It is easy to take for granted actual scales when zoomed in on modern high-res flat-screens, but in reality, the surfaces of PCB boards are not two-dimensional with precisely defined edges. The layers of copper, solder mask, silkscreen and non-planar surface finishes are raised and allowing for solder mask expansion and dams can reduce space between elements even further.Allowing for sufficient component clearances is something PCB designers know all about, but many inexperienced and experienced designers still end up digging their own graves to avoid a little bit of extra rerouting.&nbsp;As careful engineer Xiao Peng learned, the perfect layout depends not only on one’s experience but also on one’s toolbox. Various tools are freely available to provide a systematic and automated approach to detecting such issues and improving the layout overall.&nbsp;Xiao Peng is a PCB layout engineer with over a decade of industrial experience under his belt from layout to production. But his recent experience caused him to doubt his abilities.As per his standard workflow, Xiao Peng’s pre-production checks consist of meticulous design review covering signal integrity, functionality and performance, design for manufacture (DFM) and design for assembly (DFA). However, for this particular batch undergoing wave soldering, the incidence of cold solder joints and insufficient through-hole barrel fill defects was unusually high. Consequently, the batch failed to meet IPC Class 2 standards.IPC Class 2 standards define strict requirements for solder joints. For through-hole joints, the barrel must have a minimum solder fill ratio of 75%, with 25% sinking allowed.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzExMDE3OTcwNzA3LVdlaXhpbiBJbWFnZV8yMDI0MDMyMTE0MTIyNi5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 478px;" class="fr-fic fr-dib">Xiao Peng suspected that the nearby surface mount resistor could be the culprit and upon inspection, found that the distance between the pads was only 0.2mm. This spacing was more than enough for solder mask dams to form and prevent solder bridges, however, the effect on the through-hole joint was not clear. After consulting the assembly technicians, it was found that the board was suffering from shadowing effects.&nbsp;PCB shadowing is the phenomenon whereby taller parts cast shadows on other lower neighboring parts which can have consequences on soldering, thermal management and signal integrity. For wave soldering, the nearby components can physically block molten solder from reaching certain areas of the PCB, in this case, the through-hole barrels.&nbsp;To remedy the issue, the technicians recommended a clearance of 3mm to mitigate the effects of shadowing, and even more for taller components.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzExMDE4MDY4OTU1LUhRREZNIENvbXBvbmVudCBzcGFjaW5nLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib">With wave soldering, components can affect neighboring solder joints even with the presence of a solder mask dam.At the same time, HQDFM, the PCB design analysis software from HQ Electronics was recommended to Xiao Peng, which claims to be able to detect PCB shadowing issues and many other DFM issues. HQDFM indeed highlighted the spacing issue among other errors. After following the suggestions, the new design effectively eliminated cold solder and barrel filling defects.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzExMDE4MTIxNjM1LUhRREZNIENoaW5lc2UgVmVyc2lvbiBTY3JlZW5zaG90IG9mIGNvbXBvbmVudCBzcGFjaW5nIHZpb2xhdGlvbi5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 100%px;" class="fr-fic fr-dib">Tight component spacing detected in HQDFM software (Chinese Version)As Xiao Peng discovered, even the most experienced engineers could do with extra assistance here and then. Unlike in China, where engineers can freely contact their manufacturers and expertise is common, overseas designers outsourcing overseas will have time differences, language barriers and limited resources to contend with.<blockquote>“I am deeply aware of the close relationship between quality and design. A good design can not only improve product performance and quality but also reduce production costs and difficulty. Inbuilt DRCs are one way of detecting issues, but they are often limited and are based on black-and-white values you have to feed in yourself. HQDFM on the other hand is powerful, informative and explicit in its goals to reduce manufacturing issues and improve reliability even if you don’t use HQ for production.&nbsp;</blockquote><blockquote>I especially liked the fact that HQDFM is free yet powerful. HQDFM looks at everything from bare board spacings to component heights and the impact on wave soldering and gives specific recommendations and figures. There aren’t many tools outside of EDA programs that can be considered essential in the designer’s toolbox but HQDFM is one of them and I will be using it extensively from now on.”&nbsp;- Xiao Peng</blockquote><h2>Common spacing pitfalls and guidelines</h2>Knowing what to look out for and the potential impact is the first step for PCB designers to improve their DFM evaluation skills which can then be incorporated at the earliest stage of design. The second is the ability to quickly detect issues, which can be accomplished with tools like HQDFM. HQDFM helps to achieve both of these goals through education and detection. Some of the component spacing issues covered by HQDFM include:<h3>Through-hole to Surface Mount Pad Spacing</h3>In Xiao Peng’s case, the close proximity of the through-hole and surface mount pads resulted in the through-hole being inadequately filled during wave soldering. For reflow soldering, insufficient spacing between through-holes, opened vias, test points, metallic mounting holes, and surface mount pads can result in solder being pulled away from the surface mount pad, resulting in poor wetting and making solder defects such as tombstones more likely.