Far from being an inconvenience, shutdowns are critical opportunities to ensure that systems run smoothly and safely all year round. Safety, reliability, resilience, efficiency and sustainability, understanding and effectively implementing annual maintenance shutdowns can be a game-changer.

Download now to explore 10 essential steps, future trends, and strategies for overcoming shutdown challenges.

Your download is sponsored by Schneider Electric.

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In this article I’m going back to the basics of product lifecycle management (PLM) by exploring two fundamental questions: Who uses PLM?  And what are some of its challenges?  

This is my third article on PLM basics for Engineering.com. Previously, I wrote Answering 3 Top PLM Questions and Why Every Enterprise Needs Its Own Digital Twins.  

A common thread across all these articles is the importance of collaboration and innovation.  PLM supports these vital enterprise processes as no other technology can do.  Without a well-defined PLM strategy and associated enabling technologies, the gathering and management of data, insights, and inspiration is never reliable.  Only PLM-enabled collaboration and innovation can ensure the long-term sustainability of an enterprise.

Since the first two articles were written, the importance of PLM to enterprise-scale digital transformation and vice versa has become more widely recognized. But PLM’s message is often lost in noisy and cluttered marketplaces that change at ever increasing rates.  To keep pace, PLM solution providers come up with elaborate new tools and add-on capabilities that make digital twins more powerful; digital threads more all-encompassing; end-to-end-end connectivity reach further and deeper because both the ends and the beginnings of products’ lifecycles grow hazier amid expanded capabilities.  At the same time, PLM is being extended into new areas of the enterprise, some of which have only tenuous links to the everyday understanding of “product.”   In these uncertain times, going back to basics has historically proven to be a wise policy. 

All three of these back-to-basics articles drew inspiration from Answerthepublic.com, a marketing-focused platform that unearths user questions commonly entered into search engines.

Who Should be Using PLM?

This question addresses why PLM is so widely used and appreciated, even without deeper understanding of it. PLM is probably the most widely implemented among the myriad tools engineers rely on—standalone CAD included.  New-product developers in all industries turn to PLM at every stage of their work:

•  Creating, redeveloping, and enhancing the enterprise’s products, services, assets, or systems, both physical and digital.

•  Creating, developing, and enhancing processes.

•  Enhancing and extending enterprise connectivity.

•  Delving into supply chain management to cope with the variants in all of the above.

•  Everyone, technically trained or not, who supports these engineers.

If you are using PLM, the aim should be extending the use of it from product development throughout the extended enterprise, including the enablement of:

Digital twins, which are virtual representations—digital surrogates—of physical assets (or services, or even manufacturing systems and the organization itself) that exploit data flows into and out of that asset.  A digital twin of a product typically holds geometry and representations of materials, components, and behavior through the asset’s multiple iterations—as-designed, as-produced, and as-maintained.

Digital threads, which are webs of decisions and myriad links that reach all the data, decisions, and processes that create, maintain, and leverage digital twins from design engineering through production, sales, service, support, and warranties.

End-to-end (E-2-E) lifecycle connectivity, which reaches and joins everything relevant to each digital twin and its digital threads from initial ideation through end-of-life and disposal or remanufacturing and repurposing.

Digital transformation, which is rendering/converting all the enterprise’s data to get rid of bothersome formats and silos that prevent data and information from being freely accessed, used, shared, and reused; the digital transformation of an organization’s product lifecycle is thus a powerful enabler of collaboration and innovation.

What are some challenges of PLM enablement?

As with any transformational technology, implementers and project managers must overcome multiple challenges.  Because PLM’s broad capabilities and toolsets reach deep into the enterprise’s data, these challenges sometimes overwhelm the resources allocated to the implementation; start-up dates are missed, and deadlines are blown.

Like any large-impact technology implementation, some of the challenging aspects of PLM include:

•  Significant investment in money, time, and resources that requires careful monitoring and control.

•  Intensive planning, system-to-system accommodations, and staff retraining across the enterprise.

•  Tightly focused efforts for digital transformation to deal with unformatted data and information while facilitating access to departmental data silos.

•  Similarly, a tight focus on dealing with unexpected job complexity that can frustrate new users.

•  Persistence during a sometimes tedious implementation with a nonstop focus on priorities.

•  Continuous updates for top management so they aren’t tempted to reassign resources and reallocate funds.

•  Continuous evaluation and improvement throughout the continued usage of the new digital PLM-enabling technologies, commonly called “staying the course.”

