In the automotive industry, products and production processes are becoming increasingly complex. This complexity rises within each engineering domain and across disciplines. With the advent of hybrid and electric vehicles, the complexity of engineering and systems management continues to grow exponentially. So how can component and vehicle manufacturers continue to develop their products at the same pace and at competitive cost? Some may be reluctant to abandon tried-and-true processes. Still, the most progressive manufacturers have realized it is time to re-evaluate their approach and develop new processes best suited to today’s demands. The most successful businesses are adopting model-based systems engineering (MBSE), enabling them to remain competitive, agile and cost-effective while tackling the challenges of quality, increased regulation and sustainability.

Download today to learn how MBSE can improve vehicle development.

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It’s clear that digital prototyping has become a transformative tool for various industries, particularly in manufacturing. It offers significant advantages such as reducing time-to-market, improving product design, lowering costs, and facilitating more effective collaboration.

Here are some of the key industries that benefit the most from digital prototyping:

Digital prototyping in the automotive industry

Digital prototyping allows for rapid prototyping of car components and systems, significantly reducing the need for physical prototypes. Engineers can simulate and optimize designs virtually, testing different materials, shapes and performance characteristics. These models will be used across different departments (e.g., design, manufacturing, testing) can access the same digital model, ensuring a consistent understanding of the product.

Physical prototype testing can be expensive, especially in the automotive industry. With digital models, manufacturers can run simulations (such as crash tests, aerodynamics, or stress analysis) to ensure safety, reliability, and performance. This also aids in customizing vehicles for specific customer requirements, ensuring faster production of tailor-made solutions, such as when responding to fix a recall or if the customer has changed its design of another system in the vehicle.

Why It’s Important:

• Enables simulation of complex systems (e.g., engines, suspension, electronics) before physical production, which is crucial for reducing defects.
• Helps with the integration of new materials, which can be tested virtually for performance under various conditions.
• Assists in evaluating manufacturability, reducing the need for redesigns and ensuring cost-effective production methods.

Aerospace innovation with digital prototyping

Much like in the automotive sector, digital prototyping allows for the simulation of aerodynamics, structural integrity, and performance. Engineers can identify weak points, test material stress, and even simulate changes in environmental or operating conditions (like when a door plug suddenly pops out of a fuselage).

Prototypes are subjected to complex testing scenarios in the virtual world, reducing the risk of costly repetitions during physical tests or in the real-world application of the products. These digital tools allow engineers to simulate lightweight and composite materials for weight-saving without compromising strength or safety.

Why It’s Important:

• Aerospace components are typically high-cost and subject to stringent safety regulations. Digital prototyping helps ensure compliance with these regulations while minimizing waste and rework.
• Engineers can model complex assemblies (e.g., aircraft wings, propulsion systems) virtually, which is vital given the precise engineering requirements and intricate geometries in aerospace.
• Digital prototyping helps with the creation of parts that are difficult to manufacture conventionally, facilitating the integration of additive manufacturing (3D printing) for complex geometries.

Prototyping in consumer electronics

In consumer electronics, digital prototyping enables faster time-to-market for electronics such as smartphones, laptops, and wearables. Features like thermal analysis, signal integrity, and stress testing can be done virtually, allowing engineers to refine designs and optimize performance before committing to hardware.

Why It’s Important:

• The complexity of electronics (e.g., PCBs, microprocessors) demands a high level of precision, and digital prototyping can simulate electrical, mechanical, and thermal behaviors.
• Engineers can optimize the placement of components on a board or inside enclosures, allowing for better designs that minimize space and improve overall efficiency.
• Digital tools also help to evaluate manufacturability, ensuring that designs can be easily translated into production with minimal assembly issues.

Optimizing Industrial Equipment and Machinery

Digital prototyping is especially useful in designing and testing large, complex machinery or industrial systems, like pumps, compressors, or conveyor systems. It helps in simulating real-world conditions while pushing feeds and speeds to the limit without a physical prototype. Engineers can simulate the operation of machinery under stress or in extreme conditions, helping predict failure points and prevent costly downtime after production.

