about the software and instantly receive a pertinent, helpful response. The natural-language-processing programs that drive these responses learn how best to answer user questions thanks to machine learning technology.

Such speech technology software could aid fast-growing CAD companies that would otherwise need to train a slew of customer-support employees quickly.

Currently, those AI possibilities remain unrealized, says Hirschtick. “Right now no one has demonstrated any particularly compelling idea with AI.”

Making manufacturing inroads

Other CAD vendors may beg to disagree.

Autodesk is already moving to use AI for customer support, teaming with IBM to create Otto, a digital concierge that uses IBM Watson technology to manage customer and partner inquiries, says Gregg Spratto, vice president of operations at Autodesk. “Watson’s natural-language-processing and deep-learning technologies help Otto understand the intent of customer questions.”

To offer Otto initial “training,” as it were, the Autodesk team fed historical data from chat logs, use cases, and forum posts into the program to ensure it could understand and respond to a wide range of customer queries.

Then, as the project expands, Otto will use machine learning to handle increasingly complex customer requests and will scale up as call-volume grows.

Also, last October, Autodesk announced plans to embed an AI modeling engine into its IoT cloud platform, Fusion Connect. The Eureqa engine is from Nutonian, recently acquired by DataRobot.

With the AI engine on board, the IoT platform will be able to predict product failures or design flaws based on how a product or device is presently functioning in the real world, says Bryan Kester, director of IoT at Autodesk

The pairing is natural, as IoT offers continual feedback on how products are performing in the field, in real time. IoT makes use of sensors and actuators attached to a product that send back continuous information on how the product is operating, moment-by-moment, in the field.

Fusion Connect helps gather information from that network of sensors and actuators as well as upon RFID, Wi-Fi, and a range of other communications and monitoring technologies, Kester says.

The information is then analyzed and output in a format useful to engineers, who can use it to find where improvements can be made to existing product designs and to determine how new products could be designed better designed. All this based on present, real-world operation, he adds.

Similarly, PTC plans this year to link its Creo computer-aided design system, to the company’s ThingWorx IoT development platform. Developers use the platform to build and deploy enterprise-level IoT applications, says Paul Sagar, vice president of product management at PTC.

Though it’s not an AI application, after the ThingWorx and Creo interface is complete, engineers will be able to instrument their CAD model with virtual sensors that act in the same manner as the real-world counterparts do; that is, they monitor and report back about particular features of part or system operation.

These virtual sensors can offer more insight into model behavior than the what-if questioning and virtual experimentation engineers now use to explore model performance, Sagar says. The sensors can help answer questions like: is the virtual system running hot in a certain area? Is airflow too high or too low?

Fusion Connect Internet of Things software from Autodesk can help connect factory applications across a number of industrial machines and make sense of information returned from the connected machines. Image credit: Autodesk

With those questions answered, designers can redesign and repeat the process until they’ve optimized the model to meet—perhaps even exceed–specifications, Sagar continues.

AI aids 3D lookup

Introduced last summer, Autodesk’s Design Graph is another machine learning system that helps users manage 3D content, offering Google search-like functionality for 3D models, says Mike Haley, who leads the machine intelligence group at Autodesk.

“Machine learning and artificial intelligence are starting to make the first inroads into daily life, but to our knowledge this is its very first application for industrial design and mechanical engineering,” Haley says.

Design Graph algorithms extract large amounts of 3D design data from an engineering company’s designs. It then creates a catalog by categorizing each component and design using a classification and relationship system. Designers and engineers search across all of their files for a part type, such as a bolt or a bike seat, with the tool returning dozens or hundreds of pertinent options.

So how does machine learning come into play?

The system teaches computers to identify and understand designs based on their inherent characteristics–their shape and structure–rather than by tags or metadata, Haley says.

After all, whoever designed the part originally could label it any of dozens of ways, using full words or abbreviations. Metadata created by people, unless carefully managed, tends to be unreliable, Haley says. With Design Graph, the computer uses its own observations about the 3D geometry contained in every 3D model.