Every marketplace is constantly reconfiguring itself, driven by countless innovations. This can disrupt enterprise-scale collaboration and thwart innovation. Gathering and managing data and insights can become so complicated that their use becomes unreliable—or worse.

Among the consequences of these reconfigurations is the fragmentation of PLM.  PLM implementation challenges have motivated a handful of software startups to offer toolkits for digital twins, digital threads, and even enhanced connectivity as stand-alone capabilities.  Marketed as sufficient in themselves for everyday user tasks, these toolkits are being tacked on to information technology, operational technology, engineering technology, and other top-of-the-enterprise platforms and systems. 

I must caution that none of these narrow offerings have PLM’s powerful and widely used capabilities to foster true innovation and collaboration, or at least not to the extent necessary.  And only collaboration and innovation can ensure the long-term sustainability of the enterprise.

The underlying theme running through all three of these articles is enabling and enhancing collaboration with PLM, and, as noted, no other technology can do this. Gathering and managing data, insights, and inspiration can never be reliable without PLM and the digital transformation it enables. In turn, only with the digital enablement of PLM can the long-term sustainability of the enterprise be assured.

And this is why understanding the basics of PLM is so critical.

The post Understanding PLM: who uses it, why they use it and its challenges appeared first on Engineering.com.

On April 2, 2025, the U.S. government announced a sweeping new tariff regime that impacts products produced from virtually every country in the world. In an official release, the White House says the move is meant to reprioritize manufacturing in the US. While many economists believe the new tariffs will not produce this result, it doesn’t change the fact that most US manufacturers will have some significant math in their immediate future.

If you buy finished goods, this is likely not a complicated feat. But if you buy raw materials and components from suppliers in other countries and then use those materials and components to make your products, you have a much heavier lift.

Hopefully your company has maintained a reasonable digital transformation investment strategy and you have one or even several digital assets that will make this process much easier and far more accurate than any manual process—especially once you have to deduce if you can absorb some of the costs or have to pass them on to customers.

If you are stuck scrolling through spreadsheets to figure out your exposure, have fun and good luck.

Here is a list of digital solutions commonly found in manufacturing, and how you can use them to find your tariff exposure, calculate your additional spend and decide if it’s worth eating any increase:

Enterprise Resource Planning (ERP)
Purpose: Centralized financial, inventory, and operational data management

Role in Tariffs:

• Provides financial visibility into product and material costs
• Tracks landed costs for imported components
• Supports decision-making on pricing adjustments
• Inventory tracking, but (near) real-time supply chain risk assessments may require additional risk management tools

Supply Chain Management (SCM)
Purpose: Optimizes procurement, logistics, and supplier management

Role in Tariffs:

• Models tariff impact on supply chain flows (e.g., supplier costs, lead times)
• Optimizes sourcing strategies to minimize cost increases
• Supports trade route and logistics adjustments to avoid tariff-heavy regions
• Tariff-specific impact simulations may require integration with ERP or specialized tariff analysis tools

Trade Compliance and Tariff Management Tools
Purpose: Ensures compliance with international trade laws and updates tariff classifications

Role in Tariffs:

• Tracks and updates tariff changes in response to regulatory updates
• Automates classification of goods under the correct Harmonized System (HS) codes
• Supports compliance audits and documentation for trade regulations
• Forecasting financial impact of tariff changes requires integration with ERP or BI systems

Business Intelligence (BI) and Predictive Analytics
Purpose: Data visualization and financial impact analysis

Role in Tariffs:

• Analyzes historical and real-time cost impacts of tariffs
• Models financial scenarios to predict margin impacts
• Integrates with ERP and trade compliance data to provide actionable insights
• Supports strategic decision-making on pricing adjustments and supplier shifts

Pricing Optimization Software
Purpose: Adjusts product pricing based on market conditions and cost fluctuations

Role in Tariffs:

• Determines whether tariff costs should be absorbed or passed on to customers
• Optimizes pricing strategies based on competitive market data
• Prevents margin erosion by aligning pricing with demand sensitivity

Product Lifecycle Management (PLM)
Purpose: Manages product design, BOMs, and supplier data

Role in Tariffs:

• Identifies which materials and components are subject to tariffs
• Supports product redesign efforts to reduce reliance on high-tariff materials
• Stores country-of-origin and trade compliance documentation
• Can be involved in redesigning products to mitigate tariff impacts

Digital Twins and Scenario Planning
Purpose: Virtual simulation of manufacturing operations for efficiency and resilience

Role in Tariffs:

• Simulates supply chain resilience strategies in response to tariff disruptions
• Models operational efficiencies to offset increased costs
• Tests alternative sourcing and logistics adjustments before implementation
• Less directly involved in product redesign than PLM tools

The post Technology rundown for analyzing tariff impact in manufacturing appeared first on Engineering.com.

