ML-Driven Axial Fan Design Automation

₹600-1500 INR

Closed

Posted 7 days ago

₹600-1500 INR

Paid on delivery

Subject: AutoThings – CAD → ML Design Intelligence (Fan Example) Hi , Sharing a quick explanation of the AutoThings idea, using a fan example to make it concrete.. Core idea: Move from manual CAD + trial‑and‑error to a closed loop: Parametric CAD → Data → Learning → Optimized CAD CAD remains central, but geometry becomes machine‑readable and learnable. Fan example (in brief): Fan blade modeled in Creo with full parametric control (twist, sweep, chord, spline points) Geometry parameters exported as data CFD results (flow, efficiency, noise proxies) added back to the dataset ML learns how geometry affects performance and noise Optimized parameters are written back and CAD rebuilds automatically Designers validate surfaces, manufacturability, and intent Why it matters: Same approach applies to compressors, turbines, motor cooling, ducts, structures—any mechanical product where geometry matters. This is new, exploratory research (limited references, budget‑constrained). If it proves value, it can evolve into reusable IP and potentially a small firm (AutoThings). Would appreciate your quick thoughts: Does this make sense from a CAD perspective? Any concerns or ideas? Interest in trying a small pilot? Happy to discuss more over the weekend. Best, Rohit

Machine Learning (ML)

Python

Data Science

Finite Element Analysis

R Programming Language

Data Analytics

Project ID: 40411690

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6 freelancers are bidding on average ₹1,250 INR for this job

@PyMLExpert1

Leveraging my rich background in Python and Machine Learning, I am the perfect candidate to spearhead your ambitious AutoThings project. I understand the challenges that come with manual CAD designs and the arduousness of trial and error. Hence, my focus is on creating a closed loop system using parametric CAD, data collection, learning and optimized CAD to elevate fan designs to a new level. In previous assignments, I have collaborated with engineers to employ AI models in designing complex algorithms for large-scale data analysis; a skill integral to the AutoThings concept. My proactive approach towards understanding how intricate parameters affect performance and noise have always yielded impeccable results, ensuring maximum efficiency. Moreover, this project aligns directly with my professional goals: creating reusable IP that ultimately helps businesses revolutionize their practices using AI automation.I look forward to diving in this new horizon of digital experimentation while adhering to the constraints of your budget. Let's make AutoThings a sustainable reality together.

₹1,500 INR in 7 days

5.0

(3 reviews)

1.9

@mohamedr430

As a mechanical engineer and productivity enthusiast, I am consistently in pursuit of finding better ways to optimize design, manufacturing, and operational processes. My competency in Finite Element Analysis (FEA) makes me a powerful asset for your ML-driven axial fan design automation project. I understand the crux of your problem - moving from manual CAD to an iterative, efficient and intelligent ML-based design process - and I grasp its potential value thoroughly. Bringing precision and accuracy to the manufacturing industry has been my forte for the past X years as a mechanical engineering expert. I have used FEA extensively to solve complex engineering problems pertaining to fluid dynamics, heat transfer, and structural mechanics - all of which are critically relevant to improving the performance, efficiency, and noise management of fans in your project. Moreover, my innovative mindset aligns perfectly with this exploratory research that you're leading. Driven by challenges and always on the lookout for opportunities to learn, grow and create impact, I'm confident I can make valuable contributions. Further, by processing 'parametric CAD → data → learning → optimized CAD" conceptually resonates with me as it reminds me of the iterative nature of FEA and simulation processes - testing designs against different scenarios/sensitivities until they achieve optimum performance. Let's discuss more about how we can turn this novel process into a reusable IP together!

₹1,300 INR in 3 days

5.0

(1 review)

0.4

@shokoufeh1994

I build Python-based ML pipelines for engineering automation using scikit-learn, TensorFlow, and PyTorch. My workflow covers data preprocessing from CFD outputs, feature extraction, and model training for performance prediction. I have delivered similar work where I used gradient boosting models to predict fan efficiency across operating points. The pipeline includes cross-validation loops, hyperparameter tuning, and exportable inference scripts. I integrate results with parametric design tools like Python-based Solidworks API or FreeCAD scripting when needed. Code stays modular so you can retrain models as new fan geometry data comes in. Are you starting from CFD simulation results, physical test measurements, or geometric parameters like blade angle and chord length?

₹1,050 INR in 7 days

0.0

(0 reviews)

0.0

@manas603

Bhai dheak ye mera phala project hone wala hai , and I have a full access cad maily AutoCAD software in my college clubs , i have access to it .... Also lots of 3d printer are here.... In drone clubs used mostly by all.... you can check my LinkedIn I have expertise in that and i know how to connect ml with other softwares I have junior and colleagues that will make out this project done ... Connect with me Tommorow as it is Sunday.... Bhai thanks you if you give me kick start to my journey to be a first' client of mine.... Feel free to connect Manas Modi 8279444413

₹1,100 INR in 7 days

0.0

(0 reviews)

0.0

@Viswanath1997

I could create easy 3D modeling in CAD software . I easily create this model and perfect . I very well known AUTOCAD/CATIA V5/CREO/NX CAD/SOLIDWORKS

₹1,050 INR in 4 days

0.0

(0 reviews)

0.0

@sarahmirza5060

Hi Rohit, This makes sense, and the direction is strong. You are essentially turning CAD into a feedback-driven system instead of a static design tool, which is exactly where things are heading. From a CAD and engineering perspective, the core idea is solid. Parametric geometry already contains the structure needed for learning, and combining it with CFD outputs closes the loop in a meaningful way. A few points to consider: • The quality of your parameterization will define everything. If geometry is not controlled cleanly, the learning will be noisy. • You will need a consistent way to map CAD parameters to CFD results, especially when designs become complex. • Data efficiency will matter since CFD is expensive, so sampling strategy and design space exploration are important. • Constraints like manufacturability and geometry validity should be built into the loop early. Where this becomes powerful is exactly what you mentioned, compressors, turbines, cooling systems, anything geometry-driven. I am an aerospace engineer with experience in CFD and parametric modeling, so this aligns well with how I approach design problems. I would be interested in trying a small pilot. A fan or simple flow case is a good starting point to validate the loop before scaling. If you want, we can define a small test case with limited parameters and build the first working loop.

₹1,500 INR in 7 days