This repository provides code and resources for automating manufacturing feature recognition in CAD designs using vision-language models.
This repo contains implementation of deep learning-based steel surface defect segmentation models. Extensive experiments on several deep learning frameworks have been presented with various performance analysis and comparison.
Accelerating the Transition from Digital to Intelligent Manufacturing
A digital-twinning simulation platform for intelligent industrial manufacturing
FastAPIと軽量AIモデル（TensorFlow Lite）を活用した工業向けAI支援型金属表面検査システム。 金属表面の有効性判定（ゲートキーパー）と欠陥傾向分析を段階的に行い、健全性スコア算出、日本語PDFレポート生成、および工場現場に最適化された検査ワークフローを提供します。 本システムにおけるAI解析結果は参考指標であり、最終判断は検査担当者の責任において行われます。 (An industrial-grade AI-assisted metal surface inspection system using FastAPI and lightweight TensorFlow Lite models. The system performs staged validation
A Grafana panel plugin for visualizing 3D CAD models, point clouds, and metrology data within Grafana dashboards.
Predictive maintenance and quality control system for manufacturing. Uses sensor data and computer vision to predict equipment failures, optimize production lines, and detect product defects in real-time.
Upload your production data. Get intelligent OEE analysis in minutes. AI-powered manufacturing analytics — the engineer who reads your data.
Qualify-As-You-Go Sensor Fusion, Process Zone Signatures and Deep Contrastive Learning for Multi-Material Composition Monitoring in LPBF Process
Circular economy guide
Real-time predictive maintenance engine for industrial assets using Machine Learning and Sensor Telemetry.
An image anomaly detection model for B2B sensor products
This is a Predictive Maintenance System.
A collection of all the projects I have worked till now. As per the data compliance and NDA, I will share an alternate code with dummy data instead. I would like to credit my mentor, Kumagai-san, Zhao-san & Iizuka-san who suggested me alternate methods and have contributed to my overall understanding.
Multi-agent RAG system for manufacturing defect analysis — LangChain, LangGraph, SAP integration
As a follow-up to our TestStand ETL, we develop an interactive dashboard using Power BI to uncover process trends and statistical control metrics.
End-to-end analytics and decision-support engines across the global value chain. Bridging FinTech risk modeling, supply chain logistics, industrial process control (moisture optimization), and biological R&D to drive measurable business ROI.
Agentic PCB defect inspection using Amazon Nova on AWS Bedrock — 7-step MCP pipeline with autonomous tool-calling (quarantine, work orders, knowledge graph)
My company's repository
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