&nbsp; If spacing is limited, consider putting vias on pads, but these should be plugged and capped to prevent solder from leaking through the via. Be aware that via plugging and capping require specialist machinery and come at an additional cost.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzExMDE4NDk4MTE0LUhRREZNIHZpYS1pbi1wYWQgc3BhY2luZy5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 426px;" class="fr-fic fr-dib">Via on SMD pad illustration - HQDFM<h3 id="isPasted">Solder Mask Dams</h3>Any two objects that are so close together that they do not leave room for solder mask dams are at risk of solder bridges. Solder mask, or also accurately named solder&nbsp;resist, is your best friend in both SMT and wave soldering as it helps to prevent solder from building up where it is not wanted. For fine-pitch components, especially chips with underside pads, dams can be a lifesaver. For a single reflow, you can probably get away with suitably sized stencil openings, but when it comes to rework and manual soldering, the lack of dams will require greater skill and patienceharder. Pro-tip: most PCB manufacturers have their own solder mask expansion values and will not necessarily follow the lengths in your Gerber files. So a more accurate way of determining if the spacing is large enough for dams to form is to follow pad-to-pad (copper to copper) spacing from manufacturers. Also, remember that values typically vary according to solder mask color. If color is not important, sticking with regular green will produce the best results. If you need to push the limits further, use ENIG plating for a planar pad surface.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzExMDE4NTM2NTYyLUhRREZNIHNvbGRlciBtYXNrIGRhbS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 400px;" class="fr-fic fr-dib">SMD Pad Spacing illustration - HQDFM<h3 id="isPasted">Solder Mask Opening to Trace Spacing</h3>Traces positioned too close to pads may inadvertently be exposed as a result of solder mask expansion and be at risk of shorting. Ensure traces are sufficiently covered to protect the trace and ensure it remains sealed by the solder mask film.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzExMDE4NjIyNDk4LUhRREZNIFNvbGRlciBtYXNrLXRvLXRyYWNlIHNwYWNpbmcucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 410px;" class="fr-fic fr-dib">Solder mask-to-trace spacing illustration - HQDFM<h3 id="isPasted">Through-hole Spacing</h3>Whether soldered manually or automated, through-hole leads need to be reachable, be it by a soldering iron, wave, lead clippers, etc. Therefore the area around them must be void of obstacles such as tall components and other leads. Insufficient adjacent and overhead clearance could result in tricky soldering positions and accidental melting of other components or oneself.&nbsp;Lower on the ground, leads of adjacent through-hole parts should be far enough to prevent solder bridges. For through-hole parts, solder mask dams are a must, which can be achieved by reducing the diameter of pads or shaving material off the edges.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzExMDE4NjU2MTE0LUhRREZNIFNwYWNpbmctaGVpZ2h0IHJhdGlvLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 399px;" class="fr-fic fr-dib">Spacing/height ratio - HQDFM<h3 id="isPasted">Wave Soldering Considerations</h3>Wave soldering’s main advantage is the ability to solder through-hole parts in volume but comes at a cost. Layout engineers have a whole host of extra restrictions to compete with, limiting placement options and possibly ruling out higher-density designs. In addition to leaving extra space between parts, engineers have to constantly consider the direction of the incoming wave, shadowing effects and the use of glue or fixtures for mixed assembly.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzExMDIxNTkxMzU1LVdhdmUgc29sZGVyaW5nIHBhbGxldCBhbmQgYm9hcmQgd2l0aCByZWQgZ2x1ZSBkb3RzLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 768px;" class="fr-fic fr-dib">Wave soldering fixture with gaps for through-hole leads and mixed assembly board with red glue dotsAs we have explored, the consequences of something as simple as component spacing can have grave consequences, but as the DFM mantra goes, it’s better to be safe than sorry. Experienced layout engineers and beginners should make it part of their workflow to pay special attention to spacings and use tools such as HQDFM to accelerate mundane tasks and act as a safety net.HQDFM is a free PCB design analysis software covering bare board and populated PCB analysis. With over 200 inspection items under 30 DFM and DFA categories including trace, solder mask and drill clearances, pads dimensions, shorts/opens, drill hole sizes and solder mask dams. HQDFM also provides tools for the designing and manufacturing PCBs including an impedance calculator, panelization tool, footprint, BOM and centroid file checker.Transform your workflow and swiftly integrate DFM methodologies into your product development with the <a href="https://www.nextpcb.com/free-online-gerber-viewer.html" target="_blank" rel="noopener noreferrer">HQDFM</a> desktop suite from HQ Electronics (NextPCB).<h2>References</h2><ol><li><a href="https://www.nextpcb.com/blog/consequences-of-insufficient-spacing-between-components-for-surface-mount-assembly" target="_blank" id="isPasted">Consequences of insufficient spacing between components for surface mount assembly (nextpcb.com)</a></li><li><a href="https://www.nextpcb.com/blog/what-is-dfa-in-design-for-manufacturing-and-assembly" target="_blank" id="isPasted">What is DFA in Design for Manufacturing and Assembly? (nextpcb.com)</a></li></ol>

