AI CNC Program Generator

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@tyronet2

Honest read on this: what you're describing is essentially Fusion 360 CAM plus an AI feature-recognition layer, and Autodesk has spent 20 years and hundreds of engineers on that product. A working MVP that actually generates safe G-code for turning + VMC + sliding-head is a multi-person, multi-month build easily in the $50k+ range — the ₹7000 budget on this listing won't get you past a proof-of-concept for one machining strategy. That said, a scoped Phase 1 is buildable and useful. My background: Python for 9+ years, ML/CV work including CNN-based image processing at Marin Software, LangChain and OpenAI pipelines for structured extraction from documents, and comfortable stitching together the parsing layer (ezdxf for DXF, ODA File Converter for DWG, pdf2image + OpenCV for PDF prints where geometry is display-baked). A realistic Phase 1 I'd propose: DXF/DWG parsing + rules-based feature detection for a narrow shape family (say cylindrical turned parts with OD/ID/grooving/threading) + a template-driven G-code generator with a Fanuc post + a simple cycle time formula (feed rate × length + rapid + tool change constants). LLM as the reasoning layer to pick machining strategies from a rules library, not to write G-code directly — LLMs will happily generate G-code that crashes the machine.

@anjalio22

I would love the opportunity to develop your AI-powered CNC Program Generator. I have experience building Python-based automation solutions, CAD/CAM workflows, AI-assisted engineering tools, and custom desktop/web applications that streamline complex manufacturing processes. For this project, I would use a modular architecture with Python, OpenCV for drawing and feature recognition, Machine Learning where beneficial, CAD libraries for DWG/PDF parsing, and configurable post-processors to generate Fanuc-compatible G-code (with support for additional controllers). The system will automatically identify machining features, recommend tooling, materials, generate tool sheets, estimate cycle times, and package NC code and reports into a single downloadable ZIP. The application will be designed for easy expansion, allowing new machine types, tooling libraries, and post-processors to be added without changing the core engine. I'm confident I can deliver a reliable, scalable solution and work closely with your sample drawings and machining trials to validate accuracy and refine the output. I am committed to delivering high-quality results on schedule and would welcome the opportunity to discuss your requirements in detail.

@jaroslavcaprata

Hi, You’re looking to build an AI-assisted CAM automation system that can convert engineering drawings into CNC-ready outputs, including G-code, tooling recommendations, material suggestions, and cycle-time estimates. I would approach this as a combination of CAD/CAM feature extraction, computer vision, rule-based machining logic, and AI-assisted decision support. The system would need to process both PDF drawings and DWG files, identify manufacturing features such as holes, threads, pockets, contours, and turning operations, then map them to machining strategies and post-processed CNC code. A practical architecture would separate the drawing analysis layer, machining rules/tool library, post processor system, and reporting module so additional machines, tooling, and controllers can be added without redesigning the entire application. I can help design the application structure, AI/CV pipeline, backend logic, and generation workflow. For cycle-time prediction, I would recommend validating the model against real shop-floor data and gradually improving accuracy using actual machining results. Could you share a few sample drawings and examples of existing CNC programs/tool sheets so I can evaluate the required feature recognition complexity and machining rules? Jaroslav Caprata

@alluankit01

Hi, I can build a proof-of-concept AI/rule-based CNC program generator that reads sample PDF/DWG drawings, detects key machining features, and produces structured G-code, tool list, and cycle-time estimates for validation. The best solution is to start with a focused POC instead of claiming a full CAM replacement immediately. I’ll define supported part types, machine type, Fanuc-style post format, tool library, material rules, and feature-recognition limits, then generate test NC programs for your sample drawings. I’m comfortable with Python, CAD/DWG parsing, PDF data extraction, OpenCV, rule-based machining logic, CNC turning/VMC workflows, G-code generation, tooling databases, cycle-time estimation, and modular post-processor design. Deliverables include: * Drawing upload workflow * PDF/DWG parsing prototype * Basic feature recognition * Fanuc-style G-code output * Tool list generation * Material/rule mapping * Cycle-time estimate * Downloadable report/ZIP * Modular architecture notes * Validation notes from sample parts I’ll focus on a practical POC that proves accuracy before expanding to full machine-post and tooling-library support. Best regards Ankit

@NEHABHAT92

Utilizing my 9+ years of experience in web and mobile app development, particularly with Python, I bring a unique perspective and skillset to the table for your AI CNC program generator project. As you've mentioned, one of the key expectations is accurate feature recognition, something I'm well-versed in dealing with various CAD formats as well as CAM automation which will aid me in recognizing prismatic and turned parts. For this purpose, I plan to leverage Fusion API, OpenCV, and TensorFlow (and other relevant technologies) to their fullest extent. Combining my technical aptitude with my enjoyment for solving complex problems will give rise to a program that not only delivers ready-to-run G-codes but also complies with your preferences such as Fanuc-style controls. Given your emphasis on a modular architecture, rest assured that the core of the program will be adaptable to future machine posts and tooling libraries — minimizing any disruptions that future changes may bring. I can assure you a focused timeline for development while also prioritizing quality so as to accomplish your expectations – even validating deliverables through real cutting trials. The project's demands for a clean UI, simple yet efficient file-handling system & structured reports align impeccably with my knack for intuitive designs. Let's revolutionize your workflow; turn your **IDEAS TO REALITY** with me!

