• About NFPA 70B
• Why NFPA 70B matters
• The role of an effective electrical maintenance program
• How effective condition-based maintenance programs can help save costs and keep your people and your installations safe
• Examples and best practices
• NFPA 70B as a pillar of your sustainability commitments

Your download is sponsored by Schneider Electric.

The post The NFPA 70B Standard Just Got Overhauled appeared first on Engineering.com.

Let’s start with some breaking news: Today PTC announced CAM Studio for Onshape, a new computer-aided manufacturing environment for the cloud CAD platform. A beta of CAM Studio is available now for Onshape Professional and Enterprise subscribers.

CAM Studio supports 2.5 and basic 3-axis machining for common manufacturing strategies and machines, according to Onshape. Just as with CAD data, Onshape will provide versioning, branching and merging functions for CAM strategies.

There will also be a premium version, CAM Studio Advanced, that will support advanced 3-axis, 3+2 axis, 4-axis, and 5-axis machining strategies, as well as advanced mill and turning machines. CAM Studio Advanced will not be included with any Onshape subscription and will instead be available as a separate purchase, though Onshape has yet to announce its price or release date.

PTC describes CAM Studio as “one of the most requested features in Onshape’s history,” and it really does feel like the last puzzle piece for the platform. What began with browser-based CAD has steadily expanded its scope to PDM, simulation, PCB design, rendering, and now CAM—rounding off Onshape into an end-to-end design platform.

Stay tuned for details as we learn more about CAM Studio, and let me know your thoughts and questions about the release at malba@wtwhmedia.com.

Motif raises $46 million for next-gen BIM software

Engineering software startup Motif announced that it’s raised $46 million towards its goal of revolutionizing building design. Motif believes that building design software is outdated, overdue for an overhaul. They want to revitalize it with modern techniques like cloud computing and machine learning.

Not only is Motif flush with funds, it’s helmed by an experienced leadership team. That includes CEO Amar Hanspal, former co-CEO and chief product officer at Autodesk, and CTO Brian Matthews, former VP of platform engineering at… Autodesk. What do you know, Autodesk is also on the résumés of Motif’s VP of product Matt Jezyk and Motif’s VP of design Lira Nikolovska, who both worked on Autodesk’s industry-dominating BIM platform, Revit.

In a blog post about Motif’s vision, Hanspal laments a “lack of innovation” in BIM software due to “complacency among incumbent vendors with near-monopolistic control.” He doesn’t go so far as to name names, so there’s really no way to know who he’s talking about. Yet I can’t help but feel that, like a magic eye puzzle, the answer is hiding in plain sight.

Altair Enlighten Award open for submissions

The Altair Enlighten Award, which honors sustainable and lightweight design in the automotive industry, is officially open for 2025 submissions. Now in its 13^th year, the annual award is presented in association with the Center for Automotive Research (CAR).

There are seven award categories this year: sustainable product, sustainable process, sustainable computing, responsible AI, module lightweighting, enabling technology, and future of lightweighting. The deadline to apply is June 16, 2025. Altair and CAR will present the awards on September 16, 2025 in Detroit, Michigan.

Think you got the light stuff? Apply here.

The Engineer’s Guide to Digital Transformation

This newsletter may be about software, but let’s not forget that there are real people using that software. Or real AI agents, programmed by real people, or maybe programmed by different AI agents, but someone probably had to program those. Point is, there are people somewhere, and if you’re a person too, you may enjoy The Engineer’s Guide to Digital Transformation.

This 77-page e-book is all about the human element of digital transformation. Written by Peter Carr, author and instructor of the University of Waterloo Watspeed Digital Transformation Certificate Program, it’s chock-full of accessible advice for planning, executing and leading digital change at any organization. Because digital transformation isn’t as simple as downloading some shiny software and bracing for metamorphosis. It needs to be a measured, collaborative strategy that spans your whole organization, humans and all.

This e-book has been a long time in the making, and I’m thrilled it’s finally available for you to read (or for your AI agent to ingest and summarize). Best of all, it’s free—and if you can’t afford that, we’ll send you two copies on the house. Here’s one to get you started.

