DESIGNSPARK FORUMS

First, the critical issue. It doesn't occur in previous versions (12 and earlier). It's simple: click on Utilities with the Start page active. In my environment (new Windows 11), it crashes 100% of the time. I looked at some older forum logs and it seems they're churning out critical issues again with V13. This is a tradition with this software, happening almost every time since V7. https://www.rs-online.com/designspark/designspark-pcb-13-0-crashes-and-backwards-compatibilityhttps://www.rs-online.com/designspark/designspark-pcb-not-generating-drl-file The most ridiculous bug recently is that copy and paste doesn't work. It was suddenly discovered in V11.0.0. They released the official version without even testing copy and paste. That's their development approach. https://www.rs-online.com/designspark/no-copy-paste In previous versions, there was always a bug in the copper foil filling process. Especially in new version x.0.0, it was always the case. I still don't trust their software. I always need older, stable versions of the installer, but some have been lost forever. All I have now is V12.0.0. I really need V12.0.2 and V11.0.1, but I don't have them. https://www.rs-online.com/designspark/design-spark-pcb-is-not-worth-the-subscription So I copied the V11.0.1 program files from my old PC to my new PC where I installed V13. So far, it's working. I'll check for detailed issues later, but it's likely to have fewer bugs than the countless unresolved issues in V13. What the DS team should do immediately is save and release past stable versions. Not everyone needs the new features. Please consider the right to continue using stable, problem-free versions. I hope this information is helpful to someone.  

  Example: Closed coil Compression Spring. Creation uses a single helical pull operation showing the quickest method. Tips on the correct use of the Pull tool. Transition between working and closed coils showing a perfect blend and not a crease - the easy way with tips on selection, trimming blending and combining. Compression Spring details: OD 6mm Wire Dia 0.25mm 2 1/2 close but not exactly closed coils both ends. (0.01mm coil separation) Overall height 10.6 mm Other: *Smooth transition between closed and working open coils. Open coils 1.5mm spacing - 6 full turns   * Optional > the fastest way is shown.   First sketch out the spring shape: Information from spring catalogue or from calculations:   Create a point at the spring wire center: The OD is 6mm, so I use 3-0.25/2 or enter 2.875   Calculation to create the closed and working coils in a single Pull (Iterative) command: Note: The overall height of the coil center line, is the actual spring height less 1x wire diameter 10.6-0.25 =10.35 The full height of the coil consist of 3 sections: 1. Approximated closed Coils > calculate or enter formula  2. Working Coils 6x1.5mm 3. Approximated closed Coils > enter first result    Select the point > Pull Select Revolve Axis Z   Select Options - Revove helix In height column, type (10.6-0.25-(6*1.5))/2 > TAB to calculate the closely coiled height (note this value), now type the Pitch value which must be a larger than the wire diameter , say 0.26 ,otherwise the Cylinder thickening process will not be created. Note the Full pull length indicator icon is now active: Select to create the first coil center line wire length section Note the Pull helix command is still active but the pull full height icon symbol is now inactive: Type the working coil height or the formula 6*1.5 TAB > type the working coil pitch 1.5 Note the Pull full height icon symbol is active - select it to add the working coils.       Repeat the first closely coiled section results or repeat the full formula  > Height: 0.675  Pitch: 0.26 > Enter Note the Full pull icon is active :   Check the Z height between the end positions: The full coil consists of 3 sections unjoined (not connected):   For a simple spring without a blend transition between the 3 coils: Box select coils Select the Cylinder tool Enter the wire radius 0.25/2 Note the kink in the coil will be visible in any drawing view:   If preferring to not have any visible kinks on the model, then the wire helix end sections need to be blended. First, ADD a point on the curve when it is to be split: Sketch > Enable 3D ( Constraint sketcher)  Usage Tip: Do not attempt to select 'through' the coil - have the curve ends to be selected at the front of the model. Tab> Repeat for touching curve: Now use the Split Curve command from the Sketch tools selecting the curve at the point position to split it Repeat for all curve ends: If creating drawings, place a point at the original curve intersections for dimensioning purposes. Split the curves and delete the curve end lengths and the points on them:   Select each curve end > key B ( blend) Select the Add Tangent guides or Alt ( Ctrl +Alt) selecting each curve individually Comfirm the blend by selecting the green Tick Join up all the 3 helix sections and the 2 curve blends: Select the first insecting coincident curve end position > Fill (key F/f), repeat by pressing the key F/f to complete. The whole coil is now a single spline as shown.   If a drawing is required, it is important to rename the points: Finally to add thickness to the center line curve, select it, then select the Cylinder tool from the Design Tab - Insert menu   Enter the wire radius The transition sections are tangent:   A Detail drawing can be made: Model points and a circle are required to create dimensions (requiring renaming). Helix spline surfaces can not be otherwise dimensioned. With practice, the coil spring can take only 4 minutes to completely model with transition blends as shown or less without the blends.