So while some AI capabilities already exist within CAD systems, look for more to come. After all, product design plays a key role in the connected factory and the IoT systems of the future. Without design, there’d be no need for a factory—no matter how connected–to make the products and nothing for IoT to monitor.

Autodesk
Autodesk.com

Nvidia
Nvidia.com

PTC
Ptc.com

Onshape
Onshape.com

PTC, ANSYS to develop platform solution enabling digital simulation for the Industrial IoT

June 13, 2017 By Leslie Langnau Leave a Comment

Bruce Jenkins, Ora Research

PTC and ANSYS partnered to accelerate the industry-wide move to bring engineering modeling and simulation to the Industrial IoT.

Just before the start of LiveWorx 2017, PTC’s annual mega-event around all things IIoT, the two leaders agreed to develop a solution that will let ANSYS engineering simulation technology be rapidly added to applications built on the ThingWorx Industrial IoT platform from PTC. Development of a connector between these two technology platforms will let customers transform raw data into new forms of actionable intelligence, the companies said. The connector will integrate intelligent digital simulation models with products as they exist and operate in the real world, the companies explained. The aim is to open up new opportunities for companies to create value by enabling them to optimize operations and maintenance and to integrate them into their product development processes.

Simulation helps companies understand how products will behave—and may fail—as early as possible in the engineering design cycle. When faced with limited access to historical data on product performance, now engineering organizations can instead leverage simulation models to generate an initial as-designed set of expected product performance outcomes. Those simulation results then serve as a rich source of data for supervised machine learning and predictive modeling. This ongoing connection between real-world performance, predictive simulation and machine learning will help manufacturers make sense of data that can lead to predictive models and a more insightful feedback loop, helping them improve their product design and modeling.

The IIoT is making it possible to create a digital twin that combines real-time data about a physical product with the organization’s digital information about the product, PTC and ANSYS note. Simulating digital twins provides advanced intelligence and insight into a product’s behaviors. The combination of the ThingWorx platform capabilities with ANSYS simulation models will enable companies to deploy powerful applications that can analyze current operating conditions, rapidly identify and diagnose operations issues, predict future operating conditions, and improve product performance. PTC and ANSYS will make the capabilities of this solution available within IIoT applications developed on ThingWorx.

“Obtaining value from the data generated by connected products is one of the primary reasons companies invest in the Industrial IoT,” said Catherine Kniker, chief revenue officer for PTC’s Platform Business. “Simulation technology combined with machine learning can help Industrial IoT solution builders identify and make sense of the data needed to improve product design and performance—and develop next-generation products. We look forward to seeing how solution builders will create new value by introducing our solution with ANSYS, a preferred simulation partner, into their applications.”

“Many of our customers are looking to digital twins to disrupt their industries by drastically lowering their operating and maintenance costs and by marketing their products as optimized services in real time,” said ANSYS general manager Eric Bantegnie. “By leveraging the solution that ANSYS and PTC will bring to the market, our customers will bring powerful capabilities to new Industrial IoT applications. We look forward to demonstrating the value of this solution at LiveWorx17.”

ANSYS
http://www.ansys.com/

PTC
https://www.ptc.com/

LiveWorx 2017
https://www.liveworx.com/

PTC Announces Creo 4.0 for Smarter Design

November 18, 2016 By Leslie Langnau Leave a Comment

PTC (NASDAQ: PTC) announced the release of the latest version of its Creo 4.0 3D CAD software. Creo 4.0 introduces new capabilities for Internet of Things (IoT), additive manufacturing, augmented reality, and model-based definition (MBD). Creo 4.0 aids smarter design with an array of core modeling enhancements and new functions.

With this latest release of Creo, product designers can design smart, connected products and capitalize on new technologies, such as additive manufacturing and augmented reality.

Key enhancements include:

Smart Connected Product Design

For the IoT, Creo 4.0 provides the ability to pull real-world information into the design process. It enables a design for connectivity strategy where developers proactively design products with custom data streams by integrating sensors into the design process.