Agentic AI: autonomous decision-making

Agentic AI refers to AI systems that act autonomously based on goals, real-time data, and feedback loops. In manufacturing, this can be seen in self-optimizing production lines, automated quality control, and predictive maintenance. Unlike traditional automation, Agentic AI can adapt to changing conditions and make independent operational decisions without human intervention, improving efficiency and reducing downtime.

Causal AI: understanding cause and effect

Causal AI goes beyond pattern recognition by identifying cause-and-effect relationships in complex manufacturing systems. This AI type is valuable in root cause analysis, process optimization, and failure prediction. Unlike conventional machine learning, which correlates data, Causal AI determines why failures or inefficiencies occur, enabling engineers to implement targeted solutions rather than just responding to symptoms.

LLMs: processing language and documentation

Large Language Models (LLMs), such as GPT-based AI, specialize in natural language processing (NLP). While not directly involved in factory operations, LLMs help automate documentation, generate maintenance reports, assist with troubleshooting, and provide AI-driven technical support. They can summarize engineering papers, create standard operating procedures (SOPs), and improve communication across teams.

Choosing the right AI

Each AI type complements manufacturing processes in unique ways, leading to smarter, more efficient operations.

Manufacturing Example: Causal AI vs. Agentic AI

Use Case: Predicting and preventing machine failures.

How It Works: A factory uses causal AI to analyze historical sensor data from machines.

Instead of just identifying correlations (When temperature rises, breakdowns occur), it determines the cause (Excess vibration due to misalignment leads to overheating, which causes failure).

This allows engineers to intervene proactively by fixing misalignment before a breakdown happens.

Use Case: Autonomous production line optimization.

How It Works: An agentic AI system monitors production efficiency in real-time and autonomously adjusts machine settings for optimal output.

If a machine slows down, the AI dynamically reallocates work to other machines without human intervention.

It learns from past production data and adapts continuously to maximize efficiency while minimizing waste and energy use.

Key Differences:

Causal AI helps engineers understand why failures happen and improve decision-making.

Agentic AI takes direct action, adjusting processes autonomously to optimize performance.

In manufacturing, causal AI and agentic AI serve different but complementary roles.

Hybrid approach: smart factory with both AI types

A factory can integrate both causal and agentic AI. Causal AI analyzes sensor data and finds that high humidity causes metal corrosion, leading to increased friction and machine failure. Agentic AI uses this insight to autonomously adjust humidity controls, slow down machines at risk, and reassign production tasks to avoid downtime. Over time, the system learns and adapts, reducing failures, increasing efficiency, and lowering maintenance costs.

Together, they create self-optimizing factories with minimal human intervention.

The post Understanding AI in manufacturing: agentic AI, causal AI, and LLMs appeared first on Engineering.com.

This progress, while mandated by some policies, has been driven through business action. After all, the shift is not only beneficial to the planet, but also for people and profitability. Research from Gartner underscores this – in 2024, it found that 69% of CEOs view sustainability as a growth opportunity. Consequently, two-thirds of Fortune’s Global 500 companies have now made substantial commitments to tackling climate change across their total value chains.

However, we cannot afford to lose momentum. To reach our sustainability pledges, we must turn our attention to circularity. Alarmingly, the share of global circularity fell from 9.1% in 2018 to 7.2% in 2023. Yet, during this period, resource consumption continued to accelerate, and we have consumed a staggering 500 gigatonnes of resources – 28% of all materials used by humanity since 1900, concentrated in just the last five years.

If circularity is an indicator of environmental progress, we must take bold action to achieve our sustainability targets and continue driving meaningful change.

Discarding the ‘take-make-waste’ economy

Circularity is an economic model that aims to eliminate waste, preserve resources, and reuse materials. A stark contrast to the linear ‘take-make-waste’ model that our current economy is built upon.

Take the life of a bottle of water for example: oil is drilled from the earth and refined into polyethylene terephthalate – take. Then this is moulded into bottles that are filled with water – make. Finally, they are consumed once and then discarded – waste. Just 12% of the world’s plastic bottles are recycled, and meanwhile, the 88% of remaining bottles are tossed into a landfill, where they can take up to 450 years to decompose.