I want to change menasurement units in DSM 6.0.3, but the File menu item does not have an Options item. Here's a screen grab: What am I doing wrong?  

The Prove It challenge has now closed. We've had a great response and would like to thank everyone for their entries and interest. There have been some innovative ideas and we're currently working through all of the submissions as part of the judging stage.  Watch this space for more updates and details of the winners. Thank you

New Otii 3.7 is out – this is what battery life estimation should look like! The industry runs on datasheet assumptions. We just made that optional. Your device's power consumption and battery profile now work together in one seamless workflow. Real measurements in, defensible estimates out. Here's what's new: ▶ Battery Profile Manager - Browse your battery profiles database to see how different batteries perform with your device. Compare profiles side by side, and easily import or export to stay in sync with your team, suppliers, and partners.▶ Battery Life Estimator - Combine real measurements from your device with a measured battery discharge profile to get a reliable runtime estimate in seconds, along with the used capacity.▶ Custom Monitor - Watch your system in real time, choose only the variables that matter for your setup, and view channels from different Otii instruments together, all without starting a recording. Read the full feature breakdown Get started now. Otii Ace Pro 257-3555Otii Arc Pro 256-4687

The "Save Copy As" button is grey. I try to duplicate the file by right clicking it but dont know what to do for the next step

I recently decided to update my DS pcb program and uninstalled the old one first, on trying to install the new version it pops up a box where I have to agree to the license agreement, but the text is very small and I cannot scroll to an ecept box, and I can't increase the size of the box.  I have tried changing the scale and text  in windows but without success, can anybody help?

The contest link expired 1 hour before the original deadline, which was announced earlier (Friday 8 May, 5.00pm GMT.) which is 10:30 PM IST (India Standard Time). But I've tried to submit my entry at 10:00 PM, and it shows me an error that I can't enter anymore, and the link has expired as well. I've attached the screenshot with time proof.

Somehow I did something that removed the "tool" icons on the left side of the main window. I've read the relevant posts here, and tried all the suggestions, but no joy. I just uninstalled DSM and deleted the  SpaceClaim files, plus the third suggested file. Still no tools appear. Screen shot attached. What's left to do? How can I get the tool icons back? Thanks for all suggestions.  