Adequate component spacing is typically not high on the PCB designer's list of priorities, but oversights can be frustrating and could demand substantial re-work. Here we look at an example, give general guidelines and look at how tools can be used to educate designers and save time.

Consequences of Insufficient Component Spacing in PCB Design: A Case Study

This article was discussed in our <a href="https://www.wevolver.com/profile/the.next.byte" rel="noopener noreferrer" target="_blank">Next Byte podcast</a>.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/ccebc424-3934-4f8d-9421-365318aa11c9?dark=false" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> The full article will continue below.<hr>Text-to-image is soooo 2023.&nbsp;This year we're adding a new dimension to generative AI: text-to-3D CAD models, and we need your help! <h3>The Backdrop</h3>We founded <a href="https://polyspectra.com" target="_blank" rel="noopener noreferrer">polySpectra</a> with the mission to help engineers make their ideas real.&nbsp;For the past seven years, we have been focused on turning&nbsp;bits into atoms. 3D models into rugged end-use parts.Recent advances in AI have given us the opportunity to go one step further: ideas into bits.In this post, I'll share some of our challenges in building <a href="https://neThing.xyz" target="_blank" rel="noopener noreferrer">neThing.xyz</a>, our new generative AI tool for 3D CAD models. The tool converts natural language prompts into code, which is then converted into a 3D model. <h3>The Problem, The Insight, The Vision</h3>Problem: We have been incredibly energized by the pace of innovation in generative AI, but we were unable to find a text-to-3d gen AI tool that was able to create objects that we would actually want to manufacture.Insight: The key insight that led to the invention of neThing.xyz was that AI is actually quite good at writing code, and by training our "code gen" AI on domain specific languages, our AI can produce code that can be rapidly converted into a 3D CAD models.&nbsp;This is a fundamentally different approach than other text-to-3D generative AI, and it has some really interesting implications for the future of AI and CAD. Most of the other apps in this space are using Neural Radiance Fields (<a href="https://en.wikipedia.org/wiki/Neural_radiance_field" target="_blank" rel="noopener noreferrer" id="isPasted">NeRFs</a>), which is basically a combination of text-to-image and then 2D-to-3D. (Lumalabs “Genie” and Commonsense Machines “Cube” are our two favorites.)Vision: ideas =&gt; bits =&gt; atomsWhat if you could instantly generate 3D objects from a brief verbal description? <h3>The Challenges</h3>Teaching AI to understand and generate CAD models presents a unique set of challenges:1. The AI is Blind: Traditional language models (LLMs) are inherently "blind" and lack the ability to visualize. This means that they struggle to interpret or generate visual content, such as CAD models, from text descriptions alone. While multimodal models, which can understand both text and images, offer a potential solution, they are still in the early stages of development. Their ability to accurately generate complex CAD models from textual descriptions is not yet reliable.2. "Code-CAD" is Niche: The field of generating CAD models through code is highly specialized. There are few experts and, consequently, limited examples to train AI models on. This scarcity of data poses a significant barrier to teaching AI the nuances of CAD generation. On this topic, we would like to extend a huge shoutout to the <a href="https://build123d.readthedocs.io/en/latest/" target="_blank" rel="noopener noreferrer">Build123D</a> community for their tremendous generosity in helping us get neThing.xyz off of the ground.&nbsp;3. Multiplicity of Correct Solutions: For any given CAD design prompt, there can be numerous correct solutions, each employing different methods and approaches. For example, just watch the <a href="https://www.youtube.com/TooTallToby" target="_blank" rel="noopener noreferrer">Too Tall Toby</a> "CAD vs. CAD" tournaments (the <a href="https://tootalltoby.com/spring/" target="_blank" rel="noopener noreferrer">spring tournament</a> is happening now). Competitors in the speed CAD tournament adopt varied strategies to achieve the same design goals, highlighting the challenge of teaching AI to navigate the vast landscape of valid CAD solutions.&nbsp;As we mentioned in the first point: the inability of AI to "see" or visually understand the models it generates complicates the training process. Coupled with the fact that there are many valid approaches to a single design problem, this creates a complex reasoning challenge. Traditional foundation models may completely lack the necessary context to navigate these intricacies effectively.These challenges underscore the complexity of bridging the gap between textual descriptions and visual, three-dimensional CAD models. As we continue to explore and innovate in this space, addressing these issues will be crucial to generating high quality outputs.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA3ODQ1MDYzMjk0LWJyYWNrZXQucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 300px;" class="fr-fic fr-dib">What is the "right way" to make this? <h3>Our Approach</h3>Our vision for neThing.xyz is to create a generative AI tool that generates truly useful 3D CAD models from straightforward natural language prompts. With neThing we can dramatically expand the number of people who can participate in engineering. We believe that this tool will be useful for a wide range of people, from engineers to artists to students to hobbyists.&nbsp;Community-driven: Our hope is that this audacious goal can be realized through the support of our community. The AI will only ever be as smart as the sum of the community that trained it, and we are excited to see what you will create.&nbsp;Inspired by Midjourney, we are making the generated models "public by default", which we believe has a number of benefits:&nbsp;First, it will provide a rich showcase of examples for both humans and AI to learn from.Second, it means that even users who aren’t able to contribute financially are contributing to the overall community goal of making an amazing text-to-CAD generative AI tool.Finally, it encourages sharing and openness, which is absolutely essential for a product that strives to be community-supported. <h3>Introducing <a href="https://neThing.xyz" target="_blank" rel="noopener noreferrer">neThing.xyz</a></h3>neThing.xyz (pronounced "any thing dot x,y,z") is free to try. After you sign up, you will be able to...&nbsp; &nbsp; ...query the AI to create a 3D model from your prompt...&nbsp; &nbsp; ...preview your creations in AR (on compatible devices)...&nbsp; &nbsp; ...change the physical appearance of the 3D model...&nbsp; &nbsp; ...share your creations with friends...&nbsp; &nbsp; ...edit the code to change the model...&nbsp; &nbsp; ...download the model as STL, STEP, or GLTF...&nbsp; &nbsp; ...click the "Make it real" button to have polySpectra 3D print the part for you!Here are just two examples of the types of objects that you can create:&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA3ODQ0MjMxMTgzLWhleG51dC5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="A hex nut">"A hex nut"<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA3ODQ0MjgwNzE4LXBpcGUucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 300px;" class="fr-fic fr-dib">"A pipe with flange fittings" We are hungry for your feedback. There is a long road ahead to train the AI to create truly useful 3D CAD models from natural language prompts. The AI is only as smart as the good examples that we can train it on, and this will be a community effort. (And honestly, our AI is not very smart yet - we need your help!) Check out the tool today: <a href="https://neThing.xyz" rel="nofollow" target="_blank" id="isPasted">https://neThing.xyz</a>&nbsp;Join our community: <a href="https://forum.neThing.xyz" rel="nofollow" target="_blank">https://forum.neThing.xyz</a> Make it real.polySpectra

The vision for text-to-3D generative AI is not without its challenges.