Digital prototypes allow manufacturers to design variations of machines with different features tailored to specific operational requirements, improving efficiency and productivity for their customers and providing a competitive advantage.

Why It’s Important:

• Engineers can simulate how the system will operate in different environments and configurations, helping to identify design flaws before physical production starts.
• Helps in determining the optimal assembly process, minimizing tooling costs, and ensuring that all parts fit together correctly.
• Allows for virtual testing of wear and tear on components, extending the machine’s lifecycle and improving product reliability.

Medical devices: digital prototyping for precision

Few manufacturers face stricter regulatory standards than those developing medical devices. Digital prototyping helps manufacturers ensure that devices meet safety and performance standards throughout the lifetime of the device. For custom implants or prosthetics, digital prototyping allows for the creation of patient-specific devices, enhancing the product’s fit, comfort, and function. they can be virtually tested for wear, fatigue, and safety under various simulated conditions, reducing the risks associated with product failures.

Why It’s Important:

• The high precision required in medical device manufacturing means that digital prototypes can be tested in ways that physical models may not easily allow.
• Virtual models help in ensuring that devices meet all necessary ergonomic, mechanical, and functional requirements before they are physically produced.
• It also allows for better collaboration with medical professionals in the design process, ensuring that the devices are as effective and comfortable as possible for patients.

Digital prototyping in Architecture and Construction

It’s hard to find an industry with higher upfront costs in the design stage than architecture, construction and infrastructure. Digital prototyping in this industry is often associated with Building Information Modeling (BIM), which creates detailed, 3D models of buildings and infrastructure to simulate the performance of the structure, such as energy usage, airflow, and stress on materials. Architects, engineers, and clients can conduct virtual walkthroughs of the building before any physical construction begins, ensuring that all aspects of the design are feasible and functional—once a structure is poured, it’s exceedingly difficult and expensive to make changes. With digital prototypes, architects, engineers, and construction teams can collaborate more efficiently, as they have a unified visual model to work from.

Why It’s Important:

• Digital prototyping helps with evaluating structural integrity and safety during the planning phase, ensuring that the design can be built efficiently and cost-effectively.
• Engineers can work with architects to ensure that the materials chosen for construction are manufacturable, sustainable, and cost-effective.

Digital prototyping has become invaluable across these industries by speeding up design processes, cutting costs, improving collaboration, and ensuring product quality. For manufacturing engineers, it offers the ability to test designs virtually, optimize product performance, and address potential issues before moving into physical production, leading to a more efficient and cost-effective manufacturing process. As industries continue to embrace digital transformation and digital twins, digital prototyping will become even more integral to in nearly every sector of the economy.

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Definition and Process Overview

Traditional prototyping involves the physical creation of a product or component, usually using materials that are similar to those intended for the final product but sometimes out of cheaper plastic or even clay. These prototypes are often built manually, either by hand or using a subtractive manufacturing process like milling or machining.

The process begins with a design, followed by building a physical model through methods such as 3D printing, CNC machining, or injection molding. After a prototype is built, it’s tested for form, fit, and function. Based on test results, the prototype may be revised, and the process starts at the beginning again.

Digital prototyping, on the other hand, is entirely virtual. It involves using Computer-Aided Design (CAD), Computer-Aided Engineering (CAE), and other advanced simulation tools to create a digital representation of the product. Engineers simulate the behavior, performance, and manufacturability of a design without having to physically produce it. These digital prototypes are analyzed for structural integrity, aerodynamics, thermal properties, and other parameters before any physical prototype is made. Virtual testing (e.g., finite element analysis, computational fluid dynamics) helps refine the design.

Time and Speed

Traditional prototyping often requires significant lead times. The process of creating a physical prototype can take days to weeks, depending on complexity, material availability, and equipment setup. Each iteration of a prototype requires time to fabricate, test, analyze, and refine. Changes made to the design often require a new prototype, adding additional time, effort and cost.

Digital prototyping dramatically reduces this time. Since the prototype exists digitally, changes can be made and tested almost instantaneously. Engineers can simulate hundreds or even thousands of design iterations in a fraction of the time it would take to build physical models. For instance, a simulation run that might take hours could result in immediate feedback on the design’s performance, which could take days or weeks with traditional methods.

