Industrial AI Doesn’t Need a Moonshot. It Needs a Measurement.
Most industrial AI pilots stall for reasons that have nothing to do with the model. Where AI pays first in manufacturing, and how to get it onto the floor.
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Industrial AI articles
Where AI and machine learning work in manufacturing and the lab: vision AI, training data, and deployment on real equipment.
articles in this topic, written by Sciotex engineers
10topics, each with an in-depth guide
1997Sciotex has been engineering these systems since 1997
Physical AI: What It Means for Factory Automation
Physical AI explained: how AI that senses, decides and acts through machines works, how it differs from industrial AI, and what works in factories today.
How Much Training Data Does AI Visual Inspection Need?
How many images AI visual inspection needs, what drives the number, a resolution check, labeling standards, and what to do when defects are rare.
AI in Automated Test Equipment: Outliers, Adaptive Test and Faster Debug
How AI is used in automated test equipment: outlier screening, station drift monitoring, test time reduction, failure analysis and LLMs for test code.
AI Predictive Maintenance with DAQ: Sensors, Sampling and Features
AI predictive maintenance explained: which sensors to use, vibration sample rates, the features models need, anomaly vs RUL models, and how to run a pilot.
AI Anomaly Detection in Manufacturing: Finding Defects from Good Parts Only
How AI anomaly detection finds defects and machine faults by learning normal from good parts or healthy signals, how to set thresholds, and its limits.
Deep Learning vs Rule-Based Machine Vision: When Is AI Worth It?
Deep learning vs rule-based machine vision compared: how each works, when AI is worth it, a decision checklist and the hybrid designs most lines use.
VLM Token Usage: How Image Tokens Are Counted vs LLM Text Tokens
How VLMs count image tokens vs LLM text tokens: patch and tile formulas, worked examples, what image tokens cost at line rate and how to cut them.
VLM vs LLM: What’s the Difference, and Can a VLM Inspect Parts?
VLM vs LLM explained: how vision language models add image input to an LLM, a side-by-side comparison, and whether a VLM can inspect parts.
Industrial AI: How AI Is Used in Manufacturing
Industrial AI explained: how AI is used in manufacturing for inspection, counting, test and sensor data, which models fit, edge vs cloud, VLMs and ROI.
Deep Learning for Machine Vision: CNNs, Vision Transformers, GANs and 3D Networks
Which deep learning architectures fit industrial machine vision: CNNs vs vision transformers, detection, segmentation, anomaly detection, GANs and 3D CNNs.
How to Use AI in Manufacturing: A Step-by-Step Implementation Roadmap
How to use AI in manufacturing, step by step: choosing the first use case, acceptance criteria, data checks, proof of concept, integration and validation.
AI Microscopy Image Analysis: Deep Learning for Biomedical Imaging
How AI microscopy image analysis works: denoising, cell segmentation, tracking and 3D analysis for biomedical imaging, its pitfalls and pipeline design.
AI Defect Detection: How to Detect and Classify Surface Defects with Machine Learning
AI defect detection explained: surface defect types and lighting, classification vs segmentation vs anomaly models, reject limits and performance metrics.
How to Use AI for Quality Control
Using AI for Quality Control helps assure quality with better and faster defect detection leveraging AI and machine vision.
Edge AI Hardware for Machine Vision: GPU vs CPU vs FPGA vs NPU
GPU vs CPU vs FPGA vs NPU for AI and traditional machine vision: strengths, a sizing example, quantization, local VLMs and industrial hardware factors.
A Brief History of AI in Vision Systems: From Perceptrons to Foundation Models
The history of AI in vision systems, from the perceptron and rule-based machine vision to CNNs, AlexNet, vision transformers and foundation models.
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