Teaching AI CAD (is hard)

PCB Panels are separated with pre-designed “Mouse Bites” for seamless and realiable operations

Understanding the implementation, advantages drawbacks, and crucial design considerations for optimal circuit board separation and efficiency

PCB Mouse Bite: A Detailed Guide to Understanding and Implementing It

<h1 dir="ltr" id="isPasted">Introduction</h1>Virtual prototyping refers to the use of computer simulations to create and test digital models of physical systems before they are built. As semiconductor and OEM companies grapple with the integration of intricate hardware and software for the next wave of wireless, consumer, and automotive devices, the old guard of serialized development staggers under the weight of new demands. Virtual prototyping slices through these challenges, offering a versatile platform for early software development and meticulous system validation, reshaping timelines, and enhancing product quality.This article discusses virtual prototyping, contrasting it with traditional verification methodologies to reveal its role in accelerating time to market and bolstering communication across the supply chain. Readers will navigate through the transformative powers of virtual prototyping, from the inception of software development pre-silicon availability to the intricacies of system validation. The article uncovers the facets of virtual prototyping tools as we delve into how these innovations forge a path for seamless design verification. &nbsp;We use Synopsys' tools and services to illustrate how virtual prototyping is becoming an indispensable asset in digital design.<h1 dir="ltr">Understanding Virtual Prototyping</h1>Virtual prototyping is defined as the process of using computer simulations to model and validate complex system designs before manufacturing their physical components. This method is increasingly important due to the growing complexity and enhanced functionality of modern systems, which often exceed the capabilities of traditional prototyping methods. By employing virtual prototyping, designers can conduct detailed and comprehensive testing of systems, ensuring they meet stringent performance criteria. This approach not only enables early detection and resolution of potential design issues but also facilitates more efficient development cycles, aligning with the advanced requirements of current digital design practices.The intrinsic value of virtual prototyping lies in its ability to front-load the design process. It provides an interactive, software-based environment where designs can be tested, analyzed, and optimized. Engineers can simulate the behaviors of embedded systems under a wide array of operational conditions and use cases, enabling them to iterate more rapidly and with greater confidence. This simulation-centric approach empowers teams to validate assumptions, evaluate system performance, and conduct feasibility studies without the costs and time constraints associated with physical prototypes.<h2 dir="ltr">Accelerating Product Development with Virtual Prototyping</h2>A notable feature of virtual prototyping is its contribution to embedded software development. It allows software engineers to begin their design and testing work in parallel with, or even in advance of, hardware development. This overlap in development cycles is not feasible with conventional prototyping, which requires a sequential process where software development is contingent on the availability of the hardware.Additionally, virtual prototyping aids in optimizing the implementation of systems. Design teams can explore different architectural choices and hardware configurations to arrive at the most efficient and cost-effective solutions. This systematic exploration is essential for managing power consumption, processing speed, and overall system reliability – factors that are critical in today’s competitive technology landscape.Moreover, virtual prototypes can be integrated into automated testing environments, enabling continuous integration and delivery pipelines for software development. This integration leads to high-quality software being developed and maintained, with the capability to quickly adapt to changing requirements or identify potential issues early in the design cycle.In the broader sense, virtual prototyping transcends beyond the confines of mere pre-silicon validation. It stands as a strategic enabler for lifecycle management, facilitating updates, feature enhancements, and maintenance long after the product has been deployed. As systems become increasingly interconnected and expected to support new functionalities over time, virtual prototyping offers the flexibility and scalability needed to sustain product evolution.Thus, the role of virtual prototyping is multifaceted: it accelerates the design process, enhances product quality, reduces development costs, and extends the lifecycle of digital products. It encapsulates the shift towards a more agile, forward-thinking approach in digital design, one that is instrumental for companies looking to thrive amidst the burgeoning complexities of modern technological innovation.<h1 dir="ltr">Virtual Prototyping Versus Traditional Methods</h1>In digital design and verification, the cost of error escalates as one moves through the development process. Traditional verification methodologies are increasingly seen as inadequate, especially as the complexity of integrated systems grows. This section will conduct a comparative analysis of virtual prototyping against these traditional methodologies, illustrating the efficiencies gained through modern techniques.<h2 dir="ltr">The Stumbling Blocks of Traditional Verification</h2>In traditional verification, the development of software often lags behind hardware completion. This approach is inherently sequential and leaves little room for iterative improvement, which can be particularly detrimental to aggressive product development schedules. Software teams await the physical prototypes to test their code, creating a bottleneck that impedes early detection and resolution of issues.Furthermore, traditional methods often rely on physical testing environments, which can be both costly and limited in scope. The limitations of hardware-based tests—such as the number of available prototypes, the difficulty in re-creating certain operating conditions, and the inability to test every possible scenario—can leave critical bugs undetected until late in the development process.