Small manufacturing tolerances can have a disproportionate effect on electric motor performance. INSUS uses existing in-process dimensional measurements to predict end-of-line test risk, helping quality teams identify at-risk units before final assembly and testing. Advisory only.
01
End-of-line test risk predicted earlier.
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Focus on tolerances that actually matter.
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Uses existing manufacturing measurement data.
The problem
Electric motor manufacturing involves many dimensional tolerances across the stator, rotor and assembly, but they do not all influence final performance equally. Parameters such as air gap can matter more than others for torque, ripple and noise. When every tolerance is treated the same, quality attention is spread thin and at-risk units often reach final test before the problem is visible.
The shift
Quality and assembly teams stay responsible for release and rework. What changes is whether in-process tolerances are linked to eventual end-of-line outcomes early enough to investigate.
Today
With INSUS
How it works
This fits OptiStudio as quality prediction and decision support above existing measurement and test systems. Access is read-only. It does not replace end-of-line testing or automatically release or scrap units.
Connect
Connect existing dimensional measurement systems, end-of-line test systems and relevant MES/QMS records. Capture air gap, stator inner diameter, rotor parameters and corresponding motor test results.
Understand
Analyze historical relationships between in-process measurements and final motor performance for the specific production line, motor variant and target specification.
Predict
Predict a unit's likely end-of-line test outcome from tolerance measurements available earlier in the process, including potential risk around torque, torque ripple and noise or vibration criteria.
Advise
Surface at-risk units for quality or assembly review, together with the tolerance contributing to the predicted risk, so the team can investigate earlier than final test.
Verify
Compare predicted outcomes with actual end-of-line test results. Feedback validates and refines the model for the specific production line and motor variants.
Getting started
For electric motor plants that already capture in-process dimensional measurements and end-of-line results. Delivered through OptiStudio as tolerance-to-test decision support. Expert Services can help with data assessment and back-test design. Advisory and read-only. No new measurement hardware when the required data is already captured.
Related for industrial equipment
Tolerance-to-test prediction sits beside EU Ecodesign documentation and bearing vibration test consistency on the same motor and component plants.
Not every dimensional tolerance influences final motor performance equally. This use case predicts end-of-line test risk from in-process measurements so quality teams can investigate at-risk units earlier.
In-process dimensional data such as air gap, stator inner diameter and rotor parameters, plus matching end-of-line test results and motor variant context. No new sensors when those measurements are already captured.
No. End-of-line testing remains the verification system of record. The system provides an earlier prediction and risk signal.
No. It is decision support. It identifies at-risk units and likely driving tolerances so the quality and assembly team can decide further testing, rework or release.
Through OptiStudio as quality prediction and decision support. Expert Services can support data assessment, back-testing and validation with your quality team.
Predicted test outcomes are compared with actual end-of-line test results and refined for the specific production line, product variant and manufacturing conditions.
The model depends on reliable in-process tolerance measurements and sufficient historical end-of-line results. Performance is specific to the motor line and variants used for training; additional lines and variants need individual calibration.
Industrial Equipment · Electric motor manufacturing
Use existing tolerance measurements to identify motor units at risk of end-of-line test failure earlier in the manufacturing process, and focus quality attention on the parameters that actually matter.