This unsustainable pattern of consumption significantly contributes to greenhouse gas emissions and biodiversity loss. Transitioning to a circular economy could drastically reduce the materials we use by 70%, creating better resource utilization. So, how exactly can a business become more circular?

It all starts with the design

According to the Schneider Electric Circularity framework, the first consideration for achieving circularity is to adopt Eco-Design which directly impacts our product development and innovation. This means creating products for reliability and longevity, rather than quick disposal. Designing and innovating for circularity enables our products to be used better, used longer and used again.

Building on this foundation, businesses must evolve from a traditional and transactional sales approach to an as-a-service model. To support this shift, companies should consider supplementing sales with rental, repair, and support services. This simultaneously extends product lifecycles, reduces waste and creates new growth opportunities. By offering second-hand tech and trade-in programs, companies can meet demand for affordable, eco-friendly options. This model reduces waste, boosts customer loyalty, and drives long-term profits.

Optimizing resources through responsible sourcing

The first principle is use better, which calls for the responsible sourcing of materials to optimize manufacturing and minimize waste. For instance, sourcing best-in-class materials ensures reduced environmental and social impact throughout the supply chain. Schneider Electric products are currently made with 32% recycled materials with the aim of reaching 50% by the end of 2025. In addition, more than half of our sites recover 99% of waste, demonstrating our commitment to using better.

We are also expanding our smart factory network, with two recent facilities in Monterrey and Shanghai. Both sites utilize machine learning-enabled prototyping, smart planning, and GenAI-driven maintenance to boost productivity while driving down resource consumption. This commitment to sustainability and operational efficiency has been recognized by the World Economic Forum who have designated both factories Lighthouse status.

Circularity demands companies to address sustainability in their total value chains. Sustainable supply chain programs have already been launched in various sectors, such as global healthcare, the semiconductor industry, as well as mining, materials, and minerals. These programs help companies responsibly source materials, reduce emissions, and eliminate waste at every link.

Extending product lifecycles

The second principle is to extend the lifespan of products and use longer.Businesses must anticipate the need for equipment by opting for condition-based repair, digitally enabled maintenance, and equipment modernization. Reparability and circularity services, such as Schneider Electric’s EcoFit, can extend asset life by up to 25%.

In fact, our customer ArcelorMittal reconditioned 13 medium voltage switchgears, avoiding the reprocessing of 26 tonnes of material, saving the equivalent of 170 metric tonnes of CO2. Refurbishing existing equipment instead of discarding it reduces waste and emissions while conserving resources.

The third and final principle is to use again. This concept encourages businesses to recirculate products, parts, and materials within the economy; effectively refurbishing and reselling assets that have reached the end of their initial use.  Schneider Electric’s Altivar drive sends damaged modules to our repair centres for testing. Once refurbished, products come away with the same warranty as new ones, achieving up to 80% savings on resources, energy, and emissions.  

Building a circular business growth framework

Adopting circularity is not just about protecting the planet – it’s a powerful business tool that drives cost savings and growth. By embracing circularity, businesses can differentiate themselves, create new value streams, and stay ahead of an increasingly competitive market.

Moreover, global climate and environment regulations will make circularity a necessity, rather than a choice. The EU, for instance, has introduced some of the world’s most comprehensive policies, such as the Circular Economy Action Plan, the Ecodesign for Sustainable Products Regulation (ESPR), the Corporate Sustainability Reporting Directive (CSRD).

Businesses that adopt circularity now will not only proactively prepare to comply with future regulations but also position themselves as leaders, and build a competitive advantage. So you see, circularity isn’t just the right thing to do; it’s the smart thing to do for any forward-thinking organization.

Frédéric Godemel is the Executive Vice-President for Energy Management and member of Schneider Electric’s Executive Committee. 

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Engineered products, particularly consumer goods, have improved continuously since the advent of mass production century and a half ago but that process is accelerating as more devices become software defined. Assembling, configuring and preparing software defined devices for the end user usually require some kind of instruction manual, and one way that manufacturers save money is to move those manuals online. The removal of the constraints of ink on paper means that user manuals can be as long as the manufacturer wishes, and the result has been more, instead of less complexity for the product end user. It doesn’t have to be that way. 

***
Access all episodes of End of the Line on Engineering TV along with all of our other series.

The post Why The Most Important Part of Any Engineered Product, Isn’t the Product  appeared first on Engineering.com.