<h2 dir="ltr">The Strategic Edge of Virtual Prototyping</h2>Virtual prototyping offers a stark contrast, delivering a strategic edge by removing the reliance on hardware availability. Software development can commence much earlier, even before the RTL (Register-Transfer Level) stages of hardware design. This shift not only circumvents the wait for physical prototypes but also opens up parallel workflows that can dramatically accelerate the development cycle.<h2 dir="ltr">Efficiency and Scope of Testing</h2>Virtual prototypes enable an extensive scope of testing that physical prototypes cannot match. By simulating various system states and operational conditions, virtual prototypes allow for an exhaustive verification process, including the kind of edge cases that are difficult to replicate with hardware. The flexibility of virtual environments means that software testing and system validation can occur at multiple workstations concurrently, greatly expanding test coverage.<h2 dir="ltr">Cost-Effectiveness and Error Detection</h2>From a financial perspective, virtual prototyping is compelling. By detecting errors early in the design process, it mitigates the high costs associated with late-stage troubleshooting in traditional verification. Early detection not only reduces the expenditure on rectifications but also minimizes the risk of expensive recalls and brand damage post-product release.<h2 dir="ltr">Debugging and Analysis</h2>The debug capabilities of virtual prototyping are a leap forward in verification methodology. The ability to pause, rewind, and replay scenarios in a virtual prototype provides an unprecedented level of control and insight into system behavior. This allows for a more nuanced analysis and a thorough understanding of the root causes of issues, which can be elusive in traditional post-silicon debugging.<h2 dir="ltr">The Quantifiable Benefits</h2>The advantages of virtual prototyping can be quantified in reduced development time, increased product quality, and lowered costs. With the accelerated time-to-market that virtual prototyping allows, companies can stay ahead of the competition and better meet market demands. Additionally, the increased productivity and efficiency across teams contribute to a more streamlined development process, from conceptual design to final product validation.<h1 dir="ltr">Virtual Prototyping Solutions by Synopsys</h1>Synopsys offers a comprehensive virtual prototyping solution that includes several key products and services:<ul><li dir="ltr">Synopsys Virtualizer: The <a href="https://www.synopsys.com/verification/virtual-prototyping/virtualizer.html" target="_blank">Synopsys Virtualizer tool</a> enables the creation of virtual prototypes that can be used for early software development and testing. This tool is a part of a suite that includes Synopsys Platform Architect™ and a vast array of transaction-level models (TLMs). <a href="https://www.synopsys.com/verification/virtual-prototyping/vdk.html" target="_blank">Virtualizer Development Kits (VDKs)</a> are also provided for software development, allowing for early software bring-up before silicon availability.</li><li>Hybrid Prototyping: For hardware and software engineers, Synopsys offers a <a href="https://www.synopsys.com/verification/virtual-prototyping/virtualizer/hybrid-prototyping.html" target="_blank">hybrid prototyping solution</a> that combines virtual and FPGA-based prototypes. This allows for the benefits of both approaches, enabling the start of multi-core SoC prototyping earlier and facilitating high-performance execution of system-level models, which include direct connections to real-world hardware interfaces.</li></ul><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzAwMjAxNDU1OTAyLWltYWdlMi5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 300px;" class="fr-fic fr-dib" alt="hybrid-prototyping-framework-synopsis">A block diagram illustrating the hybrid prototyping framework and workflow. Source:&nbsp;<a href="https://www.youtube.com/watch?v=_ux9xPkGfF0&ab_channel=Synopsys" target="_blank" rel="noopener noreferrer">Faster Software Development using Hybrid Prototyping - Synopsys YouTube</a><ul><li dir="ltr">Synopsys Platform Architect for Multi-Core Optimization (MCO): Synopsys provides the&nbsp;<a href="https://www.synopsys.com/verification/virtual-prototyping/platform-architect.html" target="_blank">Platform Architect tool</a> for creating virtual prototypes geared towards architecture design. This tool is designed to cater to the specific requirements for simulation speed and timing accuracy, and it is used alongside Virtualizer for a comprehensive virtual prototyping environment tailored to software development use cases.</li><li dir="ltr">Virtual Prototyping Services: Synopsys provides full-service solutions where they take charge of modeling, assembling, and delivering a virtual prototype tailored to the specific needs of end-user teams. This service may include integrating software tools, environment models, test environments, and customizing virtual prototype tools for debugging and analysis to meet particular requirements.</li></ul>Each of these offerings is designed to support different aspects of the development process, from the early stages of software development to the optimization of multi-core system architectures.<h1 dir="ltr">Conclusion</h1>Virtual prototyping marks a turning point in digital design, fundamentally transforming the way we approach the development of complex systems. By allowing for early software development and thorough system validation, it significantly shortens time-to-market and enhances product quality. The ability to test and iterate designs within a virtual environment translates to a leap in efficiency and cost-effectiveness, driving innovation at a faster clip than ever before.As the complexities of design increase, virtual prototyping offers the agility and foresight needed to keep pace with rapid technological advancements. It is more than a tool; it is a strategic imperative for any organization aiming to lead in the fiercely competitive landscape of digital technology.<h1 dir="ltr">References</h1>[1] What is Virtual Prototyping? – How it Works &amp; Benefits. Synopsys; [Online]. Available from:&nbsp;<a href="https://www.synopsys.com/glossary/what-is-virtual-prototyping.html" target="_blank">https://www.synopsys.com/glossary/what-is-virtual-prototyping.html</a>[2] Chang KH. Introduction to e-Design. In: Chang KH, editor. Design Theory and Methods Using CAD/CAE. Academic Press; 2015. p. 1-37. [Online]. Available from:&nbsp;<a href="https://www.sciencedirect.com/science/article/abs/pii/B9780123985125000013" target="_blank">https://www.sciencedirect.com/science/article/abs/pii/B9780123985125000013</a>[3] Bidanda B, Bártolo PJ, editors. Virtual Prototyping &amp; Bio Manufacturing in Medical Applications. Springer International Publishing; 2020.