Cost Implications

The costs associated with traditional prototyping can be substantial. Creating physical models incurs material costs, labor, and machine time. Costs multiply quickly if numerous iterations of a design are needed, especially if the prototype uses expensive materials. Furthermore, testing the physical prototype might lead to additional expenses for failure analysis, repairs, and remanufacturing of prototypes.

With digital prototyping, costs are primarily associated with software licenses, computing power, and highly skilled engineers. Digital models also allow for the identification of design flaws early in the process, reducing the need for costly last-minute changes or expensive rework in the manufacturing phase.

Flexibility and Iteration

Traditional prototyping is inherently less flexible. Making design changes typically requires rebuilding or altering physical components, which can be time-consuming and costly. Even minor adjustments, such as adjusting a hole diameter or reshaping a contour, likely requires a full rebuild of the prototype, adding cost and time.

On the other hand, digital prototyping offers virtually unlimited flexibility. Changes can be made to the digital model instantly, and simulations can be rerun in a matter of hours, enabling rapid iteration. This is especially beneficial when testing alternative design solutions or optimizing performance criteria. The ability to quickly explore a wide range of design possibilities without incurring significant costs or rework gives engineers a powerful tool for refinement.

Accuracy and Precision

Traditional prototyping is limited by the precision of the manufacturing process. Even with advanced machinery, minor inaccuracies can creep into prototypes due to human error, material imperfections, or mechanical limitations of the equipment used. These inaccuracies may be evident in part dimensions, surface finish, or overall assembly.

Digital prototyping, conversely, allows for a level of precision that is only limited by the software and initial design. CAD software offers highly accurate digital representations, and simulations can handle various factors, such as material properties, forces, or motion to ensure the prototype performs as intended. This accuracy translates into better-prepared designs before any physical creation takes place

Collaboration and Communication

Collaboration and communication in traditional prototyping are often hindered by geographic and logistical limitations. Engineers, designers, and manufacturers need to meet in person to discuss changes or challenges with the physical prototypes. This can lead to delays in feedback and miscommunications, especially if teams are spread out over multiple locations.

With digital prototyping, collaboration is streamlined. Design files can be shared and worked on in real-time, regardless of location. Virtual simulations provide a common platform for discussing and analyzing design performance, which leads to more effective decision-making and quicker responses to design challenges. Cloud-based collaboration, where multiple engineers or departments can simultaneously evaluate a design, further accelerate product development.

Testing and Validation

Traditional prototyping allows for physical testing, which can be invaluable for assessing the real-world behavior of a product, particularly in cases where external forces such as temperature, pressure, or human interaction must be accounted for. However, physical testing is often time-consuming and limited in scope.

In contrast, digital prototyping uses advanced simulations to test a product’s performance under various conditions without requiring physical tests. Through techniques like finite element analysis (FEA), computational fluid dynamics (CFD), and kinematic simulations, digital prototypes can be subjected to virtual stress tests, fatigue analysis, and other scenarios to predict real-world performance with a high degree of accuracy. While some physical testing is still essential, digital simulations provide a more cost-effective and faster way to validate designs.

Sustainability Considerations

Traditional prototyping can generate substantial waste, especially when several iterations are required. Scrap material, failed parts, and excessive energy consumption are all environmental concerns associated with physical prototype manufacturing. Additionally, transportation of parts, sometimes across global supply chains, contribute to the carbon footprint.

Being a virtual process, digital prototyping minimizes material waste and, to a degree, energy consumption. Engineers can validate designs thoroughly before committing to physical manufacturing, reducing the need for excessive iterations and material usage.

The key difference between digital prototyping and traditional prototyping lies in their approach to design, iteration, and testing. Digital prototyping offers faster iteration, cost savings, higher accuracy, and a more sustainable process. It enables manufacturers to avoid costly mistakes, optimize designs before they are physically created, and streamline collaboration across teams.