Survey results highlighted escalating trade conflicts, rising global tensions, and persistent supply chain disruptions are placing unprecedented pressure on manufacturing and supply chain leaders.

Despite these challenges, Fictiv says the report also shows momentum in onshoring, AI adoption, and increasing reliance on digital manufacturing platforms.

“Concerns about tariffs and trade wars are clearly top of mind for manufacturing and supply chain leaders,” says Dave Evans, co-founder and CEO of Fictiv. “We’re seeing a level of global uncertainty and supply chain disruption we haven’t seen since 2020. However, the report also shows that companies are embracing new technologies and strategies to build more resilient and agile supply chains.”

Key Findings

• Global Uncertainty on the Rise: 96% are concerned about the impact of current trade policies, and 93% believe trade wars will escalate in 2025.
• Supply Chain Disruptions Accelerating: 77% report a lack of resources limits their ability to manage the supply chain effectively, and 68% prioritize onshoring as a key strategy.
• Scaling Production More Difficult: 91% face barriers to product innovation, and 86% report sourcing parts takes time away from new product introduction. However, 90% see digital manufacturing platforms as essential.
• Sustainability Takes Hold: 95% report that weather and climate events impact their supply chain strategy, and 91% have sustainability initiatives and governance in place.
• AI Advances: 87% report advanced levels of AI maturity, and 94% use AI for manufacturing and supply chain operations.

Fictiv says its report underscores the need for manufacturers to embrace innovation and adaptability by building more resilient supply chains, leveraging digital manufacturing, embracing AI to transform operations from inventory management to product design, and prioritizing sustainability.

This is the tenth year Fictiv has commissioned the report.

Download the full 2025 State of Manufacturing & Supply Chain Report here.

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Initial investment costs

Digital prototyping requires advanced software, hardware, and integration with existing systems, which can be expensive and require a team of engineers with strong expertise in a number of disciplines. Companies must invest in high-performance computing (HPC) resources, VR/AR headsets, simulation software and cloud storage. Small and mid-sized manufacturers will need a focused plan to deal with the cost of licensing, training, and infrastructure upgrades necessary to support digital prototyping workflows.

Software and hardware compatibility

Integrating digital prototyping tools with existing CAD, PLM, and ERP systems is complex. Many companies rely on legacy software that lacks seamless compatibility with modern digital platforms. Additionally, hardware limitations, such as insufficient GPU power for real-time rendering or VR simulation, can hinder performance.

Ensuring interoperability across different systems requires extensive customization, middleware solutions, and adopting standardized file formats. Converting models between different software such as CAD to a simulation suite, may cause loss of parametric data, constraints, or surface definitions. And older versions of software may not support files created in newer versions, leading to workflow bottlenecks.

Learning curve and skill gaps

Digital prototyping tools involve complex 3D modeling, real-time simulation, and data analytics, which require specialized expertise. Many manufacturing engineers are trained in traditional CAD and FEA simulations but may lack experience with VR, AI-driven simulations, or generative design. Companies must invest in training programs and hire or upskill personnel, which can slow adoption.

Data management and cybersecurity

Digital prototypes generate vast amounts of data in the form of design files, simulation data, and testing results which require efficient storage and version control. Managing this data within PLM and cloud systems introduces risks related to cybersecurity, intellectual property theft, and compliance with industry regulations (such as ITAR for aerospace manufacturing). Companies must implement strong encryption, access control, and secure cloud storage solutions to protect sensitive information.

Computational limitations for simulations

Real-time physics simulations, fluid dynamics (CFD), and stress testing (FEA) require high computational power. Companies using VR-based digital prototyping may experience latency issues, especially with large, complex assemblies. Implementing Level of Detail (LOD) algorithms, cloud-based processing, and GPU acceleration can help mitigate performance bottlenecks.

Validation and regulatory compliance

Some industries, such as aerospace, automotive, and medical device manufacturing, require extensive physical testing for regulatory approvals. Digital prototypes, while highly accurate, may not always replace real-world durability tests, crash simulations, or clinical trials. Companies must ensure that their digital twin models are validated against physical results to comply with industry regulations.

Yes, there are always challenges when investing in next generation technology. However, companies that strategically invest in digital prototyping, train their workforce, and optimize data security and processing power can unlock substantial benefits. As cloud computing, AI, and VR technology continue to evolve, overcoming these obstacles will become more manageable, leading to faster product development, cost savings, and improved manufacturing efficiency.