#ProveIt Project Title : DecayDock - AI-Powered Smart Fridge Companion for Reducing Household Food Waste 1.Concept Summary DecayDock is an AI-powered magnetic smart device designed for refrigerators that helps users reduce food waste through real-time food recognition, inventory management, freshness tracking, and intelligent waste prediction. Using Edge AI on the Arduino UNO Q, the system identifies vegetables, fruits, leftovers, and packaged food items through a compact camera module and estimates shelf life using behavioral and environmental data. The system visually displays freshness through intuitive progress bars, sends smart reminders before food expires, and encourages sustainable consumption habits through intelligent notifications and analytics.   Role of Arduino UNO Q The Arduino UNO Q forms the intelligence core of the project. It performs local AI inference for food recognition, manages sensor fusion, calculates freshness prediction models, and enables low-power edge computing without requiring constant cloud processing.   The UNO Q dual brain allows the system to: Run TinyML food classification locally Process environmental and usage data in real time Reduce latency and cloud dependency Enable privacy-focused offline intelligence Operate as a compact, energy-efficient embedded AI system   2. THE THEME DecayDock directly addresses the challenge theme “Sustainability through Intelligence” by reducing preventable household food waste using embedded AI and real-time environmental awareness. Food waste is one of the largest contributors to unnecessary carbon emissions and resource loss globally. Many households waste food not because of spoilage alone, but because people forget what they already have stored inside refrigerators. DecayDock tackles this overlooked behavioral problem using intelligent edge computing. Reference https://www.epa.gov/recycle/preventing-wasted-food-home?utm_source=chatgpt.com Inspiration Behind the Approach The project was inspired by observing how frequently vegetables, leftovers, and dairy products are forgotten inside refrigerators until they become unusable. Existing smart kitchen systems mainly focus on inventory tracking or expiry alerts, but very few systems attempt to understand human usage patterns and prevent waste proactively. This inspired the development of a system that combines sustainability, behavioral intelligence, and accessible AI technology into one compact device.   What the Project Explores The project explores: Edge AI for sustainability applications Behavioral food waste prediction Human-centered environmental technology Real-time embedded intelligence Ambient and visual sustainability feedback systems Rather than simply detecting spoiled food, the system predicts future waste risk based on usage behavior, storage duration, and inventory patterns.   Why Arduino UNO Q Strengthens the Project The Arduino UNO Q dual brain capabilities, it enables real-time TinyML inference directly on-device while maintaining low power consumption and offline functionality with use one for real-time sensor control and the other for AI-driven object detection.Its AI acceleration capabilities allow DecayDock to classify food items locally and process contextual freshness information without depending entirely on external servers. This strengthens the project by: Improving speed and responsiveness Reducing cloud processing requirements Supporting energy-efficient sustainability technology Demonstrating practical embedded AI implementation _________ 3. TARGET AUDIENCE Intended Users DecayDock is designed for: Students living independently Busy working professionals Families managing groceries Sustainability-conscious households Neurodiverse individuals with executive function challenges   Relevance to the Audience Many users struggle to remember stored food items, especially vegetables and leftovers hidden behind other items in refrigerators. This often results in: avoidable food waste unnecessary grocery spending over-purchasing poor food management habits DecayDock simplifies this process by creating an intelligent visual inventory system that passively assists users without requiring constant manual input.   Research and Insights Research from environmental and food sustainability studies shows that a significant portion of household food waste is caused by: forgotten leftovers improper inventory tracking overbuying groceries poor visibility inside refrigerators The project also considers neurodiverse users who may experience difficulties with organization, memory, or routine management. Instead of using stressful alarms or intrusive reminders, DecayDock uses calm visual feedback and predictive notifications. ______ 4. CONCEPT IDEA Detailed Concept Description DecayDock is a compact magnetic smart enclosure attached externally to a refrigerator. The device contains: an AI-enabled camera a touchscreen interface environmental sensors wireless connectivity When users place food items in front of the camera, the system identifies the item using TinyML image classification running on the Arduino UNO Q.   After recognition: 1. The item is automatically added to inventory 2. Estimated shelf life is calculated 3. Freshness status is visualized through dynamic progress bars 4. The data syncs to a cloud dashboard 5. Smart reminders are generated before waste occurs   Key Features AI Food Recognition The system recognizes: vegetables fruits milk products leftovers packaged food using edge AI image classification using Edge Impulse software.   Freshness Prediction System Instead of detecting actual spoilage visually, the system estimates freshness based on: food type storage duration refrigerator temperature user interaction history This creates a reliable and lightweight shelf-life prediction model using Arduino uno Q dual brain processing capabilities.   Dynamic Freshness Visualization Freshness is displayed using color-coded progress bars: Green → Fresh Yellow → Moderate freshness Red → Expiring soon This creates intuitive, instantly understandable interaction.   Smart Waste Prediction The system learns repeated waste patterns over time. Example: If users repeatedly forget leafy vegetables, the system adapts reminders earlier for similar items. This transforms the project from a simple inventory tracker into a behavioral sustainability assistant. Like reference https://youtu.be/By-1X25D02Y?si=QAJoLrjDtlO_czVt ________ 5.IMPELEMENTATION  Web Dashboard & Notifications Users can: monitor inventory remotely receive WhatsApp notifications view waste analytics receive “Use Soon” recommendations   Hardware Components Component Purpose Arduino UNO Q Main AI processing ESP32-CAM Image capture TFT Touch Display User interface Temperature Sensor Fridge monitoring RGB LEDs Ambient freshness status ESP32 WiFi Cloud communication Magnetic Enclosure Refrigerator attachment   Unique Innovation The unique innovation of DecayDock is that it predicts food waste through behavioral intelligence rather than simply detecting spoilage. Most smart kitchen systems monitor food directly. DecayDock studies: usage patterns forgotten inventory repeated waste behavior shelf interaction frequency This creates a proactive sustainability system rather than a reactive monitoring device.   