@ibtemirlan

Your target is a full automated CAM system, so I suggest starting with a bounded technical discovery milestone rather than promising production-ready G-code within this budget. For 10,000 RUB and up to 5 hours, I will: 1. Review up to 5 representative PDF/DWG drawings and target-machine details. 2. Define the feature-recognition and geometry-normalisation pipeline. 3. Map a modular architecture for machining rules, tooling libraries and Fanuc-style posts. 4. Specify safety checks and a shop-floor validation protocol for NC output and cycle-time estimates. 5. Deliver an MVP scope, risk register, stack recommendation and phased effort estimate. Deliverable: one technical specification/report; no ready-to-run NC generator is included in this milestone. Which exact machine models/controllers should the first MVP support? Can you provide matching drawings, manually verified NC programs, tool sheets and measured cycle times?

@MohamedAshraf812

Hi there, Automating CAM from PDF/DWG drawings requires CAD vector parsing, rule-based machining engines, and Fanuc post-processor generation. As a Systems & AI Engineer experienced in Python, vector/image processing, and automated G-code tools, I can build this CAM system end-to-end. Technical Stack & Pipeline: - Drawing Parser: EZDXF (for DWG/DXF vector parsing) + PyMuPDF/OpenCV (for PDF print geometry) extracting turned profiles, bores, threads, and pockets. - Feature Recognition & Strategy Engine: Python rule engine mapping geometry to cut depths, feed rates, speeds, and tool paths for Turning, VMC, and Swiss lathes. - Tooling & Cycle Time Estimator: Dynamic tool selection database (cutters, inserts, offsets) paired with feed-rate calculation within ±5% tolerance. - Post-Processor Engine: Modular Fanuc ISO-standard `.nc` generator (extensible for other controls). - UI & Export: Streamlit drag-and-drop web UI exporting NC code, tool sheet, and cycle report in a single ZIP file. Deliverables: 1. Complete automated CAM engine (PDF/DWG to Fanuc G-code). 2. Tooling list & cycle-time estimation module. 3. Drag-and-drop UI with ZIP package exporter. 4. Source code and setup documentation. Timeline: 10 to 14 days. Ready to inspect your sample drawings and start Phase 1. Best regards, Mohamed Ashraf

@Ashoksahu001

Hello, I understand you're looking to eliminate manual CAM programming by transforming PDF and DWG drawings directly into production-ready G-code with AI-driven feature recognition, tooling recommendations, and accurate cycle-time estimation. This is a complex engineering platform that requires robust computer vision, CAD parsing, machining intelligence, and a scalable architecture. My approach would combine OpenCV and OCR for PDF interpretation, native DWG processing, Python-based feature extraction, rule-based machining logic enhanced with AI, and a modular G-code generation engine supporting Fanuc as the initial post processor. The system will generate tool sheets, machining strategies, material recommendations, cycle-time estimates, and downloadable reports through a clean drag-and-drop interface. Why hire me? I build production-grade automation platforms with modular architecture, clean code, and AI workflows designed for long-term scalability rather than one-off prototypes. Recommended Stack: • Python (FastAPI) • OpenCV + OCR + DWG parser • TensorFlow/PyTorch where AI adds value • PostgreSQL • React.js desktop/web interface • Docker deployment • Modular post-processor framework Deliverables: • AI-powered drawing analysis • Automatic G-code generation • Tooling & material recommendations • Cycle-time estimation engine • Docker deployment & documentation Estimated Timeline: • MVP: 12–16 weeks • Production-ready platform: 5–7 months

@rahulsimraiya131

Hi, I've reviewed your requirements and understand you need an AI-powered application that converts PDF/DWG drawings into production-ready CNC programs, complete with tooling recommendations, material suggestions, and accurate cycle-time estimates. I've worked on AI-driven automation, CAD/CAM integrations, and complex engineering applications with a focus on scalability and performance. Key deliverables: ➤ AI-based feature recognition for PDF and DWG drawings ➤ Automatic G-code generation for CNC turning, VMC, and sliding-head machines ➤ Tool list, material recommendations, and cycle-time estimation ➤ Modular architecture with an intuitive drag-and-drop interface Approach: I'll build a scalable solution using the most suitable AI and CAD/CAM technologies, ensure accurate feature recognition and code generation, and thoroughly validate the workflow with your sample drawings before delivery. I can start immediately and would be happy to discuss the details. Best Regards, Rahul J