Virtual prototyping in digital design streamlines the development of complex systems by allowing early software testing and hardware optimization before the creation of a physical prototype.

The Era of Virtual Prototyping in Digital Design

The rise of generative AI, as demonstrated by text-based applications like Chat GPT and Bard, as well as image generators like Midjourney and Stable Diffusion, has sparked a wave of anticipation in the engineering realm. The hope is for similar tools to evolve that can assist in 3D design space, helping to tackle complex engineering problems.Emerging research on converting text to 2D to 3D point clouds or SDFs to 3D meshes is fueling enthusiasm for shape generation possibilities. But, the journey from creating something that merely &#39;looks&#39; right to devising something that &#39;performs&#39; to meet functional requirements is paved with many challenges. They demand distinct training data, a very approach to specifying requirements to produce forms and a different way of thinking about design.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0MzMwOTUxOTAwLUFEU0sgM0QgQUkgQ0RGQU0ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 707px;" class="fr-fic fr-dib"><a href="https://arxiv.org/abs/2110.02624" id="isPasted" target="_blank">Sanghi, Aditya, et al., Clip-forge: Towards zero-shot text-to-shape generation, CVPR 2022.</a>The engineering industry is beginning to witness the first AI-integrated commercial software tools suitable for the design and engineering. These include offerings from established software firms like Altair, with a rich history in &#39;simulation-driven design&#39;, and startups like Monolith and PhysicsX working to help engineers to predict high-performance problems while also reducing reliance on simulation in the design iteration process (not the validation process).Altair&#39;s designAI combines physics-based simulation-driven design and machine learning-based AI-driven design to create high-potential designs earlier in development cycles. While physicsAI&nbsp;is used to predict physics outcomes quickly leveraging historical simulation data to deliver physics predictions in a fraction of the time it takes traditional solvers to do the same. PhysicsAI uses geometric deep learning to operate directly on meshes and CAD models while rapidMiner, Altair&rsquo;s data analytics and AI platform enables users to develop data pipelines and machine learning models without needing in-house data science expertise.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0MzI3MjI2MTIxLUFsdGFpci1QaHlzaWNzQUktQ0RGQU0ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 603px;" class="fr-fic fr-dib"><hr><blockquote>Capturing and understanding design intent is crucial for developing machine learning models that can predict performance and optimize designs. &nbsp;</blockquote><a href="https://cdfam.com/challenges-of-ai-in-computational-engineering/" rel="noopener noreferrer" target="_blank">Jaideep Bangal - Director of Simulation Driven Design and Manufacturing, Altair</a><hr>While AI&#39;s application in manufacturing for process development, optimization, and quality assurance is being quickly established, the design space still grapples with a lack of rich metadata for training ML algorithms. The use of simulation to synthesize training data, despite its computational expense, provides the prospect of exploring solutions beyond the realm of known possibilities.Evaluating generative models poses significant challenges, often necessitating a comparison of the distribution of generated designs with training data. This merely confirms if the produced design aligns with the training data.&nbsp;Including simulation in the training data and workflow offers the potential for a more objective form of synthesis and evaluation. It may help uncover designs that, despite differing from the training data, perform well. &nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1684327021114-learning+to+generate+shaped+CDFAM+Autodesk.gif" style="width: 300px;" class="fr-fic fr-dib"><hr><blockquote>Metrics for evaluating generative models are very tricky and often come down to comparing the distribution of generated designs with the training data. This only tells us if the generated design is like the training data. But simulation allows us to evaluate more objectively, so we might be able to find designs that are unlike the training data but perform well, i.e. the network produces something that&rsquo;s both novel and useful.</blockquote><a href="https://www.linkedin.com/in/karldd/" rel="noreferrer noopener" target="_blank" id="isPasted"></a><a href="https://cdfam.com/learning-to-generate-shapes-ai-autodesk/" target="_blank" rel="noopener noreferrer"></a><a href="https://cdfam.com/learning-to-generate-shapes-ai-autodesk/" rel="noopener noreferrer" target="_blank">Karl D.D. Willis Senior Research Manager, Autodesk Research AI Lab.</a><hr>Simulation, despite its promising potential in exploring novel solutions outside of the training data, can carry a hefty computational price tag.&nbsp;Before the investment in establishing the dataset and training begins to pay dividends, a large number of optimization problems may need to be solved to provide solutions. Academic literature shows that, in the most favorable scenarios, this break-even number is in the thousands. In less favorable instances, it can reach hundreds of thousands, even for problems with a mere few thousand design variables.These approaches often lack generality, meaning their effectiveness is restricted to limited applications, number of boundary conditions and other parameters. This limitation further complicates their implementation in real-world scenarios, as engineers must contend with a diverse array of conditions when designing and optimizing products.Striking the right balance between leveraging simulation for exploring novel solutions and managing computational costs and constraints is crucial in the pursuit of AI-driven engineering advances. The development of more efficient and adaptable simulation techniques, alongside the intelligent integration of AI, will be key in overcoming these challenges and maximizing the potential of AI-assisted engineering solutions.