Implementation Approach The project is intentionally designed as a practical, low-cost prototype using minimal hardware and existing Arduino ecosystem tools. The implementation process includes: Training TinyML food recognition model using the Edge Impulse software reference: https://circuitdigest.com/microcontroller-projects/object-recognition-using-esp32-cam-and-edge-impulse  Building touchscreen inventory interface  Developing freshness prediction logic Integrating cloud dashboard Adding notification system ,like reference https://circuitdigest.com/microcontroller-projects/esp32-cam-attendance-system-using-circuitdigest-cloud  Designing magnetic enclosure   __________ 6.RESOURCES REQUIRED Software AppLab Arduino IDE Edge Impulse Arduino Cloud LVGL UI framework Hardware Arduino UNO Q ESP32-CAM TFT Display Sensors Basic enclosure materials How the System Works Step 1 — Scan Food User places: tomato onion milk leftovers in front of the camera. The AI model running on Arduino UNO Q identifies the item. Step 2 — Inventory Creation The item is automatically added to the inventory list. Example: Item.                 Freshness Tomato.            90% Milk.                  60% Spinach.           30% Step 3 — Freshness Prediction The system estimates shelf life using: food type fridge temperature storage duration user behavior patterns Step 4 — Visual Freshness UI Freshness appears as: Green → Fresh Yellow → Consume Soon Red → High Waste Risk This makes the system visually easy to understand. Step 5 — Notifications The system sends: WhatsApp alerts mobile notifications dashboard warnings Examples: “Milk may expire tomorrow.” “You already have onions in stock.”   Arduino Ecosystem Integration Arduino Services Used Arduino UNO Q Main AI processing board. Arduino Cloud Used for: remote dashboard inventory monitoring analytics mobile access Edge Impulse TinyML Used for: food image classification AI model training optimized embedded ML deployment Arduino IoT Cloud Notifications Used for: remote alerts expiry reminders cloud synchronization LVGL Graphics Framework Used for: modern touchscreen interface progress bars smooth animations  Bill Of Materials (BOM) Component.                     Qty.          Approx Price Arduino UNO Q.                1.              ₹3500 ESP32-CAM.                     1               ₹700 3.5” TFT Touch Display.  1.             ₹1200 DHT22 Temp Sensor.      1.              ₹250 WS2812 RGB LEDs.         1.              ₹200 Magnetic Enclosure.       1.             ₹400 Breadboard + Wires.          1.            ₹300 USB Power Supply.             1.                      ₹250 Estimated Total Cost: ₹6500–₹7500   Circuit Connections Basic Hardware Architecture Connections ESP32-CAM : handles image capture Arduino UNO Q :  processes AI logic freshness calculation inventory system TFT Display connected via SPI: MOSI MISO SCK CS DC RST DHT22 Sensor connected to: Digital Pin D2 RGB LEDs connected to: Digital Pin D6 ESP32 WiFi handles: Arduino Cloud Notifications  dashboard sync Simple System Flow Workflow Camera → AI Recognition → Inventory Update → Freshness Prediction → Dashboard → Notification   Sample AI Logic Example Freshness Formula freshness = ((totalDays - storedDays) / totalDays) *100; Example Tomato: Shelf Life = 7 days Day 1: 100% Day 4: 57% Day 7: 0% → RED ALERT   Sample Arduino Code Food Freshness Logic int totalDays = 7; int storedDays = 3; int freshness = ((totalDays - storedDays) * 100) / totalDays; if(freshness > 70){    Serial.println("Fresh"); } else if(freshness > 30){    Serial.println("Consume Soon"); } else{    Serial.println("Expiring"); }   Example LED Status if(freshness > 70){    // Green LED } else if(freshness > 30){    // Yellow LED } else{    // Red LED }   Challenges & Solutions   Challenge Solution Accurate food recognition Limited optimized food dataset Real spoilage detection complexity Shelf-life prediction model instead Hardware limitations Lightweight TinyML models User simplicity Minimal interaction UI _________ 7. IMPACT & BENEFITS Positive Difference DecayDock helps reduce: preventable household food waste unnecessary grocery spending over-purchasing behavior forgotten food inventory It promotes more mindful and sustainable consumption habits.   Short-Term Impact Better fridge organization Reduced food spoilage Increased awareness of waste habits Easier grocery management.   Long-Term Impact Lower household food waste Reduced environmental footprint Improved sustainable living habits Wider adoption of embedded AI sustainability tools   Environmental Benefits   Reducing food waste indirectly lowers: carbon emissions water waste energy waste from food production The system also operates using low-power edge AI, minimizing unnecessary cloud processing.   Social & Economic Benefits Users save money while building healthier consumption habits. The system also supports neurodiverse users through passive assistance and visual guidance. _____ 8. ORIGINALITY & CREATIVITY   What Makes the Idea Original   Most smart fridge systems: monitor expiry dates track inventory manually rely heavily on cloud infrastructure DecayDock introduces: behavioral waste prediction edge AI food recognition ambient sustainability visualization adaptive reminders based on user habits   Competitive Advantage The project stands out because it combines: sustainability embedded AI behavioral intelligence visual interaction practical real-world usability into one compact consumer-friendly device.Rather than functioning as a traditional IoT monitor, DecayDock behaves as an intelligent sustainability companion. _______ 9. PROTOTYPE / EARLY CONCEPT Prototype Vision The prototype consists of: a magnetic smart enclosure touchscreen inventory system AI camera module LED freshness indicators The UI displays: inventory cards freshness bars expiry countdowns waste alerts _______ 9.FUTURE SCOPE Future upgrades may include: barcode scanning recipe suggestions voice assistant grocery auto-ordering NFC food tags multi-user family tracking 10.CONCLUSION DecayDock is an AI-powered smart fridge companion that uses Arduino UNO Q dual brain features and Edge AI to reduce household food waste through intelligent food recognition, predictive freshness tracking, and behavioral sustainability analytics.