However, it is important to note that digital prototyping does not entirely replace physical prototypes; certain testing and validation processes still benefit from real-world interaction. Both approaches have their place in the modern manufacturing landscape, but understanding their unique advantages helps in leveraging each at the right stage of product development.

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What is Digital Prototyping?

Digital prototyping refers to the process of using computer-aided design (CAD), simulation and other digital tools to create a virtual prototype of a product. This prototype is a fully functional 3D model that can be analyzed and tested for performance, manufacturability and assembly before a physical prototype is ever made. By using digital prototypes, engineers and designers identify design flaws early in the process, significantly reducing the risk of expensive errors and delays in the product development process.

In traditional product development, physical prototypes are built for testing and evaluation, which can be expensive, time-consuming and resource-intensive. With digital prototyping, engineers test and modify designs in a virtual environment, allowing for quicker iterations and more informed decision-making. This process also facilitates better collaboration across teams and helps align the product design with manufacturing capabilities.

Key Components of Digital Prototyping

Computer-Aided Design (CAD): CAD software is the cornerstone of digital prototyping. It allows engineers to create precise 3D models of products and components. These digital models are then used to analyze the geometry, fit and function of the product. The ability to quickly create and manipulate 3D models helps identify potential design issues before any physical work begins.

Computer-Aided Engineering (CAE): CAE tools are used to simulate and analyze the behavior of a product under various conditions. These simulations can include stress analysis, thermal analysis, fluid dynamics and kinematic simulations, which help engineers understand how their design will perform in real-world conditions. By performing simulations early in the design process, engineers can identify potential failures and optimize the design for better performance and efficiency.

Finite Element Analysis (FEA): FEA is a specialized form of CAE that breaks down complex products into smaller, manageable parts (elements) and simulates how each part responds to different forces. This technique is particularly useful for analyzing the strength and durability of a product under load, predicting failure points and determining the most efficient use of materials. FEA is a vital tool in industries such as automotive, aerospace and electronics, where product reliability and safety are paramount.

Virtual Reality (VR) and Augmented Reality (AR): VR and AR technologies allow engineers to interact with digital prototypes in immersive environments. VR can provide a fully immersive experience, while AR can overlay digital models onto the real world. These technologies are particularly useful for visualizing how a product will function or look in a physical space, helping to catch design flaws that might not be apparent in 3D CAD models alone. VR and AR also enable collaboration with stakeholders, regardless of geographic location, further enhancing design reviews and decision-making.

Additive Manufacturing (3D Printing): While digital prototyping itself focuses on virtual design, additive manufacturing plays a crucial role in turning digital models into tangible prototypes. 3D printing allows engineers to quickly fabricate physical models directly from digital files, speeding up the prototyping process. This process is particularly useful for testing form, fit and function, as well as for producing low-volume parts and iterations of designs.

The Digital Prototyping Process

The process starts with conceptual design, where engineers and designers use CAD software to develop an initial 3D model of the product. This model includes the geometric details of the product, material properties and design intent. The focus at this stage is on form and functionality rather than on final manufacturing details.

Once the 3D model is created, engineers can use CAE tools to simulate how the product will behave under different conditions. This can include stress, strain, thermal, fluid, or motion analysis. The goal is to identify potential issues such as structural weaknesses, thermal inefficiencies, or improper fit. Engineers can make adjustments to the design based on the simulation results, allowing for optimization before any physical prototypes are made.

Based on the feedback from simulations and analysis, the design is refined. Engineers may adjust geometry, material choices, or manufacturing processes to improve the product’s performance or reduce costs. During this stage, iterative testing and fine-tuning are crucial. Digital prototypes allow for rapid modifications without the need for physical prototypes, which speeds up the design cycle.

After optimizing the design, the virtual prototype is validated using more advanced simulations and tools like FEA or motion analysis. Validation ensures that the design is manufacturable, reliable and safe. Additionally, virtual prototyping can include testing for manufacturability by evaluating how the product will be produced, assembled and maintained.

Once the digital prototype is validated, engineers can use additive manufacturing (3D printing) to create a physical prototype. This prototype allows for real-world testing of form, fit and function. It also enables stakeholders to evaluate the design before moving to full-scale production. Additive manufacturing is particularly useful for small-scale production runs, low-cost prototyping and designs that require complex geometries.