The post What challenges do companies face when adopting digital prototyping? appeared first on Engineering.com.

By integrating VR with CAD (Computer-Aided Design) software, PLM (Product Lifecycle Management) systems, and real-time physics simulations, engineers can gain unparalleled insights into product behavior before physical prototyping. But there are several technical intricacies of VR in relation to digital prototyping and its applications, integration challenges, and benefits to manufacturing.

Integration of VR with CAD and PLM

Modern VR-based digital prototyping heavily relies on CAD and PLM integration. CAD software exports 3D models in VR-compatible formats (such as FBX, OBJ, GLTF) that allow engineers to examine prototypes within the virtual environment. When paired with PLM systems, engineers can track version histories, collaborate in real-time, and integrate design modifications directly into the production workflow. This connectivity ensures that design iterations remain structured and accessible to all stakeholders.

To enhance VR compatibility, many CAD software solutions incorporate native VR apps or VR plug-ins, which allow direct visualization of engineering-grade 3D models in VR without cumbersome file conversions. These tools support parametric modeling and real-time geometric modifications, ensuring high fidelity in virtual environments.

Real-time physics-based simulations

VR goes beyond static visualization by enabling real-time physics-based simulations that engineers use to assess product performance under various conditions.

Simulations can include:

Structural Analysis: Finite Element Analysis (FEA) simulations are rendered in VR, allowing engineers to visually inspect stress distributions and failure points in a virtual space.

Fluid Dynamics: VR-integrated Computational Fluid Dynamics (CFD) simulations enable engineers to observe airflow patterns, heat dissipation, and liquid flow behaviors from a first-person perspective.

Material Deformation: Soft-body physics can replicate material flexing, bending, and breaking under applied forces, giving engineers an intuitive understanding of how materials respond to different loads.

Haptic feedback and realistic interaction

One limitation of traditional digital prototyping is the inability to physically interact with the model. VR overcomes this challenge by incorporating haptic feedback devices, which simulate tactile sensations and resistance. Such devices allow engineers to “feel” surfaces, textures, and resistances as they manipulate virtual components.

In addition to haptics, real-time rendering techniques such as ray tracing and shadow mapping improve visual realism in VR environments. High-performance GPUs enable photorealistic rendering, ensuring that materials, lighting conditions, and reflections closely mimic real-world properties. By integrating physics engines developed by a number of different companies, VR prototypes can react dynamically to user interactions, providing a near-physical testing experience before production.

Design validation and ergonomics testing

Manufacturing engineers can use VR for comprehensive design validation before committing to expensive tooling and fabrication. Dimensional accuracy checks assess tolerances and fitment by placing components in a simulated assembly line, while ergonomics assessment using VR simulations allow engineers to test human-machine interactions, ensuring that equipment is comfortable and efficient for operators.

Instead of relying on physical mock-ups, engineers can conduct virtual usability studies, allowing stakeholders to evaluate user experience and product functionality in various conditions.

Industry-specific applications of VR prototyping

Automotive: Car manufacturers use VR to perform full-scale vehicle prototyping, enabling designers to test aerodynamics, visibility, and cockpit ergonomics before building physical models.

Aerospace: Engineers visualize and test complex aircraft components, such as turbine blades and fuselage assemblies, in VR environments with real-world physics simulations.

Consumer electronics: Companies test user interfaces and device form factors in VR to refine designs based on virtual consumer feedback.

Medical device manufacturing: VR enables precise simulation of surgical instruments and implants, helping engineers refine designs for biomechanical compatibility.

Technical challenges and solutions in VR prototyping

Despite its advantages, VR prototyping presents several technical challenges. Combining multiple engineering datasets (FEA, CFD, PLM) into a cohesive VR simulation can be challenging, but standardized file formats (USD, STEP, and FBX) streamline data exchange across platforms.

Running detailed CAD models in VR can be costly, as the high computation output requires powerful GPUs and optimized software workflows. Using Level of Detail (LOD) algorithms and real-time model decimation can improve performance without sacrificing accuracy. These algorithms optimize performance by adjusting the complexity of 3D models based on their distance from the viewer or their importance in the scene. Here’s how they work:

Dynamic mesh simplification – LOD algorithms swap high-detail models for lower-poly versions when objects are further away, reducing GPU load without affecting perceived visual quality.

Adaptive rendering – By prioritizing detail only where needed (on user-interacted components), LOD ensures real-time rendering efficiency.