One last link

To tariff or not to tariff? That is the question. If you’re watching the maybe trade war possibly unfold, perhaps you’d enjoy 13 KPIs to track the impact of 25% tariffs on your manufacturing company by my definite colleague Michael Ouellette.

Got news, tips, comments, or complaints? Send them my way: malba@wtwhmedia.com.

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New Zealand-based Miraka, the world’s first dairy processor to get its power from renewable geothermal energy, has chosen smart manufacturing software Plex from Rockwell Automation to become even more efficient and sustainable.

Miraka will use Plex to integrate its enterprise resource planning (ERP) systems. ERP is a software system that helps organizations streamline and automate their core business processes—including financial management, human resources, supply chain, sales, and customer relations—across the entire enterprise.

Miraka says its use of geothermal energy helps it “emit 92% less manufacturing carbon emissions than traditional coal-fired factories, giving Miraka one of industry’s lowest global carbon footprints.”

The dairy company will use Plex to connect, automate, track and analyze its operations—from the pasture to the factory floor— to take its core values of excellence and innovation to the next level.

Robert Bell, Miraka CFO, calls Plex a “single source of truth,” with intuitive tools that will help Miraka optimize their business and operational performance by increasing efficiencies.

Plex supports Miraka’s goals to become even more resilient, agile, and sustainable by offering a holistic view across the enterprise so Miraka quickly can respond to market demands and customer changes without interrupting production. The Plex software was built around the pillars of smart manufacturing, helping companies not only streamline their operations, but making it easier for them to follow industry standards and grow their business.

“Plex is a modular system, so it can grow and adapt as needs change in the future, allowing companies like us to remain agile and stay ahead of the competition,” added Bell.

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Here’s a breakdown of key tools and software commonly used for digital prototyping:

3D CAD (Computer-Aided Design):

These tools allow designers and engineers to create detailed digital models of products. They support both 2D and 3D modeling and are used to design everything from simple parts to complex assemblies. Parametric and direct modeling capabilities allow for flexibility in making changes and iterations during the design process.

Simulation and Analysis:

Simulation enables users to test how a design will behave in real-world conditions without creating physical prototypes. They use mathematical models to simulate physical properties such as stress, thermal behavior, fluid dynamics, and vibration. They help engineers predict potential performance issues and optimize designs before production.

Visualization/Rendering:

Used for creating and visualizing digital models with a focus on aesthetics and functionality, these tools are important in the early design phase. They help visualize how a product will look and function in its environment, making them useful for concept development, visualization, and basic design adjustments.

Virtual Reality (VR) and Augmented Reality (AR):

Part of the hype cycle five years ago, VR and AR have quietly become a big part of interacting with digital prototypes in immersive, 3D environments. VR tools simulate a fully virtual model, whereas AR tools overlay digital models onto the physical world. Both are used to visualize how prototypes will look and behave in real-world settings, providing an intuitive way to review designs and test user interactions.

Product Lifecycle Management (PLM):

PLM tools manage a product’s entire lifecycle—from initial design to end-of-life. They integrate various design, simulation, and testing stages to allow teams to collaborate efficiently, track revisions, and maintain up-to-date data across all stages of the product development process.

Rapid Prototyping and 3D Printing:

These tools convert 3D models into instructions for additive manufacturing (such as 3D printing), enabling the creation of physical prototypes quickly and cost-effectively. They are commonly used to test form, fit, and function of designs before committing to full-scale production.

Digital Twin Platforms:

A digital twin is a virtual representation of a physical product, system, or process. These platforms collect data from sensors and simulations to provide a real-time, dynamic view of the product’s performance. They are used to monitor the performance of prototypes, track how they evolve over time, and optimize their operations in real-world conditions.

Generative Design:

Artificial intelligence (AI) and cloud computing are combining to automatically generate optimized design solutions based on specific input parameters and constraints (such as weight, material, strength, and cost). They create myriad design options to help designers explore innovative, efficient solutions that might not be immediately obvious in traditional design processes.

