See the defect behind the "OK"
From Greek Alḗtheia, "unconcealment" —— bringing the hidden truth to light
Aletha surfaces the False OK that rule-based systems miss — false tightening, thread stripping and cross-threading that pass the torque-angle window yet deviate in curve shape — and turns those findings into concrete production-operations improvements.
The controller only checks the end-point torque-angle window. Inside that window, false tightening, thread stripping and cross-threading land within spec and pass as OK — yet the shape of the whole torque-angle curve has already gone wrong. This False OK is an invisible process-quality risk.
A fixed window sees
only the end point
Aletha reads the whole curve
That is how it catches the "looks fine, actually bad" False OK from the curve shape — exactly what Aletha does.
Amber = suspect (fluctuation / stick-slip), red = NOK; all three head for the Final target.
A normal tightening has three clear stages — rundown → snug → elastic region. When the sequence is wrong it stalls before the elastic region, with repeated false snugging, taking several times the rotation to reach target torque — the final torque still passes, yet Aletha catches it from the shape. Curves are illustrative, axes are relative.
It turns every False OK into a structured, traceable result pinned to the exact process instance — an event + root-cause label + evidence curve. How much value that unlocks depends on the customer’s own management.
In practice with customers
Confirmed anomalies trigger quarantine / re-check; high-frequency anomaly points become operator-training material to correct technique.
Root causes feed process-parameter changes (tightening strategy / PSET, mating face, error-proofing) to eliminate the issue at source.
Anomalies are attributed by station / shift / team and folded into quality KPIs, making accountability measurable.
Anomaly patterns flow back to process / product design, driving improvements in structure, fastening scheme and target torque.
Anomalies tied to a fastener / incoming-material batch → feed back to the supplier and lock down the batch.
Real normal curves maintain the “standard-parameter library”, curbing mis-edits and parameter drift.
Detection pinned to the process instance + logged events shrink the recall scope to the minimum when issues arise.
These are only the validated starting points. The structured results the engine produces are open-ended — we look forward to operationally mature customers who turn data into action incubating more of their own use cases on Aletha.
There are data / quality tools from tightening-tool vendors, and domestic platforms too. Each has its strengths — but no single one holds all four below.
Ingests curves from any brand of tightening system / MES — one platform across every tightening tool in the plant.
Across six dimensions — plant × shop × line × model × equipment × process — every process instance gets its own model → a tighter normal baseline → extremely low false-positive and miss rates.
No labeling, no rule-writing — point it at historical normal curves and it builds models automatically; new models / process upgrades are covered automatically, no manual reconfiguration.
Detection results + expert annotations accumulate in an ontology knowledge base; the on-prem Agent does more than detect — it offers causes and recommendations (diagnosis).
Aletha connects directly to the tightening data you already have — no new hardware, no line changes, no downtime. After a one-time data integration, modeling, calibration, detection and event logging run fully automatically.
We connect your existing tightening data / platform
Take existing normal curves
One model per instance
Shadow-mode trial run
Anomalies auto-logged, fed back to the knowledge base
| Existing on-site system | What it manages on the line | Where it stops | What Aletha adds on top |
|---|---|---|---|
| Tightening controller | Real-time OK / NOK | Only checks the torque-angle window; in-window shape anomalies slip through | In-window False OK |
| Tightening data platform / quality software | Curve capture, SPC / reports | Sees statistics and trends; locked to its own tools, granularity up to 3-D | Vendor-neutral intake, per-instance detection in 6-D |
| SPC / control charts | Process distribution and drift | Doesn’t read single-curve shape; needs manual rules | Single-curve, rule-free shape detection |
| MES / quality traceability | Record, trace, report | Records data; doesn’t detect anomalies itself | Logs detection as events, feeds back to traceability |
One line, a few critical safety tightening points.
Run detection on your own historical data.
No downtime; close the loop in a few weeks.