Achieve your business goals with
AHHA Labs’ trusted data and
AI technology
Achieve your business goals with AHHA Labs’ trusted data and AI technology
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Success case
All
Quality Control
Predictive Maintenance
AI Inspection with 86% Cost Reduction Using On-Device AI
Challenge Defects caused by external contaminants are unpredictable in shape, making rule-based algorithms ineffective—necessitating a deep learning model...
Transforming Battery Manufacturing with AI Inspection & Data Automation
From Manual Inspections to Smart Factories: The Evolution of Battery ProductionWith the rapid expansion of the electric vehicle (EV) market, the...
A 3-Step AI Inspection Pipeline for Rubber Sheet Defects: Automating Detection, Classification, and Location Analysis
Challenge In the tire manufacturing process, rubber sheets produced during the calendering process lacked an automated quality inspection system and relied...
Boosting Control Efficiency of Battery Assembly Lines by 300% Through a Remote Monitoring System
Challenge The assembly lines for EV batteries are massive and highly complex, requiring multiple personnel to monitor equipment status and respond to alarms....
Detecting Inspection Data Drift Caused by Material Changes: A Case Study on the Data Quality Index (DQI) Model
Challenge In manufacturing, consistently capturing high-quality optical images is essential for training machine learning models and ensuring high...
Predicting Motor Failures with Vibration Sensor Data (Predictive Maintenance)
Challenge Previously, vibration sensors were installed on power plant motors, and inspections were conducted based on predefined rules. However, since...
A successful case of reducing ‘false-positives’ by applying a deep learning model to detect ‘type 1 errors’
Challenge If the test standard is set too sensitively to achieve 0% ‘false-negative’, ‘false-positive (type 1 error)’ increases. Inefficiency due to...
Success case of monitoring ‘data drift’ and performing predictive maintenance with a data quality index (DQI) model
Challenge Consistent optical images must be taken at all times to properly train the model and increase the accuracy of quality control However, if...
Configuring an AI model pipeline to detect robotic grasping anomalies in real time
Challenge Robot drops battery, causing downtime Difficulty utilizing existing machine learning solutions due to 'class imbalance' problem Difficulty...
How the Anomaly Detector model was used to automate the quality control process using only normal data
Challenge Pouch-type batteries have flexible surface geometry, complicating defect identification. This makes it challenging to use existing...
Implementing Quality Control in Multi-Product Small-Batch Production through the Utilization of Data CAMP’s ‘RECIPE’ feature
1. Challenge Managing Varied Quality Standards for Each Model without a Centralized History View The latest trend in manufacturing revolves around the...
Data Integration: Managing Inspection Data by Barcode Number for a Secondary Battery Manufacturer with Data CAMP
1. Challenge Lack of a production history management solution to manage product inspection data by barcode number In 2015, the introduction of the 21700...
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