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Error Aggregation in the Reengineering Process from 3D Scanning to Printing.

Jennifer G Michaeli1, Matthew C DeGroff1, Roman C Roxas1

  • 1Old Dominion University, Norfolk, VA 23529, USA.

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|December 19, 2017
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Summary

This study examines dimensional errors in 3D scanning and printing for reengineering without original designs. It analyzes error aggregation in caliper-based and 3D scanner methods for a spur gear.

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Area of Science:

  • Mechanical Engineering
  • Manufacturing Technology
  • Metrology

Background:

  • Reverse engineering processes often lack original design data, necessitating alternative methods for component replication.
  • Dimensional inaccuracies can accumulate throughout the reengineering workflow, impacting final product fidelity.
  • 3D scanning and 3D printing offer potential solutions for reengineering but require careful error management.

Purpose of the Study:

  • To investigate the aggregation of dimensional errors in reengineering processes that utilize 3D scanning and 3D printing without initial design drawings.
  • To compare two distinct approaches for reengineering a 57-tooth spur gear: one based on caliper measurements and another using 3D scanner data.
  • To analyze the flow of geometric data from acquisition to model construction and identify critical stages for error propagation.

Main Methods:

  • Developing a reengineering workflow for a 57-tooth spur gear using two distinct data acquisition methods: manual caliper measurements and 3D laser scanning.
  • Quantifying dimensional errors at each stage of the reengineering process, including data capture, data editing, and 3D model creation.
  • Tracing the propagation of geometric errors through the data flow from initial measurement to final model construction.

Main Results:

  • Both caliper-based and 3D scanning approaches exhibit dimensional error aggregation during the reengineering process.
  • The 3D scanning method demonstrated a different error profile compared to the caliper-based approach, influenced by data acquisition and processing steps.
  • Specific stages in the geometry data flow, such as data editing and model construction, were identified as significant contributors to cumulative errors.

Conclusions:

  • Effective error estimation and alleviation strategies are crucial for successful reengineering using 3D scanning and printing without original designs.
  • Understanding the unique error characteristics of different data acquisition methods is essential for mitigating inaccuracies.
  • Recommendations are provided for improving the reliability and precision of reengineered components through careful management of dimensional errors.