Improved frame rates – LOD prevents frame rate drops by decreasing the polygon count in non-critical areas, ensuring smooth VR interactions at 90+ FPS (critical for reducing motion sickness).

Memory optimization – Less-detailed models free up GPU memory, allowing for larger assemblies and complex simulations without performance bottlenecks.

Hybrid use with culling techniques – Combined with occlusion culling (hiding objects not in view), LOD further enhances computational efficiency.

Future trends

The future of VR-based digital prototyping in manufacturing is set to become even more powerful with advancements in AI-driven automation, cloud-based collaboration, and hybrid AR-VR environments.

AI-driven automation integrates machine learning algorithms that analyze designs in real time to detect structural weaknesses, suggest material optimizations, and even predict potential manufacturing defects before physical prototyping begins. By continuously learning from past designs and simulations, AI can help engineers refine product performance and reduce costly trial-and-error iterations. This capability will significantly shorten development cycles while improving the reliability and manufacturability of new products.

In addition, cloud-based VR collaboration will redefine how global engineering teams interact with digital prototypes. Instead of requiring high-end local hardware, cloud-rendered virtual workspaces will allow engineers to access and manipulate detailed VR models from anywhere in the world. This technology will enable real-time design reviews, remote troubleshooting, and seamless integration with PLM (Product Lifecycle Management) systems, ensuring that teams remain aligned even when working across different locations. Cloud-based VR will also facilitate large-scale manufacturing projects by enabling multiple stakeholders—from designers to production managers—to interact with virtual prototypes without needing specialized workstations.

Furthermore, the rise of AR-VR hybrid environments will bridge the gap between digital and physical prototyping. By overlaying VR-generated 3D models onto real-world objects using Augmented Reality (AR), engineers will be able to test virtual components in real-world settings without requiring a full digital or physical setup. This will be particularly useful for ergonomics testing, assembly validation, and factory layout optimization, where seeing how a virtual component interacts with real machinery or workspace constraints is crucial.

As these technologies continue to evolve, VR-based digital prototyping will become an intelligent, collaborative, and highly integrated system, streamlining manufacturing workflows and enabling faster, smarter product development.

The post What role does virtual reality play in digital prototyping? appeared first on Engineering.com.

“We began this effort based on feedback from educators. Industry 4.0 is here and companies are struggling because they’re not workforce ready. Much of the knowledge being taught in the classroom is not geared to digital strategies,” says Pooja Thakkar Singh, program manager for the American Society of Mechanical Engineers. The courses are meant for mechanical engineers, manufacturing engineers and Computer Numerical Control (CNC) machinists. The sixth course contains examples of R&D work that show how to apply acquired knowledge to projects using Autodesk’s Fusion software. The overall goal of the curriculum is to empower educators and equip students and professionals with in-demand skills that will advance their careers and support modern manufacturing.

“The courses run between 30 and 45 minutes, with the underlay of the Fusion exercises being a little bit deeper. The software is very easy to use. This is advanced manufacturing,” says Debra Pothier, senior manager for Autodesk for strategy for architecture, engineering, construction and operations (AECO) and a partnership owner for ASME.

For example, the design for sustainability course covers how value chains and supply chains impact the environment and product lifecycles influence design solutions. It also explores the importance of the “Triple Bottom Line,” a framework that measures social, environmental and financial benefit.

“There were requests to make the courses as customizable as possible due to the changing landscape of Industry 4.0. We accomplished this by integrating PowerPoint slide decks and videos that faculty and instructors can switch out to keep up with the latest information,” says Singh.

Another way to customize the courses is to use Doodly, an animation software. The program can create dynamic virtual board drawings for videos. 

“Then you can remake the courses by re-recording and re-downloading content as many times as you need,” says Singh.

Critical ingredients for the courses

One of the key components of the courses is explanations of digital manufacturing skills that apply to mechanical engineering, manufacturing engineering and CNC machining, like CAM 2.5, 3-axis milling and simulation.

“Doing this impactfully involved getting all the stakeholders together in one virtual classroom. We had to ask ourselves what students were looking for and what they were passionate about. We also had to cover the challenges that industry experts were facing. We didn’t know those until we heard that from the sources,” says Singh.

Another key component is a simultaneous focus on hard skills, such as data analysis, and soft skills, like collaboration.

“We demonstrate how Fusion software facilitates the communication of generative design AI outputs, bridging the gap between technical skill and practical application,” says Curt Chan, strategic partnerships manager for Autodesk.