Said Chad Jackson, Lifecycle Insights, “Connecting Creo’s 3D model with ThingWorx’s sensor model is key, as it allows organizations to virtually prototype sensor placement and emulate data streams without having to build anything physical.”

Additive Manufacturing
Creo 4.0 removes barriers to the efficient design of production parts built with additive manufacturing techniques. It delivers “design for additive manufacturing,” enabling designers to design, optimize, validate, and run a print-check in a single environment. With the ability to create parametrically controlled lattice structures, it helps designers optimize models to meet multiple design objectives or constraints.

Augmented Reality

Creo 4.0 allows for more engaging and informative visual experiences of designs by bringing the digital product into the physical world. Designers can seamlessly reuse CAD data to easily create engaging and informative visual augmented reality experiences of a design with a realistic sense of size, scale, and context.

Model Based Definition

Creo 4.0 enables designers to implement MBD and increase efficiency in product development by reducing dependency on 2D drawings. Designers can reduce errors that result from incorrect, incomplete, or misinterpreted information by guiding and educating designers in the proper application of Geometric Dimensioning and Tolerance (GD&T) information. Creo 4.0 also validates that the GD&T is captured in the 3D CAD model in a fully semantic way, that the model is compliant with ASME and ISO standards, and that it constrains model geometry to enable efficient and error-free downstream use in manufacturing and inspection.

“Realizing the potential of the IoT is not just about getting more product usage data, it means you can use, refine, and analyze that data to design better and smarter,” said Brian Thompson, senior vice president, CAD segment, PTC. “Creo 4.0 helps designers replace assumptions in the design process with real-world data to make better product design decisions, and along with model-based definition, give designers a more complete digital definition of a product. The enhancements in Creo 4.0 not only enable designers to increase productivity, but also help designers leverage the IoT to support their digital engineering journey.”

PTC
www.ptc.com

CAD and the Internet of Things

October 23, 2015 By Jean Thilmany Leave a Comment

The Internet of Things is certainly being hyped, but is it overhyped? We’ll see when the technology becomes mainstream—as business analysts say it will– within a few years’ time. The way Internet of Things (IoT) will become part of designers’ and engineers’ job also remains to be seen. But engineering software maker PTC says there’s a definite role and that PTC itself will be a part of it.

Certainly designers and engineers will be creating the sensors used for IoT as well as ensuring the machines, manufacturing and transportation—and nearly everything else–they design will be IoT ready. Transport for London, for example, the organization that runs the London Underground (pictured), recently announced it’s implementing the IoT to streamline maintenance of the system, improve customer service and boost efficiency by 30 percent over three years..

The IoT calls upon wireless sensors that continuously monitor the operation of machines of all types, no matter where they’re located, and then aggregates the data so it can be broken out and analyzed to discover trends and make conclusions that can’t be drawn from local data or from lag-time feedback.

Management consulting firm Accenture predicts the global Industrial IoT landscape (IoT for industrial use) will be worth $14.2 trillion by 2022. Technology research firm Gartner Inc. forecasts nearly 26 billion devices on the Internet of Things by 2020.

To become linked with IoT, PTC has acquired ThingWorx, an IoT platform provider and Axeda, which provides cloud-based software for managing connected machines. Earlier this month, ThingWorx announced an agreement to offer semiconductor manufacturer Analog Devices customers an integrated sensor-to-cloud environment on the platform.

James Heppelmann, PTC chief executive officer, wrote last November on the stock market analysis and insights blog Seeking Alpha that IoT will redefine what CAD and PLM are about.

The company has embraced IoT by selling its ThingWorx and Axeda offerings to users outside PTC’s regularly customer base, he said.

IoT gives PTC also an opportunity to “improve the capabilities, value proposition and differentiation of our CAD, PLM, ALM and SLM offerings,” he wrote. Application lifecycle development (ALM) software is used to manage computer software while service lifecycle management (SLM) software is used to manage the delivery of services.