Hi  How do I hide selected nets from the yellow rats nets when routing a PCB, i've used DS for years and I always turn off power connections and route signals on a multilayer board.   I can select a net but the option to hide it isn't available for some reason.   Cheers

Hi everyone, I’ve been reading through the previous discussions here regarding airflow optimization in compact 2U enclosures. One specific point that caught my attention was the mention of how standard chassis fans often struggle to maintain positive pressure when the internal drive bays are fully populated. This is exactly the wall I’ve hit with my current project. I’m currently transitioning a data-heavy workstation from consumer-grade SATA over to an enterprise-grade storage setup. I recently integrated a few 3.2TB 2.5-inch SAS 12Gbps SSDs to handle a massive increase in simultaneous I/O streams. While the 12Gbps throughput is absolutely incredible compared to what I’m used to, the heat dissipation has been a bit of a shock. In my experience with consumer SSDs, thermals are almost an afterthought, but these 3.2TB enterprise drives seem to run significantly hotter, especially when they’re under sustained load in a tight 2.5-inch form factor. My current setup has four of these drives stacked closely together, and even with the intake fans cranked up, I’m seeing some worrying temperature spikes that I suspect might lead to thermal throttling during long data-logging sessions. I really want to stick with the 12Gbps SAS interface because the latency is so much lower for my specific use case, but I'm worried about the longevity of the hardware if I can’t get the "hot spots" between the drives under control. Has anyone here experimented with custom 3D-printed ducting or specific active-cooling backplanes to help move air directly across the surface of enterprise SAS drives? Or is it generally better to just sacrifice the density and leave empty slots between the drives to allow for better natural convection?

Just a quick reminder that entries for the Arduino UNO Q Community Challenge close tomorrow, Friday 8th May.If you’ve been working on an idea, refining your concept, or waiting for that final push, there’s still time to submit. We’re looking for clear ideas, thoughtful use of the UNO Q, and a strong link to your chosen theme. Whether you’re exploring AI at the Edge, Sustainability Through Intelligence, or Human‑Machine Harmony, we’d love to see what you’ve been building. Submit your entry here before the deadline: Community Challenge | Arduino Uno Q | DesignSpark Good luck to everyone taking part... we can’t wait to see your ideas.  - Connor

when i use the create a round corner tool it looks good in design spark as a .dxf but when I load it into sheet cam the the rounded corners are distorted. any help? thanks Gary

After upgrading my computer's processor and graphics card, I can't log in to DSM 6.03, even though I can log in to my DSM account without any issues. I'm getting the message "DesignSpark Mechanical has encountered a registration error." I've changed my password several times, but to no avail. I still can't log in. Please help. Chris