Benefits of Digital Prototyping for Manufacturing Engineers

Digital prototyping significantly reduces the costs associated with physical prototyping, which often involves expensive materials and labor. With digital models, engineers can simulate the functionality and manufacturability of a product without the need to build each prototype physically. The need for expensive and time-consuming physical testing is minimized.

By enabling rapid iteration and early detection of issues, digital prototyping accelerates the design process. Engineers can test multiple variations of a design in a virtual environment before deciding on the final solution. This shortens product development cycles and allows manufacturers to bring products to market more quickly.

The ability to simulate a product’s performance before physical testing helps ensure that it will meet the desired specifications. By identifying weaknesses and optimizing the design early in the process, engineers can produce more reliable and high-quality products. This leads to fewer product recalls and higher customer satisfaction.

Digital prototypes can be shared and modified across teams, enabling better collaboration between engineers, designers and other stakeholders. Virtual environments, such as VR or AR, allow people to interact with the design from different locations, making it easier to make decisions and ensure everyone is aligned on the project goals.

With the ability to test and iterate designs virtually, digital prototyping makes it easier to optimize products for performance, cost and manufacturability. Engineers can experiment with different materials, manufacturing processes and design configurations without incurring the costs of physical prototypes.

The ease with which designs can be modified and tested allows engineers to explore new ideas and push the boundaries of traditional manufacturing. Digital prototyping fosters innovation by removing many of the constraints imposed by physical prototypes, allowing for more creative solutions and advanced product designs.

Challenges of Digital Prototyping

While digital prototyping offers significant benefits, it is not without its challenges.

The software and tools used in digital prototyping, such as CAD, CAE and FEA, can be complex and require specialized training. Engineers need to have expertise in these tools to effectively create and analyze digital prototypes.

As digital prototypes become more complex, managing large volumes of data becomes a challenge. Ensuring data consistency, version control and secure storage are important aspects of using digital prototypes effectively.

Integrating digital prototyping into established manufacturing processes can be challenging. Many organizations may need to upgrade their infrastructure or rework their workflows to take full advantage of digital prototyping.

While digital prototypes allow for extensive simulation, the results depend on the accuracy of the input data and the assumptions made during the modeling process. Simulations may not always perfectly predict real-world performance and physical prototypes may still be needed for final validation.

Digital prototyping represents a significant advancement in product development, offering manufacturing engineers the ability to design, test and refine products in a virtual environment before physical production. As digital tools continue to evolve, the integration of technologies like additive manufacturing, VR and AR will further enhance the capabilities of digital prototyping, allowing manufacturers to innovate and optimize designs.

For manufacturing engineers, embracing digital prototyping is not just about adopting new tools, but about rethinking the entire approach to product design and manufacturing. By leveraging the power of digital models, simulations and virtual environments, engineers can build more reliable, cost-effective and innovative products.

The post What are the fundamentals of digital prototyping in manufacturing? appeared first on Engineering.com.

In a significant move towards innovation and efficiency, Volvo Group and Daimler Truck AG recently announced their intent to form a joint venture headquartered in Gothenburg, Sweden, focused on developing a software-defined vehicle platform. This collaboration, driven by a mutual commitment to enhancing vehicle efficiency and customer experience, builds on a history of joint initiatives between the two OEMs. As the companies note, “In the context of the already heavy investments into the transformation towards CO2-neutral drive technologies, cooperation on digital technology development has become even more vital to best meet development objectives and customer expectations within a feasible timeframe.”

This latest joint venture highlights how established OEMs are adapting to a rapidly changing industry landscape, where digital innovation is increasingly the key to competitive advantage. In an industry undergoing a seismic shift due to the rise of electric vehicles (EVs) and software-defined functionalities, partnerships like this one exemplify the potential of joint ventures to accelerate innovation and deliver enhanced customer experiences in the next generation of vehicles.