“As we land these new logos, we’ll work to expand our position by introducing SLM, which is really the killer app for IoT, as well as ALM, PLM and even CAD technologies over time,” Heppelmann wrote.

In other words, PTC will offer its engineering technologies for sale to its existing IoT customers. The engineering software maker still seems uncertain how to best merge, sell, and manage IoT, CAD, and PLM.

Which perhaps is as it should be with a technology like IoT that some researchers say will become as ubiquitious as the Internet itself.

Certainly many more engineered products will include sensors in the not-to-distant future and will be connected wirelessly. Designers and IoT will learn to play well together, though how that play will look and how far in the future it will become commonplace still is still unclear. Stay tuned.

PTC Introduces PTC PLM Cloud for SMB Market

January 29, 2015 By Barb Schmitz Leave a Comment

One of the last holdouts in cloud deployment, PTC, announced yesterday that it’s porting its Windchill PLM software to the cloud. The goal it appears is to address the needs of the small- to mid-sized (SMB) business market, a demographic that’s been largely left out of the PLM market. These companies often didn’t have the IT resources to support a full-scale PLM deployment and prefer the SAAS business model that enables them flexible, subscription-based pricing.

The PTC PLM Cloud is based on PTC Windchill, a proven technology with over 1.5 million seats deployed and a platform that was designed from the beginning as a web-based approach to PLM. The result is a true PLM cloud solution, specifically designed to enable team collaboration and product data sharing. This accelerates product development and helps design teams that are stretched across different locations, working with varying CAD applications or coordinating with partners and suppliers.

The PTC cloud offering eliminates the typical, but risky, SMB practice of shared folders and file naming conventions, which can hamper product development. With more effective and reliable data sharing in the cloud, customers are able to improve product development across teams in different locations, teams working with varying CAD applications, and with external teams such as partners and suppliers who are a growing part of the product development process.

PTC introduces a SAAS PLM solution for small and mid-sized businesses.

“The value of PTC PLM Cloud is clear,” said Peter Bilello, President, CIMdata. “PTC’s offering has been designed to allow smaller companies, which have previously been unable to rapidly implement PLM capabilities, to quickly deploy and adopt key data management, visualization and reuse functionality. Those are all vital capabilities for any company in the business of developing products.”

Specific benefits of PTC PLM Cloud are:
Security. PTC’s cloud environment is compliant with ISO 27001:2013 which outlines standard protocols for information security management.
Availability. PTC PLM Cloud is based on PTC’s existing hosted PLM environment which achieves 99.5% availability.
Speed. PTC PLM Cloud performance has been optimized for distributed teams.
Time-to-Value. PTC PLM Cloud enables rapid PLM adoption and accelerated product development processes, enabling improved on-time delivery.
Total Cost of Ownership. PTC PLM Cloud allows for reduced technology acquisition and system administration costs.

“Our main objective when deciding for PTC Windchill on the Cloud was to have central repository of up-to-date product information and efficient delivery of that data to customers and technical support staff, but at the same time to minimize the burden on IT while maintaining a predictable monthly cost,” said Vinny Guercio, VP Engineering, RAB Lighting. “With PTC, we have met our goals.”

Availability

PTC PLM Cloud will be available March 2015 in the following packages:
Standard–a shared, pre-configured PTC Windchill instance that enables immediate go-live
Premium–a dedicated PTC Windchill instance that supports customer-specific system configuration
Enterprise–a dedicated PTC Windchill instance that supports customer-specific system configuration and integrations to corporate systems

You can find more information on the PTC PLM Cloud Resource page. You can also watch a PTC PLM Cloud demo video that shows a synopsis of how the solution will work here.

Barb Schmitz

Harley-Davidson Uses High-Tech Tools to Create First Electric Bike

October 24, 2014 By Barb Schmitz Leave a Comment

When you think of Harley-Davidson motorcycles, often referred to as “hogs,” its iconic, rumbling sound is often the first thing that comes to mind. The company at one point famously sought to trademark its bikes’ distinctive sound.

