Data CAMP

(Collecting, Analyzing, Monitoring, Predicting)

Use manufacturing big data to achieve successful digital transformation(DX)

CUSTOMER SUCCESS STORIES

A 3-Step AI Inspection Pipeline for Rubber Sheet Defects: Automating Detection, Classification, and Location Analysis

2025/02/10 | Quality Control

Challenge In the tire manufacturing process, rubber sheets produced during...

How the Anomaly Detector model was used to automate the quality control process using only normal data

2024/07/03 | Quality Control

Challenge Pouch-type batteries have flexible surface geometry,...

Implementing Quality Control in Multi-Product Small-Batch Production through the Utilization of Data CAMP’s ‘RECIPE’ feature

2023/12/14 | Quality Control

1. Challenge Managing Varied Quality Standards for Each Model without a...

DataCamp easily integrates with existing systems, collecting, monitoring, and analyzing data to help predict on-site conditions

AHHA Labs' Data CAMP Solution_process overview

Features

Compatible with Legacy Systems

Manage all manufacturing big data in an integrated manner with easy compatibility with legacy equipment, existing production systems, and commercial analysis tools.

제약 없는 확장성</p>
<p>별도 소프트웨어 개발 없이 장비/설비/데이터/기능 등을 자유롭게 추가 및 제거

Scalable thanks to Micro-Service Architecture

Add or remove equipment, facilities, data, and features flexibly without additional software development.

손쉬운 No-code </p>
<p>레시피 기반의 No-code 블록코딩 UI로 제조 프로세스에 최적화된 데이터 파이프라인을 쉽게 구현

Easy to use No-code Recipe UI

Easily implement data pipelines optimized for manufacturing processes with a recipe-based, no-code blockcoding UI.

풍부한 SPC</p>
<p>기본 제공되는 다양한 통계적 공정 관리(SPC) 기법으로 수집된 데이터의 패턴을 분석하고 이상 트렌드를 실시간 감지

Business intelligence enabled by user-friendly dashboard

Analyze patterns in manufacturing data and detect anomalous trends in real time using Data CAMP’s advanced statistical process control (SPC) capabilities.

검증된 안정성 Rust 언어 기반 코어 엔진이 365/24/7 중단 없이 실시간 대용량 데이터 처리

No downtime in 3 years (RUST-based core engine)

The Rust-based core engine processes manufacturing big data in real time with zero downtime, 24/7/365.

	Minimum	Recommended
CPU	Intel Core i5	Intel Core i9
RAM	DDR4 16GByte	DDR4 64GByte
Storage	256GByte SSD	2TByte PCle 4.0×4 M.2 SSD + External Storage / NAS
Network	4Port Gigabit Ethernet	4Port 10Gigabit Ethernet

	Minimum
CPU	Intel Core i5
RAM	DDR4 16GByte
Storage	256GByte SSD
Network	4Port Gigabit Ethernet

	Recommended
CPU	Intel Core i9
RAM	DDR4 64GByte
Storage	2TByte PCle 4.0×4 M.2 SSD + External Storage / NAS
Network	4Port 10Gigabit Ethernet

✓ Pre-Implementation Considerations

- System Requirements: Requirements can vary depending on the volume of data to be processed. The minimum requirements are based on processing 80 MBytes of data per second.
- Data Processing Capacity: The amount of data that can be processed in a certain period of time is influenced by the network environment. Ethernet ports need to be installed considering purpose and accessibility, we will provide an ideal network specification after on-site inspection.
- Storage Space: Storage space is dedicated to the system and database. If large data storage is needed (e.g., for images), additional storage space must be secured separately.
- Performance Improvement: Consider a multi-node configuration to enhance performance. Each node does not need to have identical specifications; prioritize the main node’s specifications and add sub-nodes accordingly.

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