Importance Of Prototyping In Engineering

A $12 prototype can make $50,000 of engineering analysis look ridiculous A team of engineers was stuck on a bearing failure analysis for six weeks. Vibration data, FFT analysis, metallurgy reports - they had everything except answers. The client kept asking for root cause and the engineers kept finding more variables to analyze. Temperature gradients, load distributions, contamination levels, manufacturing tolerances. Each analysis created more questions. Then the intern did something that made the engineers feel stupid. She 3D printed a transparent housing and filled it with clear oil so the engineers could actually see what was happening inside the bearing assembly. Took her four hours and $12 in materials. They watched the oil flow patterns and immediately saw the lubrication wasn't reaching the critical contact points. All their sophisticated analysis was based on assuming proper lubrication distribution. Wrong assumption. Six weeks of wasted effort. The visual prototype didn't just solve the problem - it changed how the engineers approach these types of investigations. Now they build crude mockups before diving into analysis rabbit holes. Cardboard, tape, clear plastic, whatever works. Physical models force you to confront your assumptions before you spend weeks analyzing the wrong thing. Sometimes the cheapest prototype teaches you more than the most expensive simulation. #engineering #prototyping #problemsolving

In my final year of college, we decided to take on a project that is ambitious, and we built an AI-powered posture correction chair. The idea came from a very common problem. Most people sit at desks for hours, often without realising how poor their posture gets over time. We thought, what if your chair could tell you when you're sitting wrong? So we started building. We embedded sensors into the seat and the backrest. These sensors would track how a person was sitting, and if the posture wasn’t right for a certain duration, the system would send out a notification for the user to correct their position in real time. On paper, the concept looked solid. But the moment we started testing it across different users, we realised how much we had missed. The exact same posture would trigger an alert in one person and nothing at all in another. The system was wildly inconsistent and it took us a while to understand what was actually going wrong. Our model was treating everyone the same, without accounting for natural variations in body weight, shape, and sitting patterns. That chair never became a product. But that failure taught me how important proper research and testing is especially when you're building tech that's supposed to interact with human behavior. Because it’s one thing to make something that works under perfect conditions. It’s another to make it work in the real world.

PROTOTYPES ACCELERATE DISCOVERY, NOT DELIVERY Prototypes are powerful tools for the discovery phase — helping teams quickly explore product directions, validate concepts with customers through high-fidelity experiences, and align executives around tangible visions. The leverage they provide in answering "what's the right experience to build?" is remarkable. However, I frequently see PMs expecting to hand prototypes directly to engineering teams for production implementation. This approach consistently leads to disappointment. Here's why: prototype code isn't built to meet the security, reliability, robustness, and maintainability standards that production systems require. Your engineering team rightfully prioritizes these critical attributes. And that's perfectly fine. The value of prototypes lies entirely in discovery. Even when engineering teams ultimately rebuild from scratch, prototypes have already delivered tremendous ROI by: - Accelerating team alignment on product direction - Validating customer demand with realistic experiences - Securing executive buy-in through tangible demonstrations The code was never meant to ship — the insights were.

I had the chance a few weeks ago to sit down (virtually of course) with Woodrow Bellamy III, host of SAE’s Aerospace & Defense Technology podcast, for a candid conversation about virtual prototyping. While I work across many industries these days, I always enjoy a chance to share insights from my years working in Aerospace. The key question was about the reliability of virtual prototyping as a replacement for physical prototyping. How much faith can be put into a digital model for accurately simulating a real-word system, when you have real-world stakes and consequences? The answer is: a lot. When we first started using simulation for some of the aircraft programs I worked, we questioned if we could trust the results to predict peformance of a new aircraft. It was an appropriate question to ask. It was only after conducting extensive testing to validate the results of the simulation against an existing aircraft that we started to trust the simulation models while designing a new aircraft. The same litmus test will be needed for companies in any industry to take the leap with virtual prototyping: A company can start by developing the virtual model and ensure the validity and robustness of the simulation by comparing against existing physical products. Once the team is sufficiently confident about the performance of the digital model, they can take the insights from existing products and systems and apply them toward the development of future projects. The digital twin of a physical system is infinitely more malleable. It can be designed, tested, and experimented upon with far more ease and using significantly fewer resources. Virtual prototypes allow for limitless design exploration, help to identify design issues early and before building physical prototypes, and make physical testing more effective. Before going for the real-life testing, virtual analyses highlight critical areas in the design, and the test plans can be adjusted to focus on areas of greatest concern. The aerospace industry adopted the use of the digital twin as a revolutionary design tool decades ago. And the digital twin has evolved quite a bit over the ensuing decades. It is no longer just a 3D model of a product or process. Today, the comprehensive digital twin is a precise virtual representation of the product that replicates its physical form, function and behavior and encompasses all cross-domain models and data from mechanical and electrical through software code. So, although the current ratio of prototyping testing, worldwide, may be 90% physical and 10% digital, it isn’t an overstatement to conceive of a future where the ratio is flipped; maybe even 100% digital. I'm excited about the opportunities offered by virtual prototyping and testing as a means to enable companies to develop and validate innovative products faster! To check out my conversation with Woodrow, please check out the link in the comments. #digitaltransformation #siemensxcelerator

