Material performance testing including improved load detection

Patent metadata

Field	Value
Publication number	US-2026009707-A1
Application number	US-202519329115-A
Country	US
Kind code	A1
Filing date	Sep 15, 2025
Priority date	Jan 6, 2017
Publication date	Jan 8, 2026
Grant date	—

Abstract

A material testing apparatus integrates mechanical testing with cloud-based predictive analytics to forecast long-term material performance. The apparatus comprises an actuator and load head that applies force to material specimens, with multiple load line displacement measuring devices positioned on opposing sides of the specimen to detect positional variations during testing. A controller performs performance tests while detecting displacement variations and transmits test data including position signals and calculated performance metrics to a cloud-based analysis system. The cloud system executes machine learning models trained on aggregated historical test data from multiple testing apparatuses to generate predictive analytics results. These results include predicted service life, failure probability scores, and recommended maintenance schedules for the tested material. The controller receives and stores these predictive analytics, enabling quality control decisions based on both immediate mechanical test results and long-term performance predictions. The system locally stores test data and automatically synchronizes when connectivity is restored.

First claim

What is claimed is:

1 . A material testing apparatus with cloud-based predictive analytics, the apparatus comprising:
an actuator to apply a force; a load head to supply a load to a material specimen, wherein the load head has a moment causing the load head to pivot; two or more load line displacement (LLD) reference points that extend outward from the load head; two or more measuring devices that correspond to the LLD reference points, each positioned to detect a position of the corresponding LLD reference points and to transmit position signals to a controller, wherein the two or more measuring devices are located on opposing or near-opposing sides of the material specimen; a network interface module configured to establish communication with a cloud-based analysis system; wherein the controller is programmed to:
perform a performance test on the material specimen that includes detecting a variation in the position of the two or more measuring devices,
transmit test data including the position signals and calculated performance metrics to the cloud-based analysis system during or after the performance test,
receive predictive analytics results from the cloud-based analysis system, wherein the predictive analytics results are generated by machine learning models trained on aggregated historical test data from multiple material testing apparatuses, and
display or store the predictive analytics results including at least one of: predicted service life, failure probability score, or recommended maintenance schedule for the tested material.

2 . The apparatus of claim 1 , wherein the controller is further programmed to receive updated calibration parameters from the cloud-based analysis system, wherein the updated calibration parameters are optimized using machine learning analysis of measurement accuracy across multiple testing apparatuses.

3 . The apparatus of claim 1 , wherein the machine learning models include a neural network trained to identify correlations between flexibility index patterns and documented pavement failures occurring more than one year after initial testing.

4 . The apparatus of claim 1 , wherein the controller is programmed to operate in an offline mode when network connectivity is unavailable, storing test data locally and automatically synchronizing with the cloud-based analysis system when connectivity is restored.

5 . The apparatus of claim 1 , wherein the cloud-based analysis system implements federated learning, updating global machine learning models using locally computed gradients from individual testing apparatuses without transmitting raw test data.

6 . A method for predictive analysis of material performance using distributed testing and cloud-based machine learning, the method comprising:
performing a performance test on a material specimen using a material testing apparatus having multiple load line displacement measuring devices; transmitting test data from the material testing apparatus to a cloud-based analysis system, the test data including position measurements from the multiple measuring devices, load measurements, and calculated performance metrics; processing the test data through a first machine learning model trained on historical failure data to generate a failure probability score for the material specimen; processing the test data through a second machine learning model trained on field performance data to predict an expected service life for material incorporating the tested specimen; aggregating the test data with historical test data from multiple testing locations to update training datasets for the machine learning models; detecting anomalous test patterns by comparing the test data against learned distributions of normal test results; generating a comprehensive material assessment report including the failure probability score, predicted service life, and any detected anomalies; and transmitting the comprehensive material assessment report back to the material testing apparatus for display or storage.

7 . The method of claim 6 , further comprising:
identifying material specimens from a common production batch by analyzing chemical signature patterns in the test data; tracking performance metrics across all specimens from the identified batch; and generating batch-specific quality alerts when aggregate performance metrics fall below predetermined thresholds.

8 . The method of claim 6 , wherein the machine learning models are continuously retrained using a sliding window approach that weights recent field performance data more heavily than historical data to adapt to evolving material formulations and testing conditions.

9 . The method of claim 6 , further comprising generating real-time quality control alerts when the test data indicates a statistical deviation from expected performance parameters, wherein the expected parameters are dynamically adjusted based on environmental conditions and material specifications.

10 . A cloud-based system for enhancing material testing accuracy through collective intelligence, the system comprising:
a data ingestion module configured to receive test data streams from a plurality of material testing apparatuses, each apparatus having multiple displacement measuring devices; a data validation module implementing anomaly detection algorithms to identify and flag potentially erroneous measurements; a predictive analytics engine executing ensemble machine learning models trained on correlated test and field performance data; a model training pipeline that periodically retrains the machine learning models using accumulated test data and documented field performance outcomes; and a feedback distribution module that transmits updated calibration parameters and testing recommendations to connected material testing apparatuses, wherein the system generates population-level insights about material performance trends that are not detectable from individual test results.

Assignees

• Troxler Electronic Lab Inc

Inventors

• Brovold Shawn

Classifications

• G01N33/42 Primary

  Road-making materials (G01N33/38 takes precedence) · CPC title

• G01N3/20

  by applying steady bending forces (G01N3/26, G01N3/28 take precedence) · CPC title

• G01N2203/0062

  Crack or flaws · CPC title

• G01N3/08 Primary

  by applying steady tensile or compressive forces (G01N3/28 takes precedence) · CPC title