Electronic Design Automation (EDA):

These tools are used to design and prototype electronic circuits and printed circuit boards (PCBs). They help engineers create, simulate, and test electronic components and systems in the digital space, ensuring that the circuits are functionally correct before physical assembly. They also help manage the layout and routing of components for optimal electrical performance.

Each tool plays a role in different stages of the digital prototyping process, whether you’re creating a 3D model, simulating real-world conditions, testing functionality, or preparing for physical production. For manufacturing engineers and managers, selecting the right set of tools depends on the specific needs of the product being developed, such as complexity, cost, and how closely the prototype needs to mirror the final product. Tools like SolidWorks, ANSYS, Fusion 360, and Teamcenter are often popular in industrial environments where both design and testing are crucial.

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As the pace of technological change accelerates, keeping up is becoming increasingly difficult. Whether it’s the operating systems on our laptops, new versions of our smart phones, or the new and unfamiliar features of a new car, change is inevitable. For manufacturers, each new generation of production equipment is smarter than the last, and that added capability means more information generated by each machine, at a faster rate.

A major promise of the Internet of Things is the ability to keep control of multiple machines in a production line or plantwide, frequently with multiple pieces of equipment from different manufacturers, often running different software, all under the control of manufacturing engineers who must make sense of the data and turn it into actionable insight. Older, legacy equipment can be retrofitted in most cases with IoT capability. How can users of older, legacy equipment integrate new machines into existing production processes without creating chaos?

Jim Anderton spoke with Bill Dykas, product manager for Telit IoT Platforms on connecting old and new equipment in the age of the Internet of Things. 

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Faster Product Development Cycle

Digital prototyping allows engineers to create virtual prototypes of products, which can be tested, analyzed, and iterated upon quickly. This leads to faster product development cycles because physical prototypes—often time-consuming and costly to build—are replaced with digital models that can be easily modified and tested in virtual environments.

Multiple teams (design, testing or manufacturing) can work on the same digital prototype simultaneously, allowing for more collaborative efforts and quicker decision-making.

Cost Savings

Traditional prototyping involves building physical models, which can be expensive, especially if several iterations are needed. With digital prototyping, these physical prototypes are eliminated, saving on materials, labor, and production costs.

Problems in the design phase are identified and resolved early, avoiding costly rework during the manufacturing phase. This reduces the likelihood of production delays and scrap due to design flaws.

Simulation tools allow engineers to analyze the material requirements and structure before physical production, leading to optimized use of materials and reducing waste.

Enhanced Design Precision and Quality

Digital prototypes allow engineers to simulate real-world conditions, ensuring that designs are optimized for performance, strength, and durability before moving to production. This leads to higher-quality end products with fewer design flaws.

Engineers can perform complex tests (e.g., stress, thermal, fluid dynamics) on digital prototypes to see how they will behave under various conditions, which might be difficult or impractical to replicate with physical prototypes.

Quick revisions to the digital prototype enable engineers to try out multiple variations of a design, ensuring the best possible version before manufacturing.

Better Collaboration and Communication

Digital prototypes can be shared across teams and even external partners globally, enhancing communication between departments (e.g., design, engineering, manufacturing) and improving alignment on goals, timelines, and requirements.

Managers and stakeholders can easily visualize and interact with the digital prototype, facilitating more effective decision-making and feedback. This improves transparency in the design process and helps prevent misunderstandings.

Streamlined Production Processes

Digital prototypes can be designed with the manufacturing process in mind, allowing engineers to simulate how a product will be built, identify potential manufacturing challenges, and optimize the design for ease of production.

Once a digital prototype is refined, it can be used to create detailed specifications for tooling, machinery, and assembly processes. This helps ensure a smoother transition from design to actual manufacturing with fewer adjustments needed on the factory floor.

Simulating assembly processes in the digital space can help identify potential issues in assembly lines or machinery setups, minimizing costly mistakes and time-consuming adjustments during physical production.

Customization and Flexibility

With digital prototypes, engineers can quickly adapt designs to meet customer-specific needs or adjustments. This is particularly advantageous for industries that require high levels of customization or need to pivot based on market demands.