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0MzMxMDU5MTcyLUNERkFNIHRvcCBvcC5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 645px;" class="fr-fic fr-dib"><hr><blockquote>High resolution is required either to resolve local field properties like stress concentrations or to allow optimization of huge structures like airplane wings or bridge decks.&nbsp;One finite element evaluation may take hours on super computers making it infeasible to generate data enough for training an ANN.&nbsp;</blockquote><a href="https://cdfam.com/is-ai-driven-design-a-computational-dead-end-in-engineering/" rel="noopener noreferrer" target="_blank">Prof. Ole Sigmund TopOpt Group - Technical University of Denmark</a><hr> In the manufacturing domain, proprietary design, simulation, and manufacturing data is often considered the &quot;secret sauce&quot; of each company. This proprietary information is closely guarded, sometimes to the extent that it&#39;s not even shared internally.Amassing a warehouse of meaningful data, both within and beyond these corporate &quot;walled gardens,&quot; poses a formidable challenge. This protective stance towards data not only limits the potential for broad collaborative efforts, but also hampers the comprehensive training of machine learning algorithms, potentially stymieing the evolution of AI in engineering.&nbsp;It underscores the need for new strategies and <a href="https://3mf.io/" rel="noopener noreferrer" target="_blank">protocols that encourage data sharing</a> while preserving intellectual property and competitive advantage.Most companies looking to integrate AI into their engineering workflow are at this very earliest, data collection and distillation phase.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0MzI3MTExNDk5LUFJLUhpZXJhcmNoeS1vZi1uZWVkcy1DREZBTS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 525px;" class="fr-fic fr-dib">&nbsp;<a href="https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007" id="isPasted" target="_blank">&ldquo;AI Hierarchy of Needs&rdquo;</a> by Monica Rogati.<hr><blockquote>We all want to get to the top of the pyramid &ndash; deploying powerful AI models that revolutionize our businesses. However, many of us are still at the base of that pyramid, figuring out how to effectively log and clean data.</blockquote><a href="https://cdfam.com/design-for-artificial-intelligence-dfai/" rel="noopener noreferrer" target="_blank">Chris McComb &ndash; Director, Human+AI Initiative -Carnegie Mellon University</a><hr>While many researchers are focusing on the task of integrating AI into engineering, addressing a multitude of data-related challenges. Chris McComb and his team at Carnegie Mellon University are taking a broader approach by examining not only the mechanical processes and individual approaches towards Design for Artificial Intelligence (DfAI), but also the dynamics of team-based collaboration.&nbsp;Their research delves into understanding the subtleties of how engineers interact with AI-enabled engineering software and, just as crucially, their interpersonal communication within this context.&nbsp;By exploring both the technical and human aspects, their work aims to pave the way for more efficient and effective AI integration in the engineering process, fostering innovation and breakthroughs in the field.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0MzMxMjc0MDU0LWRmYWktQ0RGQU0gaW1hZ2UucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 720px;" class="fr-fic fr-dib"><hr><blockquote>One thing that I encourage people to keep in mind when working with AI is that we are not Teflon &ndash; just as we influence AI tools, the tools also influence us. Exactly how AI influences us depends on how we work with it, though.</blockquote><a id="isPasted" target="_blank">Chris McComb &ndash; Director, Human+AI Initiative -Carnegie Mellon University</a><hr>The utilization of AI as a tool prompts a shift in our focus, moving away from basic modeling operations and towards activities of higher value such as data distillation and problem framing.&nbsp;By integrating AI as an equal collaborator within our teams, we foster a hybrid human/machine work environment that has the potential to outperform purely human teams.However, while it might be tempting to believe that AI is an unwavering ally, it is important to note that it is not always beneficial. Much like any tool, AI&#39;s effectiveness is contingent on its appropriate application and use within the given context.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0MzMyMDIzOTI4LUFJLXRlYW0tcGVyZm9ybWFuY2UtQ0RGQU0ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 579px;" class="fr-fic fr-dib">Chris and his team developed an AI Designer that surpassed its human counterparts in engineering performance.&nbsp;However, <a href="https://www.sciencedirect.com/science/article/pii/S0142694X21000016" rel="noopener noreferrer" target="_blank">mixed teams consisting of both human engineers and an AI designer didn&#39;t always fare as well</a> as the individual AI or human entities.&nbsp;This underscores the potential of AI in enhancing design, while simultaneously revealing potential risks if blindly trusting results from a black box system.&nbsp;As we embrace this new era, it&#39;s crucial to consider both the opportunities and challenges that integrating AI into our engineering practices presents.<hr>Chris McComb, Karl D.D. Willis and Jaideep Bangal will be presenting alongside other experts in the field of computational design, engineering and AI at <a href="https://cdfam.com/" rel="noopener noreferrer" target="_blank">CDFAM Computational Design (+DfAM) Symposium</a> in NYC June 14-15.