As competition in the automotive sector continues to intensify, OEMs are finding that collaboration, rather than rivalry, is the best path forward. The Volvo-Daimler joint venture reflects a strategic alignment that allows both companies to pool resources and expertise, creating a comprehensive platform to support software-defined vehicle functionalities. This approach of leveraging digital technology to meet evolving customer demands is increasingly central to success in the automotive industry.

This collaborative approach echoes other recent partnerships, such as the BMW-Rimac alliance. In this case, BMW strategically tapped into Rimac’s expertise in electric vehicle (EV) technology to enhance its own performance models, thereby accelerating BMW’s path to innovation. By focusing on specialized strengths, OEMs like BMW and Rimac can accelerate product development and bring cutting-edge advancements to market faster. Such partnerships illustrate the powerful potential of digital collaboration: leveraging complementary skills and resources allows OEMs to meet heightened consumer expectations for advanced, high-performing vehicles.

Cross-industry collaborations further highlight the trend toward joint innovation. For example, the Sony-Honda joint venture, Sony Honda Mobility, combines Sony’s strengths in digital and consumer electronics with Honda’s extensive automotive capabilities. Together, they aim to produce EVs that incorporate Sony’s entertainment technology, delivering a unique in-car experience. Success in such partnerships depends on establishing clear communication channels, aligning goals, and fostering a culture of innovation that enables agility and rapid time-to-market—outcomes that can be difficult to achieve independently.

Managing IP, patents and data integrity

One of the key pillars of successful joint ventures, especially in the high-stakes automotive industry, is the effective management of intellectual property (IP) and product data. Volvo and Daimler will need to create frameworks that protect their proprietary technologies while enabling capital gains from collaboration. This involves negotiating licensing agreements, defining usage rights, and implementing robust data management systems that allow for tracking and securing shared information in an equitable way.

The implications of joint and respective patents are significant. OEMs must navigate the complexities of joint ownership and ensure that both companies’ innovations remain protected within a collaborative framework. This is particularly important as joint ventures often require integrated ecosystems involving legal, procurement, financial, and engineering departments, as well as supplier networks. Establishing protocols for patent registration and enforcement is essential to safeguarding each company’s IP while promoting an environment where shared innovation can flourish.

In the era of software-defined vehicles, sometimes referred as ‘computers on wheels’, digital and data management introduces new challenges, particularly in the integration of software and hardware components. Volvo and Daimler will need to develop seamless integration processes that allow for real-time data sharing between software systems and hardware functionalities. Such integration is crucial to enhancing vehicle performance and maintenance while meeting customers’ evolving expectations for connected and responsive vehicles.

Digital as a driver of innovation

Digital transformation is no longer just an added value for OEMs; it is essential to maintaining competitiveness and enhancing customer experiences. By adopting advanced software platforms, companies like Volvo and Daimler can produce vehicles with superior connectivity, safety, and functionality. These platforms provide the foundation for future advancements in autonomous driving, smart mobility, and other transformative technologies that redefine what vehicles can offer consumers.

Similar trends are visible in the technology sector. A prominent example is the Google-Samsung partnership on the Wear OS smartwatch platform. This partnership allowed both companies to combine their resources, creating a more versatile smartwatch platform that elevated the Android ecosystem and posed a direct challenge to competitors. Such collaborations demonstrate that digital transformation can unlock new product capabilities and allow companies to stay competitive in a connected, technology-driven landscape.

The benefits of collaboration are clear, but these partnerships also come with challenges, especially regarding data security and privacy. OEMs like Volvo and Daimler, in sharing sensitive information, must implement stringent security measures to prevent data breaches and unauthorized access. By establishing rigorous data governance protocols and ensuring compliance with privacy regulations, companies can protect customer trust and the integrity of their joint venture. Additionally, protecting shared data ensures that each company maintains a competitive edge even as they work together to drive industry transformation.