Once a product design is finalized digitally, scaling up production becomes smoother because the digital model can be used for a variety of manufacturing methods (e.g., additive manufacturing, CNC machining, injection molding).

Integration with Advanced Manufacturing Technologies

Digital prototypes integrate seamlessly with additive manufacturing (3D printing), which enables quick and cost-effective production of physical prototypes or even end-use parts, enhancing flexibility in the prototyping process.

Engineers can optimize designs specifically for additive manufacturing techniques using digital prototyping, which fits into Simulation-Driven Design for Additive Manufacturing (DfAM). This reduces the need for costly adjustments during the actual production process.

Enhanced Innovation and Risk Mitigation

Digital prototyping allows engineers to experiment with different design ideas without the risk or cost of building physical prototypes. This fosters a more innovative environment, as teams can quickly test a variety of concepts and find the best solution.

By identifying design flaws, performance issues, and manufacturing challenges in a digital environment, potential risks are minimized before physical production begins, leading to reduced risks of product recalls or costly revisions during the manufacturing phase.

Sustainability Benefits

The ability to simulate and optimize designs virtually reduces the need for multiple physical prototypes, resulting in less material waste and a more eco-friendly development process.

Through digital modeling, engineers can analyze energy consumption, waste, and emissions, helping to design more energy-efficient products and production processes that align with sustainability goals.

Investing in digital prototyping offers significant advantages in terms of cost savings, faster time to market, and improved product quality. They can use it to test and iterate designs rapidly, streamline manufacturing processes, and encourage more innovative solutions. Ultimately, digital prototyping helps create higher-quality products, reduce risks, and improve the overall efficiency of the manufacturing process, all of which make it a powerful tool for modern manufacturers.

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AI is not going to replace your workers and make strategic executive decisions. But any manufacturer not carefully considering a well-thought-out AI strategy is leaving money on the table. And it doesn’t matter if you are an enterprise-scale manufacturer or regional company with less than 150 employees, there are plenty of ways to extract value and gain an edge from AI.

“One area where manufacturers can quickly find ROI from AI is real-time knowledge sharing and translation, where AI breaks down language barriers by instantly translating and summarizing documentation like standard operating procedures, repair logs, and shop floor communications,” says Qaiser Habib, Head of Canada Engineering at Snowflake, a cloud data platform developer based out of Bozeman, Montana.

Snowflake has developed a flexible cloud-based platform for modern data teams looking to store, process, and analyze data in a highly scalable and cost-effective manner while enabling collaboration across different departments and organizations. The company has offices in 50 cities around the world. Habib speaks to us from Snowflake’s Canadian engineering hub in Toronto.

“This enables global manufacturing teams [and supply chain partners] to collaborate seamlessly, share best practices across facilities, and access critical operational insights in their native language.”

This is one of several “low-hanging fruit” areas Habib says will drive successful adoption of AI amongst manufacturers.

Manufacturers can also find near-immediate AI ROI is in key areas such as predictive maintenance, where AI monitors equipment to prevent costly unplanned downtime. Additionally, AI guided maintenance solutions can analyze repair logs and equipment manuals to recommend troubleshooting steps faster for more effective repairs.

According to Habib, the key to creating AI ROI is using AI solutions that understand your company and industry-specific processes rather than generic solutions.

AI predictions for manufacturing

Habib made a few predictions about how manufacturers will approach and interact with AI for the next year or so. Here’s a look:

Shift from AI hype to ROI: 2025 will mark the transition from AI experimentation to measurable business impact. 2024 saw widespread hype and experimentation as organizations explored AI and solidified their data strategies. Organizations are now demanding concrete returns, and engineering teams must identify specific, high value problems where AI can deliver measurable results and move beyond proofs of concept to full production systems that emphasize accuracy and reliability. The focus will be on operationalizing large language models (LLMs) and taking evolved approaches to security, governance, and observability.