And the Challenges of Meaningful Training Data

Entering the Age of AI in Engineering

<h2 id="isPasted">Quick Wins</h2>We&#39;ll be looking at three items here that should be broadly universal across any Mechanical Engineering function, and should also be applicable to other disciplines. Our objective is to highlight some minor changes in workflow that can offer a significant benefit for your team. Taking these in order...<hr><h2 id="isPasted">1. Drawings: Datum Tables</h2>This is something I started doing several years ago to combat my own frustration with trying to read other people&#39;s drawings, and I was always surprised that it wasn&#39;t a standard practice in any of the design departments I&#39;ve worked in.Picture the scene: one of your colleagues has just finished off the drawings for a new cylinder head, and they pass them to you for peer review. If your brain works anything like mine, the first things you want to check are all the major datum features. However, when a drawing pack runs to six or seven substantial pages, this turns into a less entertaining, more time-consuming, monochrome version of Where&#39;s Wally? &mdash; which isn&#39;t really what we&#39;re after.For me, the fruit doesn&#39;t hang much lower. Work up a table&dagger; that lists datum features in alphabetical order, list their sheet, list their grid reference, then place the table somewhere easy to find on the drawing. Anyone else lifting the drawings will be able to dive right into reading the important bits, without having to scour the thing for ten minutes before they start actually thinking about the task at hand. This can as simple as Table 1.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0MjI1NDYxMjE1LVRhYmxlSW1hZ2UucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib">Table 1 &dagger;If you&#39;re so inclined, I also imagine that it should be possible to implement automatic table assembly with a little bit of code. From memory, when I last explored this with CATIA v5 macros, I couldn&#39;t find any reference to datum objects in the object model documentation.<hr><h2 id="isPasted">2. Excel: Named Ranges</h2>As the sort of engineer whose professional home lies in writing code to analyse, simulate, and control physical systems, this point is much closer to my heart. With a few notable use-case exceptions, such as its easily accessed optimisation tools, I basically hate Excel. I consider it to be like the adjustable spanner of the software world: it might work for the task at hand, but it&#39;s still the wrong tool for just about every job.I believe that Excel inadvertently pushes people to construct terribly organised models and analyses that are often borderline unintelligible, and that building a clean solution in Excel requires the adoption of some very considered practice that you would basically get by accident if you did this work in any one of MATLAB, Python, Julia, Fortran etc. &mdash; the list goes on.More specifically, when I&#39;m forced to unpick somebody else&#39;s Excel-based model or analysis, there is one overwhelmingly common piece of practice that makes me want to tear my hair out: an overflowing formula bar, brimming with unnecessary parentheses and mysterious references to <code>!Sheet2&nbsp;</code>and <code>$C$907</code>. Let&#39;s take a real-world example of how unnecessarily messy this can get, even for relatively simple cases, then we&#39;ll move onto cleaning things up.<h3>The Problem</h3>The simple example case I&#39;m going to give here will be familiar to anyone who&#39;s ever designed an engine, a gas strut, or a (hydro)pneumatic suspension system: computing gas pressure in a cylinder of known volume. We&#39;re going to use the <a href="https://en.wikipedia.org/wiki/Van_der_Waals_equation" target="_blank">Van der Waals equation of state,&nbsp;</a>owing to the fact that the ideal gas law is pretty rubbish at approximating the behaviour we&#39;d typically see in any of these systems. The Van der Waal&#39;s equation of state is given by:<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0MTY3MzEzOTIyLUVxMS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib">For simplicity, we&#39;ll take the case for n moles of gas, so this becomes:&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0MTY3MzQ0NzcxLUVxMi5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 766px;" class="fr-fic fr-dib">Where:<ul><li>P denotes pressure.</li><li>V denotes volume.</li><li>n denotes moles of gas.</li><li>R&nbsp;denotes gas constant.</li><li>T denotes gas temperature.</li><li>a denotes Van der Waals constant for intermolecular interaction.</li><li>b denotes Van der Waals constant for real gas molecule volume.</li></ul>In our example scenario, we can pretend that we&#39;re solving for pressure, with all other values known, so we can quickly rearrange as follows:&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0MTY3Mzk1NTExLUVxMzQucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib">Equation shuffling out of the way, let&#39;s pluck some values from the air and get these plugged into a spreadsheet to see what we&#39;re dealing with. The values we&#39;re going with assume a 10cc volume, 20&deg;C, and enough pure nitrogen fill to generate approximately 86barA.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0MjIxMjQ5NDg2LVNjcmVlbnNob3QgMjAyMy0wNS0xNiAxMDA5MTYucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib">The typical quick spreadsheet setup leaves us with something that looks like this:&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0MTY3NTAxNDA0LTIyMDUxOS0zUXVpY2tXaW5zLU5vTmFtZWRSYW5nZS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 730px;" class="fr-fic fr-dib">In my view, this isn&#39;t the end of the world; all the referenced cells are close by, and I can pick apart the formula quickly &mdash; but you can already see that extension of this sort of pattern to something more complex will start to cause serious headaches. By the time you&#39;re working with something which has parameters spanning sheets and substantially more complex formulas in play, this method is a nightmare.&nbsp;<h3 id="isPasted">The Solution</h3>Thankfully, a better way forward here is entirely quick and painless: <a href="https://support.microsoft.com/en-us/office/define-and-use-names-in-formulas-4d0f13ac-53b7-422e-afd2-abd7ff379c64" target="_blank">named ranges.&nbsp;</a>Named ranges effectively allow us to assign variable names of our choosing to these parameters, while also allowing us to configure the scope of the variables, i.e., whether they are visible only on a given worksheet, or whether they&#39;re global across the workbook.Setting a named range on a single value is as easy as clicking the cell in the top-left corner that currently shows cell address, overwriting it with an appropriate value, and hitting enter. There are plenty of other websites that explain how to use the &#39;Name Manager&#39; tools and get the most out of named range usage, but I&#39;ll just show you how much cleaner things look once you&#39;ve converted cell addresses to named ranges:<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg0MTY3NTI3MDI2LTIyMDUxOS0zUXVpY2tXaW5zLU5hbWVkUmFuZ2UucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib">At least to my mind, this is an immediate and significant improvement. Compared with how it was previously, it is now substantially more straightforward to compare the formula with our target equation above. Any misplaced brackets or indices that might otherwise cause some serious pain to review or debug can now be caught much more readily, and you can move forward with increased confidence that your spready is trustworthy.<hr><h2 id="isPasted">3. Files: Naming Convention</h2>Finally then, the best (and shortest) tip: file naming convention. As far as I&#39;m concerned, there is precisely one correct way to name the sort of files generated by most engineering operations, and that way is as follows:<ul><li><code>YYMMDD-FileName.ext</code></li></ul>For example, the file in our named range scenario might be called something like <code>220519-NamedRangeExample.xlsx</code>.By placing the date at the start of the filename, and by following the <a href="https://en.wikipedia.org/wiki/ISO_8601" target="_blank">ISO8601</a> date component order, lexicographical sorts will yield files in chronological order, irrelevant of when they were copied to the particular directory.Then, by adopting dashes instead of spaces, underscores, or whatever else you might fancy for separation of sections of the file name, you&#39;re making the decision up-front that you simply want to avoid any funnies with respect to how filenames are handled.For example, any files appearing at a web URL will have spaces substituted for <code>%20</code>, which isn&#39;t exactly readable, and underscores can cause issues with both certain platforms and certain indexing systems.&nbsp;

Three straightforward, easy to implement tweaks that can help make life easier for engineers. 

3 Quick Methods Wins for Mechanical Engineering Teams

CAD software is used to create 3D printable models

Most beginners start their 3D printing journey by downloading 3D models from the internet. But knowing how to make a 3D model for printing using CAD software opens up a world of possibilities.

How to Make a 3D Model for Printing

Triangles, Cross, and Grid infill patterns

3D printer slicing software Cura lets users choose from a range of infill patterns. But what are the differences between patterns like Grid, Cubic, and Zig-zag, and how do you choose between them?

Cura Infill Patterns: What They Are and When to Use Them

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