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Machine Learning Based
System for Classification Using Deviation Parameters

- Situation This Solves

A quality control inspector checks 300 components per shift. They can physically examine perhaps 5% of them. The rest pass through untested. Most are fine. But the ones that aren’t — the subtle deviations from spec that the eye can’t catch — make it to the customer.

Product recalls, warranty claims, safety incidents — many trace back to a defect that was technically detectable, but wasn’t detected because no human can check everything. The gap isn’t attention or effort; it’s scale.

This system uses deviation parameters — the specific ways things go wrong — to train a machine learning model that can classify every unit, every sample, every reading, all the time. Not sampling. Not spot-checking. Continuous classification at the speed of the process.

The Problem

Human quality control scales poorly — and the consequences of what slips through can be severe

Manual inspection is inherently limited: it samples, it tires, it varies between inspectors. Rule-based automated systems catch known defects but miss novel ones. The result is a systematic gap between what quality standards require and what quality processes actually detect — a gap that creates recalls, liability, and safety risk.

The Solution

A machine learning classifier trained on deviation patterns to catch what rules and humans miss

The system builds ML models based on deviation parameters — the specific measurable ways output deviates from specification. Rather than hardcoded rules, it learns what variations matter and how to classify them — enabling automated quality classification that improves over time and catches the subtle, novel, and combination defects that conventional QC misses.

Who This Transforms — And How

Production & Quality Managers

Instead of hoping the 5% sample is representative, quality managers get classification data on 100% of output — with every deviation logged, categorised, and traceable to its source in the production process.
  • From sampling confidence to actual certainty.

Product Safety & Compliance Engineers

Safety-critical components — aerospace, medical, automotive — require proof that every unit meets specification. ML classification provides a continuous, auditable record that is defensible in regulatory and legal contexts.

  • Confidence that every unit is what the spec says it is.

Process Improvement & Operations Teams

Deviation patterns in the classification data reveal where processes are drifting — flagging the machinery, the shifts, and the input batches that are generating the most out-of-spec output, so root causes can be fixed, not just symptoms managed.
  • Fix the process, not just the product.

How It Works

1.

Deviation parameters are defined and the system is trained on historical data

The system identifies the measurable parameters that characterise each type of deviation — dimensional, material, functional — and trains ML models on historical records of conforming and non-conforming output, learning what patterns predict each failure mode.

Like training an expert inspector — but instead of years of experience, it takes a labelled dataset and a training run.

2.

The model classifies new output continuously against learned deviation patterns

In operation, the system applies its trained models to every new data point — sensor readings, measurement outputs, inspection records — classifying each item in real time as conforming, deviating, or requiring human review.

Like a spam filter for your production line — continuously sorting good from bad, and improving with every classification.

3.

Classified results are surfaced with confidence scores and root-cause indicators

Deviations are reported with their classification, their confidence level, and the specific parameters that triggered the flag — giving quality teams the information they need to act, quarantine, investigate, or escalate.

Like a medical diagnosis that doesn’t just say ‘something is wrong’ — it tells you exactly what, why, and how confident it is.

Without This

  • Quality control relies on sampling a fraction of output, missing systematic issues in the rest
  • Rule-based automated systems catch known defects but are blind to novel failure modes
  • Defects that reach customers are expensive — recalls, warranty, liability, and reputational damage
  • Process drift is identified retrospectively, from trends in complaint data rather than production data

With This

  • Every unit is classified by an ML model trained on the actual deviation patterns of that process
  • Novel and subtle defects are caught because the model learns from patterns, not just rules
  • Defect rates, deviation types, and root causes are visible in real time
  • Process drift is flagged immediately — before defective output leaves the line

What Makes This Different — The Protected IP

AI

Machine Learning

Quality Control

Industrial AI

Most ML quality systems are classification models applied to images — visual inspection automation. This system takes a different approach: it works from deviation parameters — the specific measurable ways output deviates from specification — which means it applies across any measurable production process, not just those with visual defects. The innovation patent covers that specific deviation-parameter-based classification methodology.