It sounds this way because of its unique two-cylinder, v-twin engine design. The engine has a single pin connecting the pistons, originally done to reduce manufacturing costs. To make this unique configuration work the spark plugs were positioned in a 45° arc. So after the first cylinder fires, there is 315° of rotation before the second cylinder fires, and then 405° until the first fires again, and so on, and so on, giving the non-symmetrical distinct potato-potato-potato sound.

Owners love that sound, which sets them apart from the rest of the pack of motorcycle enthusiasts. Imagine their collective horror then when Harley-Davidson announced the debut of its first electric motorcycle prototype, called Project LifeWire, which touts zero emissions.

When the company recently released a teaser video that showed the bike blazing down Route 66, the sounds emitted more closely resembled the sound of a turbine. Response has understandably been mixed among die-hard Harley fans. The company is seeking to attract the novice rider, hoping the lack of a clutch and gears will be less intimidating to newbies.

The LiveWire boasts a 74-horsepower (hp) motor, edging out that of the 60-hp Prius, and according to specs the bike can go 53 miles between charges and takes just 3.5 hours to charge on a 220-Volt outlet. Performance wise, the bike tops out at 92 mph, not bad for a battery-powered bike.

Harley-Davidson's LiveWire electric motorcycle has a 74-hp motor and can go up to 92 mph with zero emissions. — Harley-Davidson’s LiveWire electric motorcycle has a 74-hp motor and can go up to 92 mph with zero emissions.

Company deploys high-tech tools to design new bike

Though not everyone has jumped on board the all-electric and hybrid trends in vehicle design, it does show that Harley has one keen eye on the future and will not left in a cloud of dust when the transportation industry–through necessity–eventually moves away from internal combustion engines (ICE).

In order to accomplish this rather Herculean task, Harley-Davidson’s design team started from scratch, using the latest in design, prototyping and manufacturing technology. In order to meet demanding production schedules that determine the eventual success of any new product, the company leaned heavily on CAD software for design and FEA software for testing.

The company used PTC’s Pro/ENGINEER (now Creo/Parametric) to design the new bike and finite element analysis (FEA) software to conduct virtual testing on the design before any actual parts were machined or prototyped.

The FEA analysis enabled the designers to conduct design optimization studies to determine the optimum weight versus structural strength and durability. Designers also advanced wire routing software and visualization tools to evaluate the design prior to prototyping.

Harley-Davidson uses 3D printer to create prototype parts

Prior to machining and fabrication, some of the parts for LiveWire were 3D printed in order to validate the tolerances, proportions, and other attributes. Harley-Davidson has several 3D printers that run around the clock to validate designs for both ICE bikes and for other projects like LiveWire.

The 3D printed parts used for validation are full-sized 1:1 scale parts, not shrunk down models. This prototyping process allowed everyone involved in the design and manufacturing process to test, fit, hold, and see parts of the bike in proper proportion.

Once a design was completed, the company’s design team used computer-aided manufacturing (CAM) software to manufacture it. The entire concept-to-prototype process took only four months.

Today, the bike can been seen throughout the country on Harley-Davidson’s LiveWire Experience Tour. The question of whether Harley-Davidson will begin cranking our production models of the concept bike is unknown, but it certainly shows the company has an eye out for the future.

What do you think about an electric Harley? Share your comments below.

Barb Schmitz

Design Must “Get Smart” in the IoT World

October 14, 2014 By Barb Schmitz Leave a Comment

Once upon a time, products consisted of mechanical components. Much thought and brainstorming went into the best ways to design these components to make them function better than competing products. Life for product designers and engineers became more complicated when products began including electrical parts, as mechanical design and electrical design are often done on disparate systems that typically don’t speak common languages.

Today, in the era of the Internet of Things, products are not only becoming more complex but “smarter.” Products have evolved into complex systems that encompass hardware, sensors, data storage, microprocessors, software, along with the means to “connect” to the outside world via wireless connectivity to the Internet. Complex? You bet.