The old PM loop takes three weeks. Write the PRD. Wait for design queue. Wait for engineering queue. By the time something real exists, the conversation has moved on. The prototyping loop takes one afternoon. Abhi Muchhal runs international growth for ChatGPT. His team tracks dozens of countries across seven Databricks and Tableau dashboards. PMs used to spend mornings loading each one individually, trying to piece together what mattered. He built a single web app that pulls from all seven sources. It categorizes strengths and risks by country against a peer set Codex identified on its own. Refreshes every morning at 9am. No designer. No engineer. No sprint queue. Codex ran its own Playwright smoke tests before Abhi even opened the preview. Then someone asked him for a PRD. He started writing, stopped 20 minutes in, and built a working prototype instead. Attached a 10-question companion FAQ covering hypotheses, success metrics, guardrails, and safety review. The FAQ does what a PRD used to do. The prototype leads now. Here’s what actually flipped: when a PM walks into a review with a running prototype, engineers stop asking “what do you want?” and start asking “how do we make this better?” That conversation shift only happens when something runs. Three weeks of PRD → design queue → engineering queue compressed into one afternoon. Abhi works alongside world-class engineers at OpenAI. His code isn’t production-grade and he knows it. The goal was to skip the three queues sitting between a PM’s idea and an engineer’s reaction. His team also built a Codex skill for experiment reviews. Point it at a StatSig experiment, it writes the hypothesis, monitors the data, and generates recommendations when the engineer is ready to present. The person who cares most about the outcome authors the skill. So here’s how to make this shift: 1. Podcast with Abhi (full setup): https://lnkd.in/g-G_wn4B 2. Ship your first PR as a PM: https://lnkd.in/gAPBEKkr 3. AI prototyping tutorial: https://lnkd.in/eJujDhBV 4. The new AI PRD: https://lnkd.in/eMu59p_z 5. Become a Builder PM: https://lnkd.in/ggXYkwxB The PRD isn’t dead. But the PRD as starting point is. Prototype first. Document second.

Prototypes aren't for testing your product. They're for testing your assumptions. Most teams get this backward, and it costs them weeks of wasted effort and a product nobody wants. A prototype isn't a tiny product; it's a medium for learning. It's a tool designed to ask a specific question and test a core assumption with the right audience. An unintentionally designed prototype is a flawed input, and even with advanced teams and tools, flawed inputs only amplify flaws. The true power of a prototype isn't in its polish, but in the intentional "message" it sends. To unlock this power and truly accelerate collective learning across your organization, you must design with intent: ✺ Low-Fidelity Prototypes: These are for asking foundational, "Does this even solve the right problem?" questions. They signal that everything is up for debate. The intentional message is: "Let's explore the idea, not the pixels." ✺ Medium-Fidelity Prototypes: Use these to test core user flows and information architecture. The intentional message is: "Is this journey intuitive?" By keeping them a little rough, you prevent stakeholders from getting fixated on visual design. ✺ High-Fidelity Prototypes: Reserve these for the final stages to test things like micro-interactions, brand consistency, or subtle emotional responses. The intentional message is: "We're almost there. What are we missing?" This is how you turn prototyping from a simple task into a strategic lever for change and Team Learning. It ensures your team isn't just building things, but is learning together and making better decisions about what to build and why. It's how you break down silos and create a "Holding Environment" for generative dialogue. What's a time you intentionally used a low-fidelity prototype to prevent a high-stakes meeting from spiraling? Let’s discuss in the comments below. #ProductDesign #SystemsThinking #StrategicDesign #UXStrategy #DesignLeadership #ComplexSystems #TeamLearning #Prototyping #OrganizationalDesign #Innovation

