Capturing extrusion quality and machine readings in one governed process.

A Canvas App and Dataverse solution records final-product checks alongside detailed extrusion settings, while Power Automate creates time-banded child readings linked to the production record.

Power Apps CanvasMicrosoft DataversePower AutomateQuality control

Final Extrusion Product Quality Control

MANUFACTURING QUALITYExtrusion process record

Illustrative field grouping

Final productMachine readingsRun window

PROCESSZones + meltDie zones, temperature, pressure

LINESpeed + haul-offTorque, output and downstream settings

QUALITYFinal checksDimensional and product measurements

RELATED ROWSInterval readingsParent-linked machine observations

Canvas App→Dataverse→Power Automate

Final productQuality checks stay beside the production context.

Machine detailSettings are structured as queryable fields.

Interval rowsRelated readings preserve the run window.

Evidence-ledNo unsupported reporting or outcome claims.

The problem and the build

The data structure is the product.

The useful detail is not a generic “operations dashboard”. It is the relationship between a final extrusion record, its process settings, its quality checks and the readings created across a defined interval.

What the app captures

Product qualityFinal extrusion checks and dimensional measurements tied to the production record.
Extrusion processDie zones, melt temperature, back pressure, groove feed bush, speed, torque and output rate.
Downstream settingsHaul-off, line pressure, spray temperatures, ultrasound, vacuum, water pressure and weight-meter context.

Why this matters

A final quality result is more useful when the conditions that produced it remain attached. The app’s Dataverse model turns machine setup into named fields and lets related readings retain a lookup back to the originating production record.

Evidence boundary: the available solution export proves the Canvas App, Dataverse tables and Power Automate flows described here. It does not prove a measured defect-rate, throughput or reporting improvement.

How the automation works

One run window, ordered child records.

The flow is designed around a parent production record and a start/end window rather than a disconnected export.

Submit the run window

The app passes the parent record, start time and end time to a Power Automate flow.

Create related readings

The flow iterates through the interval and creates machine-reading rows with concurrency constrained to one.

Keep the relationship

Each generated row carries the production lookup so operators can move from machine detail back to the originating run.

Public-proof boundary: proprietary formulas, real record values and internal identifiers are intentionally not reproduced.

Talk through your workflow

Need quality data that follows the process?

Tell us what your operators capture today, where the context gets lost and what a useful handover would need to contain. We can help map the record model and the automation boundary.

Discuss a similar project

Share the workflow and we’ll come back with practical next steps.