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Customer Accuracy Issue: Recalibration With Customer Parts

Use this SOP when the customer reports accuracy problems after onboarding. CSM collects exemplary customer parts and scope first. CS Ops then runs a new calibration and validation using the customer parts. CS Ops never contacts the customer.

When to use this

Use this module when:

  • The customer has started using the setup

  • The customer reports accuracy problems that require recalibration

  • CSM confirms scope and provides a complete handoff package to CS Ops

Do not use this module for a standard validation FAIL during onboarding. Use: Validation FAIL: Escalation And Handover To CSM.


Preconditions from CSM

CS Ops must not start until CSM provides:

  • Confirmation the issue is accepted as a recalibration task (scope agreed)

  • Customer dataset folder link with exemplary CAD parts

  • Customer slicer project file, or confirmation the existing project file is still valid

  • Definition of what “accuracy” means here (Cost WMAPE on total cost, not material consumption)

  • Target acceptance criteria for this task if different from the standard 10% Cost WMAPE rule

  • Customer constraints that must not be violated:

    • Orientation rules

    • Supports locked or not

    • Parameter locks


Inputs

You need:

  • CSM handoff package

  • Existing onboarding evidence pack (machine, material, process chain)

  • Customer CAD parts dataset and any customer validation set

  • Slicer project file with preset saved inside


Outputs

You will produce:

  • New calibration results based on customer parts

  • New validation results based on customer parts

  • Documented PASS or FAIL against the agreed acceptance target

  • Updated evidence pack for CSM review with dataset identifiers and traceability

  • A note stating what changed versus the original onboarding setup


Rules

Apply these rules:

  • CS Ops never contacts the customer. All questions go to CSM

  • Keep customer datasets separate from standard datasets

  • Record dataset identifiers and versions in CS_Internal/Evidence/Onboarding_Execution_Note.docx

  • Do not export presets. Preset stays inside the slicer project file


Procedure

Do this:

  • Create a dedicated CS_Internal folder for this correction work that is separate from the original onboarding run

  • Verify setup consistency:

    • Confirm 3D Spark machine, material, and process chain match the customer production intent

    • Confirm the slicer project opens and contains the intended customer preset

    • If unclear, stop and ask CSM

  • Prepare datasets:

    • Copy customer CAD parts into CS_Internal under a clearly named dataset folder

    • Record dataset identifier and source provided by CSM

    • If a customer validation set exists, keep it separate from the customer calibration set

  • Generate plates and G-code using the customer preset and agreed constraints

  • Run extractor on the G-code outputs

  • Run calibration in 3D Spark using the customer calibration set

  • Run validation in 3D Spark using the customer validation set

  • Document result and changes in a completion note:

    • Customer issue summary from CSM

    • Datasets used and identifiers

    • Preset name used

    • Cost WMAPE value and PASS or FAIL against agreed target

    • What changed versus original onboarding

    • Statement: “CS Ops did not contact the customer. All inputs were provided via CSM.”

  • Hand back to CSM:

    • Evidence folder link

    • One paragraph summary of what was done and the result

Use these existing modules for execution details:


Stop and escalate to CSM

Stop and escalate if:

  • Customer dataset is incomplete or unclear

  • Constraints are missing or contradictory

  • Acceptance target is undefined

  • Results are still unacceptable and further work would require new scope


Common mistakes

Avoid this:

  • Starting without a complete CSM handoff

  • Mixing customer datasets with standard datasets

  • Changing slicer parameters outside the agreed preset

  • Reporting results without dataset identifiers and acceptance target