Strategic alignment in digital partnerships

For OEMs considering joint ventures to accelerate digital transformation, these guiding questions can help ensure alignment and a strong foundation for collaboration:

1. What strategic goals does this digital partnership aim to achieve, and how does it align with the long-term vision for growth and innovation?
2. How can digital tools be leveraged to identify and select partners that complement existing strengths while addressing areas requiring additional expertise?
3. What digital frameworks will be established to manage shared intellectual property, ensuring both protection and mutual benefit of proprietary technologies?
4. How can the complexities of joint ownership, patent rights, and intellectual property be navigated digitally to protect innovations and enhance market position?
5. What secure, digitally enabled processes will support seamless data exchange, integrating software and hardware to maximize functionality and performance?
6. How can digital tools foster a collaborative culture across teams that promotes adaptability and sustained innovation throughout the partnership?
7. What data governance measures will ensure digital security and privacy, safeguarding sensitive information within the joint venture?
8. How will the success of the joint venture be digitally monitored, allowing real-time adjustments to achieve strategic goals?
9. What digital infrastructure will enable continuous improvement and adapt to evolving technology needs as the partnership scales?
10. How can digital insights be used to enhance customer experiences, ensuring alignment with consumer demands and market trends?

These guiding questions emphasize the crucial role digital capabilities play in aligning strategic goals, fostering secure collaboration, and achieving successful outcomes in joint ventures. Relevant not only to digital product development, these considerations also broadly apply to modern product strategies that seek to scale innovation across organizational boundaries.

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This white paper covers why using Ultra-Wideband (UWB) technology in wireless battery management systems (BMS) communications offers great advantages. In this paper NXP explains the benefits of wireless communications such as higher energy density, easier pack assembly and second life options. It explains how UWB works and its advantages over narrow-band technology in battery pack environments. And finally, it explains NXP’s UWB wireless BMS full solution, its benefits and how it is protocol level compatible with NXP’s wired BMS solution.

Your download is sponsored by NXP.

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Powering the future of mobility requires sophisticated solutions, and NXP’s Power Management Integrated Circuits (PMICs) are at the forefront of enabling software-defined vehicles (SDVs). NXP’s latest innovations enhance efficiency, safety, and design simplicity for automotive manufacturers.

Discover the key benefits of NXP PMICs:

• Power Efficiency: Advanced low power modes to minimize energy consumption.
• Functional Safety: Built-in safety mechanisms meeting ASIL B or D standards.
• Simplified Architecture: Reduce complexity and component count with the ByLink System Power Platform.

Scalable PMIC solutions also support the growing demands of electrification and autonomous driving, paving the way for streamlined designs and faster time-to-market. Explore how this power management technology is driving a greener, smarter future in the automotive world.

Your download is sponsored by NXP.

The post Driving the Future: Power Management Solutions for Software-Defined Vehicles appeared first on Engineering.com.

Battery technology is long been understood as the limiting factor in electric vehicle adoption worldwide. Electric vehicle batteries need to be lower in cost, offer higher energy density, and be faster to charge than current technology allows, and all major automakers are working to achieve this.

A new solid-state lithium-based design from Factorial, backed by Mercedes-Benz and Stellantis, promises to increase electric vehicle range by up to 80%, with lower operating temperatures and increased safety. Test cells have already been produced, and factorial has built a full-scale production facility just outside Boston. Widespread implementation should be ready by the end of the decade. 

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Access all episodes of This Week in Engineering on engineering.com TV along with all of our other series.

The post Is the Solid-State Battery the Solution for Weak EV Sales?  appeared first on Engineering.com.

No more tedious setups, fiddling with virtual cameras and specifying shaders. No more setting the scene and getting the lighting just right. No more ray tracing. Just one click, and seconds later, a gorgeous render.

It wouldn’t work with traditional rendering techniques. But with generative AI, Depix claims to have made one-click rendering a reality.

“It’s so easy it’s almost frightening,” Lunn, CEO of Depix Technologies, told Engineering.com.

Here’s a look at the emerging world of AI rendering.

Generative rendering

Some applications of generative AI are obviously in their early stages. AI can write, but not well. It can create 3D models, but they’re painfully rough. AI can run simulations, but only in narrow cases.

Image generation is different. While AI images vary in quality, the best can be incredibly convincing. And even if you can tell that an image is AI-generated, it often doesn’t matter; it does the job well enough. (For a case in point, go to any blog and look at the thumbnail images.)