@aydont

YOU ONLY NEED ONE DEVELOPER WHO GETS IT RIGHT THE FIRST TIME. HERE'S WHY I'M THAT DEVELOPER. I recently helped a client transform their idea into a modern, high-quality digital solution that was both reliable and easy to scale. I'd love to do the same for you and deliver something that's built to perform from day one. We specialize in creating automated solutions for manufacturing processes, ensuring accuracy and efficiency. I understand your need for accurate feature recognition on prismatic and turned parts, along with a clean user interface that simplifies the workflow. We have extensive experience with AI and CAM technologies, leveraging tools like TensorFlow and custom Python. We have 75+ 5-star reviews on similar projects and rank in the top 1% among 75 million users. I'd love to chat about your project! The worst that can happen is you walk away with a free consultation. Regards, Aydon.

@enricos0

Hi there, Imagine a world where a simple drag-and-drop of your PDF or DWG instantly transforms into G-code, tool lists, and cycle-time estimates—all seamlessly generated without manual CAM intervention. With my extensive background in AI-driven CAM solutions, I’m confident in delivering a clean and user-friendly application that meets all your expectations. I see you're looking for accurate feature recognition for prismatic and turned parts, along with a modular architecture for future expansions. My experience with Fusion API, OpenCV, and custom Python will ensure we hit your targets, including cycle-time predictions within ±5%. I specialize in creating automated solutions that streamline workflows, and I'm committed to speedy communication and fast turnaround times. My returning customers appreciate my reliability and dedication. I am available for a quick chat! Regards, enricos0

@christech1117

Hi, I read your project "AI CNC Program Generator". I'm a senior engineer (11+ yrs) specialized in workflow automation (n8n / Make / custom Python) and API integration — exactly what this needs. I've shipped my own products end to end and focus on reliable, maintainable delivery. How I'd approach it: 1) Quick chat to lock the exact scope and edge cases 2) Build in small, testable steps so you see progress early 3) Clean handover with docs I can start right away and communicate clearly via chat. Happy to answer any questions. Best, Chris

@chandanm053

have worked on AI and computer vision projects involving OpenCV, Python, TensorFlow, OCR, CAD/CAM workflows, and automation, and I can show you similar work. This project aligns well with my experience—let's connect to discuss your sample drawings. My approach would combine OpenCV/OCR for PDF feature extraction, DWG parsing, AI/rules-based feature recognition, and a modular Python backend to generate ISO-standard G-code, tooling sheets, material recommendations, and realistic cycle-time estimates. The architecture will be scalable, allowing new machine posts, tooling libraries, and machining strategies to be added without changing the core system. I'll build a clean drag-and-drop UI with downloadable reports (G-code, tool sheet, and time study in a ZIP). Once I review your sample drawings and machining requirements, I can provide a detailed development plan and realistic timeline.

@andriyb0

With my extensive experience as both a mechanical and electrical engineer, I bring a unique skill set to the table that perfectly aligns with your project needs. My expertise in 3D modeling, technical drawings, and engineering documentation in SolidWorks allows me to work adeptly with both 2D PDF prints and native AutoCAD files. Your requirement for accurate feature recognition, modular architecture, and a clean UI are all areas where I can demonstrate my proficiency. Regarding your AI expectations, while I haven't used Fusion API or TensorFlow, I have honed exceptional coding skills in languages like Python which can be customised to meet the specific requirements of your project. My solid understanding of CAM/AI combined with my creative problem-solving abilities will be invaluable in creating an intelligent program that recognizes machining features and applies appropriate strategies. Lastly, I want to emphasize my commitment to delivering accurate results within set timelines. I recognize the importance of cycle-time predictions with regard to quoting and scheduling. It's this commitment to accuracy and meeting deadlines while maintaining strong communication that illustrates why I am the ideal fit for your project.

@shudhanshuj29

Hello, I have experience developing AI-powered automation solutions and CAD/CAM applications, and I can build the intelligent CNC programming platform you're looking for. The application will automatically convert PDF and DWG drawings into production-ready G-code, complete tooling sheets, material recommendations, and accurate cycle-time estimates with minimal user input. The solution will use Python as the core language, along with OpenCV for drawing analysis, DWG/DXF parsing libraries for CAD processing, and AI/rule-based feature recognition to identify machining operations such as turning, contour milling, drilling, tapping, threading, grooving, and deep-hole drilling. It will generate ISO-standard G-code compatible with Fanuc controls while maintaining a modular architecture that allows future machine posts and tooling libraries to be added easily. The interface will be simple and user-friendly—drag and drop a drawing, select the machine type and material, click Generate, and download a ZIP containing G-code, tooling reports, offset sheets, and machining time estimates. I understand that the software will be validated through real machining trials, so my focus will be on reliability, maintainability, and cycle-time accuracy. I'm happy to review your sample drawings, discuss your machining requirements, and deliver a scalable solution that meets your production needs. I look forward to working with you.

