AI & Machine Learning

When Measurement Data Becomes Predictive Intelligence

Every CMM run, every CT scan slice, every Gage R&R data point is a signal. Individually they confirm past quality. Collectively, with the right models, they predict future failure.

Service Metrology Limited combines decades of precision measurement expertise with modern machine learning engineering to build AI systems that are grounded in real manufacturing physics — not generic data science applied blindly to production lines. Our models are trained on metrology-domain data, validated against industry standards, and deployed in a way that your quality and engineering teams can interpret and trust.

Whether you need a computer vision model classifying defects in CT scan output, an LSTM network predicting CMM process drift before it causes scrap, or an NLP pipeline extracting GD&T requirements from legacy drawing PDFs — we architect, train, validate, and deploy end-to-end.

Computer Vision & Automated Defect Classification

A human expert reviewing thousands of CT scan slices or optical scan point clouds is a bottleneck. A trained convolutional neural network does it in milliseconds — consistently, without fatigue.

We build and deploy CNN-based computer vision models trained on your specific part families and defect typologies. The model learns the visual and geometric signatures of porosity, cracks, inclusions, delaminations, and dimensional deviations from labelled scan data — then classifies new scans automatically with confidence scores your quality team can act on.

Alongside classification, we deploy unsupervised anomaly detection models (autoencoders, isolation forests) on continuous CMM and sensor streams — flagging geometric deviations and process shifts the moment they emerge, before a single non-conforming part reaches the next station.

• • CT Scan Defect Classification: CNN models trained on volumetric voxel data identifying porosity, cracks, and inclusions with >90% precision on production-representative datasets.
• • Surface Anomaly Detection: Unsupervised autoencoder models detecting novel surface defects without requiring labelled examples — essential for new part families.
• • Visual Inspection Automation: Deploying trained models on inline camera systems for high-speed cosmetic and dimensional pass/fail screening at line speed.
• • Model Explainability (XAI): Grad-CAM visualisations showing exactly which features the model used to reach its classification — critical for aerospace and medical regulatory sign-off.

Predict Failure Before It Happens

SPC charts tell you when a process has already drifted out of control. ML models tell you it is about to — with enough lead time to act.

We train time-series models — LSTMs, gradient boosted regressors, and ARIMA hybrids — on your historical measurement streams to detect subtle drift signatures weeks and hundreds of batches before a process crosses a control limit. The model's prediction is served in real time alongside your existing SPC infrastructure, requiring no replacement of current systems.

We also build intelligent CMM path optimisation models that analyse thousands of historical inspection cycles to recommend the minimum statistically sufficient sampling density for a given part family — cutting CMM cycle time by 20–40% without increasing measurement uncertainty.

• • Predictive Quality Analytics: Reject-rate prediction models trained on process parameters (tool wear, temperature, spindle load) delivering per-batch quality forecasts before inspection.
• • Intelligent CMM Path Optimisation: Reinforcement learning and Bayesian optimisation models selecting optimal probe paths and sampling densities for minimum cycle time at given uncertainty budgets.
• • Predictive Maintenance (CMM Equipment): Models monitoring probe qualification trends, thermal compensation drift, and axis velocity signatures to predict calibration requirements before performance degrades.
• • Process Parameter Optimisation: ML regression mapping the relationship between upstream CNC parameters and downstream dimensional outcomes — feeding recommended parameter adjustments directly back to the machine.

From Drawing PDF to Inspection Plan in Seconds

Legacy engineering drawings represent decades of institutional knowledge trapped in PDF files. AI can read, interpret, and act on that knowledge at machine speed.

Our AI drawing interpretation pipeline uses a combination of computer vision (for geometric feature extraction) and natural language processing (for GD&T callout parsing) to ingest engineering drawing files and automatically extract every toleranced feature, datum reference, surface finish requirement, and material specification — outputting a structured inspection plan, CMM characteristic list, and PPAP-ready measurement report template.

Combined with AI-accelerated Monte Carlo digital twin simulation, we can predict assembly yield rates across 100,000 virtual builds in the time it would take a human analyst to complete a single worst-case stack-up — identifying which tolerances are genuinely critical vs. which are over-constrained with negligible functional impact.

• • AI Drawing Interpretation: NLP + CV pipeline extracting GD&T callouts, tolerances, datums, and notes from DXF, PDF, and STEP drawing inputs — producing structured JSON/Excel inspection plans.
• • AI-Accelerated Digital Twin Simulation: Monte Carlo simulation enhanced with surrogate ML models delivering 10,000× speed-up over traditional FEA-based tolerance analysis.
• • Intelligent SPC & Reporting: LLM-powered report generation converting raw CMM datasets and Gage R&R results into structured, human-readable PPAP documentation and process improvement recommendations.
• • Measurement Uncertainty AI Modelling: Neural network surrogate models for GUM-compliant combined measurement uncertainty evaluation — incorporating environmental, geometric, and operator sources simultaneously.