These smart, connected products are demanding sea changes in the way companies do everything, from how they design products to how they use and manage all of the “big data” that will be captured by these smart devices. The expanded capabilities of smart, connected products and the data they capture is creating disruption in how companies operate and compete.

Information technology (IT) has now become a key player in new product design. Embedded sensors, processors, software and connectivity in products, combined with cloud-based platforms where product data is stored and analyzed, are driving major boosts in product functionality and performance. These improvements have and will continue to be driven in large part by the product-usuage data captured by the “smarts” (sensors, software, processors) now embedded in products.

What Are Smart, Connected Products?

Smart, connected products have three core elements: physical components, “smart” components, and components that enable connectivity. Smart components amplify the capabilities and value of the physical components, while connectivity improves upon the capabilities and value of the smart components and enables some of them to exist outside the physical product itself.

Verizon's line of smart home products enable customers to lock and unlock doors and windows, watch home video cameras remotely, and manage thermostats and lighting. — Verizon’s line of smart home products enable customers to lock and unlock doors and windows, watch home video cameras remotely, and manage thermostats and lighting.

In the November issue of the Harvard Business Review, an article entitled, How Smart, Connected Products are Transforming Competition, uses a car as an example to illustrate the core components of a smart, connected product. The article explains that the physical components comprise the product’s mechanical and electrical parts. In a car, these include the engine block, tires, and batteries.

Smart components comprise the sensors, microprocessors, data storage, controls, software, and, typically, an embedded operating system and enhanced user interface. In a car, these smart components include the engine control unit, antilock braking system, rain-sensing windshields with automated wipers, and touch screen displays. Connectivity components comprise the ports, antennae, and protocols enabling wired or wireless connections with the product.

Connectivity takes three forms, which can be present together:

* One-to-one: An individual product connects to the user, the manufacturer, or another product through a port or other interface. For example, when a car is hooked up to a diagnostic machine.
* One-to-many: A central system is continuously or intermittently connected to many products simultaneously. For example, many Tesla automobiles are connected to a single manufacturer system that monitors performance and accomplishes remote service and upgrades.
* Many-to-many: Multiple products connect to many other types of products and often also to external data sources. An array of types of farm equipment are connected to one another, and to geolocation data, to coordinate and optimize the farm system. For example, automated tillers inject nitrogen fertilizer at precise depths and intervals, and seeders follow, placing corn seeds directly in the fertilized soil.

Connectivity serves a dual purpose. First, it allows information to be exchanged between the product and its operating environment, its maker, its users, and other products and systems. Second, connectivity enables some functions of the product to exist outside the physical device, in what is known as the product cloud.

How IoT will affect product design

Smart, connected products dramatically expand the ways in which manufacturers can differentiate their products. Knowing how customers actually use the products enhances a company’s ability to segment customers, customize products, set prices to better capture value, and extend value-added services. Smart, connected products also allow companies to develop much closer customer relationships.

These so-called smart products will also enable companies to tailor products to more-specific niche markets, and even customize products for individual customers.

Though smart products offer manufacturers a better way to differentiate their products and meet more specific needs within their user base, it also brings a host of challenges. These include the higher fixed costs of more-complex product design, embedded technology, and multiple layers of new IT infrastructure that will be required to manage the data being produced by smart products.

PTC jumps on board IoT bandwagon

At the very end of last year, PTC announced its acquisition of ThingWorx, a tech developer of an application platform designed to rapidly build Internet of Things and Machine-to-Machine (M2M) applications. The PA-based company develops what it calls the “1st Application Platform for the Connected World,” one that combines the key functionality of Web 2.0, social media and Connected Intelligence, and applies to any process that involves “things.”

The ThingWorx platform was designed to reduce the time it takes to build M2M and Internet of Things apps.

The goal of the platform is to reduce the time, cost and risk required to build M2M and Internet of Things (IoT) apps. The platform is comprised of ThingWorx Composer, a modeling environment; a drag-and-drop Mashup Builder for creating apps, real-time dashboards, collaborative workspaces and mobile interfaces without coding; an event-driven execution engine; 3D storage; collaboration capabilities; and connectivity to devices via third-party device clouds, direct network connections, Open APIs and AlwaysOn using the ThingWorx Edge Microserver.