A third key component is explanations of the enormous impact of AI and how this tool affects the design process. In some situations, generative AI software can handle 90 percent of the programming required for machine part development. A mechanical engineer can then utilize their expertise to finetune the last 10 percent.

AI is like “a whole new toolbelt in a number of ways,” says Jason Love, technology communications manager for Autodesk.

Before AI was widely used, a mechanical engineer designing an assembly might be required to learn how to draw a diagram of the parts in an assembly.

“Now there are tools in place that with the click of a mouse, create those 2D diagrams from your 3D models. It falls to the human engineer to double check the accuracy of those drawings,” says Love.

Some educators may be unfamiliar with such changes or the new workflow itself. The courses address these problems by ensuring viewers grasp how many options AI creates.

“Faculty are going to have to teach the entire process, not their little silo,” says Pothier.

How and why the curriculum works

The six courses are not critically tied to one another. This gives an educator flexibility to “plug and play.” A manager could assign a course when an employee has downtime or an educator wants to offer extra credit.

“Through conversations with educators, I’ve observed many different ways they are planning and implementing the curriculum,” says Chan.

The models have a loose sequential order. For example, the course that serves as an introduction defines the term “Industry 4.0.” It also explains the driving forces behind production processes and relates the remaining challenges from Industry 3.0. A later course on digital literacy and data skills provides participants with an understanding of Industry 4.0 technologies and data measurements. This course also gives participants an understanding of the role of big data and how numerical insights drive manufacturing processes.

Each course has a self-assessment that learners can complete to earn a certificate. Participants can earn credit or mark their skills as upgraded after completing certain courses or the entire set.

One factor contributing to the popularity of the courses is the shift during the past five years to the use of online education, for both synchronous and asynchronous learning. This is partly due to the influence of the COVID-19 pandemic. Students and engineers have also become more well versed and more highly motivated to utilize knowledge they have drawn from online content. 

ASME and Autodesk’s history of partnership

The Industry 4.0 curriculum is the latest result of ASME’s and Autodesk’s history of teamwork. The two entities have been working together since 2021. That year, Autodesk Foundation, Autodesk’s philanthropic arm, began donating funds to ASME’s Engineering for Change (E4C) research fellowship program.

As of late February 2025, the Autodesk Foundation has funded over 100 E4C fellowships to support nonprofits and startups in a range of fields. These include energy and materials development, health and resilience systems and work and prosperity opportunities. The donations to E4C have also expanded the reach and impact of Autodesk Foundation’s Impact internship program. That program connects individuals in the Autodesk Foundation portfolio with new engineers.

In 2022, ASME and Autodesk released the results of a collaborative multiphase research project on the future of manufacturing. The effort involved a research study conducted between August 2021 through May 2022. The report on the study investigated and identified the future workflows and skills required for mechanical engineering, manufacturing engineering and CNC machinist roles.

“This was the project that was the basis for the six-course curriculum. The second phase of the project was the curriculum design and creation. We piloted the first four courses by launching a competition relating to sustainability and ocean clean-up,” says Singh.

The Autodesk-hosted event featured university teams designing an autonomous robot to clean up trash from the ocean. Students relied on skills they had learned from the courses.

One of the teams in the competition, Wissen Marinos, was formed of students from India’s National Institute of Technology Silchar. Wissen Marinos team captain Pratisruti Buragohain says participating in the competition enabled team members to develop problem-solving abilities, technical skills and soft skills.

“Despite facing various hurdles along the way, we tackled each one of them strategically and with a meticulous determination. In essence, our experience throughout the competition bestowed upon invaluable lessons, equipping us with enhanced design proficiency, research skills and efficient problem-solving strategies,” says Buragohain.

Additional steps for the curriculum have included the translation and localization of the courses into Japanese and German. ASME and Autodesk are tracking how widely the curriculum is used and asking what information students and engineers are learning from it.

“Any curriculum takes time. It’s going to take time to drive it the use of this curriculum. That’s about keeping a pulse on the industry, hearing what they have to say and what Autodesk’s customers have to say,” says Chan.

Pothier says Autodesk is striving to close the skills gap and be a trusted partner to engineering firms.

“We give the underpinning of, “This is how you do it with Fusion and we’re giving you modular pieces.” We’re giving it to universities and firms in a way that students really want to consume it. Our team is very passionate because we feel if you’re going to be sending your kids to school, they need those skills today,” says Pothier.

View the courses at: https://www.autodesk.com/learn/ondemand/collection/asme-manufacturing-education-courses.

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