Given that rendering is nothing more than creating an image, it should come as no surprise that AI is now doing the job. And it’s doing it so well that Lunn, who’s been in the rendering software industry since 1997, calls it “revolutionary rendering.”

After seeing a demo of the process, it’s hard to disagree.

You don’t even need a CAD model to create a fantastic render: a screenshot of a CAD model will do the trick. So will a pencil sketch. Depix has developed an AI image generator specialized in product renders, with an image of your design as the prompt.

Take a look. The image pair below is from CADviz, one of Depix’s products, which takes a CAD screenshot (Solidworks, in this case, but it could be any CAD program) and outputs a product render:

The user doesn’t have to specify any details about the render—no materials, no scene info, nothing. CADviz takes about 8 seconds to generate a render from a CAD screenshot.

Here are a few more examples using screenshots from Solidworks, SketchUp and Revit, respectively:

You can try CADviz yourself—it’s free, but limited. A paid version with more features will be available soon, according to Lunn, allowing high-resolution renders with control over the output.

That’s just the first turn of the rendering revolution.

How to make designers cry

Another Depix product, SceneShift, is “blowing up” in preview, according to Lunn. “Everybody wants it,” he says.

SceneShift merges text prompting with the crucial constraint of product rendering: shape preservation. Give it a product image, plus a text prompt for a new background, wait a few seconds, and you have a brand new render: new scene, new lighting, same product. Lunn boasts that the results are “better than any CG expert.”

Here’s an example of a SceneShifted car:

Then there’s StyleDrive, another Depix tool that’s struck a chord with early testers. Or, as Lunn puts it: “This makes designers cry.”

In StyleDrive, users upload two images, a base image and a style image. The style image drives a change to the base image. For example:

There are no restrictions on what either image can be. A designer could sketch a car and apply the style of a diamond, as in this example:

In the time it takes a designer to scribble on a napkin, plus 8 seconds, StyleDrive generates a completed render. Even if it’s not strictly photorealistic, Lunn views StyleDrive as a conceptualizing superpower.

“It can just spit out infinite variations,” Lunn said. “It’s a little disturbing, but exciting at the same time.”

Some are already excited about it. Lunn says Depix already has a number of paying customers in the automotive industry (though to respect their privacy, he declined to name any).

How to try AI rendering

Though Depix has initially focused on the automotive industry, its technology is not limited to vehicles. Depix’s AI model is based on Stable Diffusion, an open source model trained on 5 billion images, so it works with many subjects.

Here’s an example of SceneShift applied to a fashion model:

Lunn says users will be able to “personalize” the rendering AI with custom training data, up to 1000 images. They could include sketches, CAD models, renders or any other images that could tune the model in the desired direction. With the ability to specify the weight (or “influence”) of this training set, users will notice an appreciable difference in the output, according to Lunn.

Depix’s AI tools are available as individual APIs and together in an interface called Depix Design Lab. Right now, anybody can test the technology on Depix’s Discord server (warning: it’s addictive).

Someday Lunn hopes to see Depix tools integrated in professional software such as CAD and PLM. He says the company is having discussions with several developers, noting specifically that his team is working on a plug-in for Autodesk VRed.

“We’re taking the rendering world by storm,” Lunn says.

The inhabitants of that world may want to reach for an umbrella—or perhaps a lifeboat.

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The Ansys SimAI cloud-enabled generative artificial intelligence (AI) platform combines the predictive accuracy of Ansys simulation with the speed of generative AI. Because of the software’s versatile underlying neural networks, it can extend to many types of simulation, including structural applications.

This white paper shows how the SimAI cloud-based software applies to highly nonlinear, transient structural simulations, such as automobile crashes, and includes:

• Vehicle kinematics and deformation
• Forces acting upon the vehicle
• How it interacts with its environment
• How understanding the changing and rapid sequence of events helps predict outcomes

These simulations can reduce the potential for occupant injuries and the severity of vehicle damage and help understand the crash’s overall dynamics. Ultimately, this leads to safer automotive design.

Your download is sponsored by Ansys.

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