@princep607

Given my experience in automation, machine learning, and full-stack development, I’m confident I can build a reliable solution for your CNC automation requirements. Using Python, FastAPI, OpenCV, and TensorFlow, I’ll develop a system to recognize machining features from 2D PDFs and AutoCAD files and generate accurate G-code. It will support features such as taps, threads, grooving, deep-hole drilling, and contour milling. The application will include a clean drag-and-drop UI for uploading drawings, selecting machine/material types, and generating structured reports. Its modular architecture will also support future machine and tooling integrations. I’ll validate the system through real cutting trials, targeting cycle-time predictions within ±5% of actual runtime.

@ravigehlot02

You want an AI-driven app to convert PDFs/DWGs into safe Fanuc G-code, tool sheets, and accurate cycle times for VMC, turning, and sliding-head lathes. Realistically, a fully autonomous CAM engine covering all three machine types is a massive enterprise build. Trying to do it all at once risks buggy code and machine crashes. I propose a focused Phase 1 MVP that solves your immediate bottlenecks for a specific family of parts (like cylindrical turned components) before scaling up. I have deep experience in Python, computer vision pipelines, and LLM-driven structured extraction. I will use ezdxf to parse DWG data, alongside pdf2image and OpenCV to extract features from 2D prints. Crucially, I will not let an LLM write raw G-code directly—which is a massive safety hazard. Instead, I use LangChain to build a reasoning layer that identifies features and selects strategies, passing them to a deterministic, safe template-driven G-code generator. First, we will build the ingestion engine to parse vector data and recognize a narrow scope of geometries (OD/ID turning, grooving, threading). Second, we will map these features to rigid Fanuc post-processor templates to ensure the output code is structurally safe. Finally, we will implement a math-driven cycle-time calculator based on your exact feed rates and tool-change constants, wrapping it in a clean drag-and-drop UI so you can safely validate the ZIP outputs during shop floor trials.

@ExactSpec

Understanding: You need a desktop/cloud appliance that converts PDF/DWG prints into ready-to-run G-code (turning, VMC, sliding-head), a tooling list, material recommendations, and a cycle-time estimate with shop-floor accuracy—without manual CAM steps. The listing’s posted budget and client identity are unclear; technical targets (±5% cycle-time, full automation) carry material engineering and validation risk. SEAN REVIEW REQUIRED. Recommended approach: Build a modular, hybrid rules+ML pipeline: (1) deterministic feature extraction for common primitives (OpenCV + CAD parsers), (2) ML-assisted classification for ambiguous geometry, (3) a rules-based process planner and parametric strategy library to produce operations, (4) pluggable post-processor layer (Fanuc baseline) and tooling-library mapping, (5) a calibration/validation harness for cycle-time modeling tied to shop trials. Prioritize an MVP that demonstrates fully automated output for a constrained part family, then expand machine/posts. Approved capability / case study: ExactSpec has delivered customer-facing intake and routing platforms and platform-alignment engagements (CellularPort Buyback Platform; Innovation36T Platform Collaboration) that demonstrate practical product delivery and integration discipline. SEAN REVIEW REQUIRED. Delivery stages & milestones: 1) Discovery & acceptance criteria (sample dataset, controller list, IP terms) — 10 days — $6,000 — 10% 2) Feature extraction & parser MVP (PDF/DWG intake, primitives) — 21 days — $18,000 — 30% 3) Process planner & tooling library + baseline Fanuc post — 21 days — $18,000 — 30% 4) Cycle-time model, validation harness, shop-floor trial support — 21 days — $12,000 — 20% 5) Final polish, documentation, handover, and 30-day support — 11 days — $6,925.20 — 10% Price: Total fixed-price proposal = $60,925.20 (USD). Timeline: ~84 calendar days to initial delivery and trials. These numbers reflect technical risk and required engineering validation; posted INR budget appears inconsistent—please confirm currency and budget expectations. Key assumptions: client provides representative sample drawings, machine specs (controller posts), access to measured cycle-time ground-truth, and clear IP/licensing expectations. Exclusions: hardware procurement, machine tool upgrades, long-term on-site tuning beyond validation trials, and CAM commercial-license costs unless agreed. Focused questions: 1) Please confirm currency and realistic project budget (posted INR range appears placeholder). 2) Will you provide sample parts, measured cycle-time logs, and exact controller/post formats for initial validation? Recommended next step: Approve a paid Discovery & Acceptance milestone (as above) so we can lock test parts, controller targets, and an acceptance test plan. Upon approval I will issue a short Statement of Work and schedule kickoff within 5 business days.