Boeing engineers once filled an entire airplane with sacks of potatoes just to test the in-flight Wi-Fi. It happened around 2012 when the company needed a reliable way to fine-tune their wireless signal strength for passengers. They needed to simulate a plane full of people, but having human testers sit motionless for days on end was not a practical solution. They discovered that potatoes, due to their specific water content and chemistry, absorb and reflect radio wave signals in a way that is remarkably similar to the human body. So, they loaded a plane with thousands of pounds of potatoes, placing a large sack in every single seat to mimic a full flight. This allowed them to systematically map the signal strength throughout the cabin, identifying weak spots and dead zones that needed to be fixed. The method, while unusual, was a clever and effective piece of engineering that helped ensure a better connection for travelers. This potato-based testing provided the data needed to optimize the placement of Wi-Fi routers and signal boosters on their aircraft. Sources: Journal of Food Science, Phys org

Kinetic – Human Motion This project is a finger prosthesis designed for a friend who lost his last phalanx. Not as a product. Not as a commercial device. But as a proof of what engineering skills can do when applied with intention. I’ve worked in the medical field before, and I’ve seen how precision, simulation, and manufacturing constraints directly affect people’s lives. Here, I used: • Motion Design for kinematic validation • Topology optimization to reduce material • Additive manufacturing for accessible prototyping The first prototype was intentionally printed at 2x scale to simplify mechanical validation before adapting it to real morphology. 3D printing is not just a prototyping tool. It can be a pathway to more accessible, lower-cost prosthetic solutions. Engineering is not neutral. It becomes meaningful when directed toward real human needs. A quick note as well, thanks to 3Dconnexion for supporting my workflow. Tools like the #SpaceMouse make navigating complex design environments much more fluid, allowing ideas to move faster from concept to simulation and prototype. Still a work in progress. But a reminder that simulation can restore more than motion. Dassault Systèmes - Fabio Ballari - Luca Senesi - Ioana Ocnarescu - Pravin P #3DPrinting #Kinematics #MechanicalEngineering #Design

What if the best solutions for your process started with cardboard? When testing new ideas or improvements, jumping straight to high-cost, permanent solutions can be risky—and expensive. That’s where cardboard engineering comes in. Cardboard is one of the simplest, most cost-effective tools for rapid prototyping and testing ideas. It’s lightweight, easy to shape, and lets you visualize, test, and refine your concepts before committing to more expensive materials. Why Cardboard Is Perfect for Prototyping: 1️⃣ Low-Cost Experimentation Testing with cardboard lets you try multiple iterations of a design without worrying about material costs. 2️⃣ Fast Feedback Loops You can build and modify a prototype in minutes, gathering instant feedback from your team or operators. 3️⃣ Hands-On Collaboration Cardboard prototypes allow teams to actively engage with ideas, making it easier to identify issues or opportunities for improvement. 4️⃣ Visual Validation Sometimes, seeing a physical model highlights challenges that wouldn’t be obvious in a drawing or plan. How to Use Cardboard for Lean Improvements: 🔍 Test Workstation Layouts Use cardboard cutouts to mock up layouts and placement of tools, parts, and equipment. Adjust until everything flows smoothly. 📦 Simulate Material Flow Prototype racks, bins, or carts to ensure materials are stored and moved efficiently before building them with more durable materials. 🛠️ Design Fixtures or Jigs Create cardboard versions of fixtures or jigs to test their functionality in the process. Refine the design before investing in the final version. 📐 Test Ergonomics Mock up equipment or workstation designs with cardboard to test ease of use, reach, and operator comfort. Example of Cardboard in Action: A manufacturing team wanted to redesign a workstation to reduce operator motion. Instead of committing to expensive reconfigurations, they used cardboard to prototype the layout. After several iterations, they found the optimal setup, reducing motion by 25% and saving hours of work. Cardboard isn’t just for packaging—it’s a powerful tool for testing and refining your ideas. By prototyping with low-cost materials, you can experiment, learn, and improve quickly without breaking the bank.

Prototyping a system in small numbers on a tight timeline can lead to interesting decisions. For instance, it was quicker and more cost-effective for us to source a copper-core PCB than a sheet-metal part.   In the Framework Laptop SDR, we use two heat pipes to cool the AFEs. The heat pipes are soldered to a 1mm copper sheet that sits on top of the AFEs. Instead of using a copper sheet metal part with a surface finish suitable for low-temperature soldering, we found that, in low quantities, it was much faster to source a copper-core PCB. The surface finish and planarity of the copper-core PCB are also subject to much tighter process control. With a lead time of only two days, and the added benefit of being able to place sensors directly on the heat spreader, it proved to be a much better option than a sheet metal part. The heat pipes are in direct contact with the copper core at critical points, so the thermal performance is equivalent to that of a plain copper sheet.   For higher quantities, the copper sheet metal part is of course a lot cheaper, due to fewer processing steps. However, we might even stick with the copper core PCB solution due to the added benefit of being able to place sensors on the heat spreaders without the need for wiring. #design #hardware #electronics