This shift is particularly evident in the manufacturing industry, where enterprise AI applications are delivering tangible results through computer vision quality control systems. These systems are transforming production by dramatically reducing inspection time compared to manual processes, catching subtle defects that human inspectors might miss, preventing costly downstream quality issues, and enabling 24/7 continuous inspection without fatigue.

Organizations will prioritize AI projects that can demonstrate clear financial impact within specific timeframes, moving away from speculative “AI for AI’s sake” projects toward targeted solutions for well-defined business problems.

Engineers will face the stress test: With attention shifting towards ROI, engineering teams are experiencing unprecedented pressure to validate AI investments while maintaining code quality, security, and team wellbeing. As engineers, we are accustomed to working in fast-paced environments with new technologies and innovations, but there is now added pressure to balance rapid AI implementation with sustainable team practices.

To foster a productive and engaged workforce, leaders must ensure their teams have a crystal clear understanding of the business case behind their AI initiatives and provide the necessary training needed. As organizations prioritize these efforts, upskilling through certification programs and professional development initiatives will remain a critical focus in the workplace.

The key to success in 2025 will be finding the sweet spot between aggressive AI adoption and sustainable engineering practices. Teams that can demonstrate ROI while maintaining team health and code quality will set the standard for the industry, making strategic upskilling and clear business alignment more critical than ever.

AI-as-a-Service will accelerate AI adoption: AI-as-a-Service (AlaaS), which is the delivery of AI tools, apps, and capabilities as a cloud-based service, will emerge as a game-changer in 2025, making AI implementation more accessible and cost-effective for organizations facing adoption hurdles. By removing the need for large upfront investments in hardware and development, AIaaS allows companies to bolt AI capabilities onto existing systems with built-in security and governance features.

Now, instead of requiring developers and engineering teams to build and maintain their own AI systems, they can focus on solving business problems rather than building AI infrastructure from scratch. This also means teams can scale their AI initiatives based on their needs and select AI tools or services tailored to specific use cases. For example, a healthcare provider could use AlaaS to quickly stand up a diagnostic tool that analyzes medical images for early disease detection, while a large retailer can rapidly deploy GenAI-powered chatbots that pull from warranty and return policies to enhance customer service.

The AIaaS revolution also creates new opportunities for engineers to develop and monetize their own AI applications within cloud-based platforms. Similar to how mobile app stores transformed software distribution, AIaaS platforms will enable a new generation of AI-powered startups and create additional revenue streams for developers.

Manufacturing’s AI sweet spot

What does a sweet spot for AI adoption and engineering sustainability look like for a small or medium sized manufacturer that doesn’t have the scale to dedicate an executive to managing this?

“For small and medium manufacturers, the AI adoption sweet spot lies in focusing on practical, high-impact projects with clear ROI and avoiding overly complex, experimental initiatives,” Habib says, citing examples such as using conversational AI interfaces to make ad hoc data and analytics more accessible, creating agents to automate tasks like quality inspections, or implementing retrieval-augmented generation (RAG) systems to search, summarize, and recommend actions from knowledge bases.

At a small manufacturer, RAG systems could be used to improve customer support, technical documentation, or internal knowledge management. For example, an AI-powered chatbot could retrieve relevant technical manuals or past troubleshooting cases to answer customer inquiries more accurately. Additionally, RAG systems could assist with automated reporting or data analysis by generating insights from production data or historical reports, helping the manufacturer make better decisions.

Habib says manufacturers will leverage AIaaS through readily available cloud solutions that integrate with their existing operations. For example, they could use AI-powered demand forecasting services to optimize inventory levels, implement digital twin simulations to test process changes before deployment, or utilize natural language AI to automatically generate technical documentation from production data.

He adds that these solutions can be implemented incrementally, with costs scaling based on actual usage, making AI adoption more manageable and cost-effective than building custom solutions from scratch.

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Both the former Biden and now Trump Administration’s rejection of the proposed buyout of U.S. Steel by Nippon Steel will represent a turning point for the iconic American corporation. Nippon Steel has the investment capital and the technology to turn around U.S. Steel, but political considerations, especially in the new administration, mean that the future of U.S. Steel is very much in doubt.

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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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