PTC gave editors, analysts and users some glimpses of the next-gen technology that might be developed with the ThingWorx platform at their Live Global event in Boston this summer but have yet to introduce a specific product that leverages this platform.

The bottom line

Increasingly smart and connected products can generate value in several key ways, as streams of real-time operational data are captured, analyzed and shared to increase a company’s understanding of its products’ performance, use and reliability. The technology will provide companies with a wealth of information to feed back into their respective product pipelines, which will in turn will increase competitiveness and their ability to customize products for niche markets and specific customers.

Though there are still technical hurdles to be overcome, the era of smarter, connected products is here and we will continue to cover the topic as it evolves. Stay tuned.

Barb Schmitz

Integrated CAM APPS help engineers take designs straight to manufacturing

October 6, 2014 By Barb Schmitz 1 Comment

by Barb Schmitz, Senior Editor

There have been many hardware advances on the manufacturing side of product development. Increasingly sophisticated machining technology is becoming more affordable and accessible, and as a result, more and more companies are bringing technology, such as CNC machines, in house. Having the capabilities to create prototypes quickly in house saves significant time to market over the traditional process of outsourcing to machine shops.

Another exciting new technology poised to radically change the landscape for manufacturing forever is the 3D printer, which enables manufacturers, as well as hobbyists and would-be inventors, to quickly create physical prototypes with the push of a button. The prices of these machines and the materials they use have plummeted in recent years, putting the technology in the hands of anyone with an idea and a desire to convert that idea into a reality

New technologies demand new methodologies
As more and more companies are turning to rapid prototyping and manufacturing to cut turnaround time for prototypes and customized one-off parts, the necessity to provide product data in the form of 3D models is increasing.

Three-axis and up NC programming, rapid prototyping, mold design and sheet metal manufacturing now require 3D models that can be referenced to create NC toolpaths, SLA molds and sheetmetal flat patterns with proper bend allowances. These systems require input generated in STL format, which can be easily output from 3D models by saving models in that format.

For companies still creating designs in 2D, problems await. When manufacturers have to “rebuild” 2D drawings in 3D, problems often rear their ugly heads downstream. When mistakes are made translating 2D designs into 3D, the resulting part might not meet its original design requirements. In addition, when drawings are tweaked to work for manufacturing, those original 2D drawings are often not changed, so if referenced later, problems again arise.

Automate CAM with CAD-integrated solutions
CAM is the component that enables engineers to turn their design into a physical part on a CNC machine. With traditional CAM software, the workflow is completely different than the workflow in CAD, making it difficult for engineers to use. Yet, with more and more companies bringing machining capabilities in-house, the ability to use and understand how to generate toolpaths has become more important to engineers.

To make it easier for product designers and engineers to take 3D CAD models straight to manufacturing, many fully CAD-integrated CAM software solutions are now available, typically sold by partners of CAD vendors through their reseller channels.

Single-window integration for CAM means that engineers and designers can launch the CAM app from within their CAD program. When design changes are made, these updates are automatically made in downstream manufacturing applications as well. As a result, accuracy of design data is also maintained because the CAM application creates data by directly referencing the 3D CAD model.

Integrated CAM software also enables designers to assess the manufacturability of products early in the design process. “Integrated CAM helps expedite the manufacturing process by helping engineers and designers gain early insight into the cost, performance and manufacturability of designs,” said Aaron Frankel, marketing director at Siemens PLM. “It also enables easy optimization of designs for maximum manufacturing efficiency by reducing manufacturing steps, improving efficiency of tooling, reducing machining times and maximizing shop floor equipment utilization.”

Mending fences: Integrated CAM facilitates concurrent design
By integrating CAD and CAM, companies seek to solve the traditional disconnect between designers and their manufacturing counterparts. Design engineers have a reputation of simply “lobbing” CAD models over the wall to manufacturing without regard to the efforts required to prepare those models for manufacturing, creating tensions between the two groups that have hampered processes and prohibited truly collaborative design efforts.

EasyFill-Moldex3D — Use integrated mold flow simulation technology, such as EasyFill Moldex3D mold analysis software, to gain insight into the real-world manufacturability of design alternatives.

Traditionally, even though both teams are working on the same design, they use different tools that don’t speak the same language, which requires manufacturing to translate the design data they receive from one language to another. This requires them to import and possibly repair the geometry, which creates delays and increases the likelihood of errors.

ptc-creo-time-to-production — PTC Creo enables faster time-to-production and customer responsiveness.

“The design and manufacturing process is not a one-way street,” said Craig Therrien, a product portfolio manager at SolidWorks. “There are always many iterations back and forth, so every time a designer makes a change, that cycle of importing and exporting begins again. Data translations and conversions add extra steps and introduce potential errors.”

When both teams can work in parallel through an integrated design and manufacturing process, they can react faster to design changes. By using a master model with associativity, users can update their mold design, NC programs, electrodes and setup sheets every time the CAD model changes. Associativity makes design changes go faster and simple changes can be updated automatically— they don’t require reprogramming because they automatically re-adjust. More than one designer can work on the same tool at the same time, and you can start NC programming before the tool design is completed.

PTC-Creo-NC-capabilities — PTC Creo provides users with NC capabilities for mold machining.

“Seamless integration of CAM software into the design process can be a huge help in addressing time-to-market issues,” said Brian Thompson, vice president of Creo Product Management at PTC. “If the manufacturing and design engineers are working with the same data, then early visibility into any manufacturing issues is easy, and the resulting collaboration will allow the team to catch and correct manufacturability issues before they hit the production floor.”

Fully integrated CAM packages enable users to easily automate CAM to help lower costs, increase productivity and speed time to market. Because these solutions work directly with 3D CAD data, there is no need to translate or recreate designs for manufacturing, reducing the chance of errors. Many were also designed to follow more of a CAD-like workflow, making it easier for engineers to learn and use.

While integrated CAM software helps to identify manufacturing issues early in the design process, the benefits continue once products move to production. “Once the design is in production, the benefits of a single, associative data model continue,” said Thompson. “Any changes to the production design are automatically propagated into the manufacturing toolpaths, enabling incredibly quick implementation of product design changes into the manufacturing process.”

An integrated approach to CAM also greatly reduces the traditional disconnect between the two processes—design and manufacturing—by providing an integrated platform that serves the needs of both. “Communication barriers are removed with this approach and processes are smoothed out, reducing cycle times while improving quality,” said Therrien. “It also eliminates the need to import/export/repair model data, improves data accuracy, promotes concurrent design and reduces overall costs.”

HSMWorks is an integrated CAM solution focused on CNC milling and CNC turning, available exclusively for SolidWorks users but is sold and supported by Autodesk.

Integrated CAM opens doors to new rapid manufacturing technologies
The combination of powerful 3D modeling, CAD-integrated CAM apps and affordable machining technologies, such as 3D printers, has created new opportunities and radically changed the way people think about design. If you can think it and design it, you can now build it.

For design engineers, 3D printing means faster turnarounds on physical prototypes, the ability to make parts that were impossible with traditional manufacturing techniques, and a way to quickly create one-off configurations for truly customized final products.

Using an integrated approach to CAM also will enable users to quickly take advantage of “next-gen” manufacturing equipment, such as new generations of hybrid additive technologies that can both build up and machine metal.

The combination of CAD-integrated CAM software and new advanced rapid manufacturing technologies packs a mean productivity punch. Not only will it help to bridge the gap between design and manufacturing teams, but will also speed time to market for new products while cutting down on errors and the costs associated with them.

Reprint info >>

Autodesk
www.autodesk.com

SolidWorks
www.solidworks.com

Siemens PLM Software
www.siemens.com

PTC
www.ptc.com