Standards Development

Scope

This document specifies the assessment methods of 3D scanned data for 3D printing model for the accuracy and precision in the total 3D printing life cycle.

This document focus mainly on 3D scanned data from computed tomography. Computed tomography can acquire the information of internal structures, regional density, orientation and/or alignment of scanning objects as well as shape and appearance.

This document is not intended to evaluate the 3D printed product itself. 

Purpose

3D scanning is the process of scanning a real-world object or environment to collect data on its shape and possibly its style attributes. The main purpose of 3D scanning is for generating high-precision digital 3D models.

A 3D scanner can be based on many different technologies, each with its own purposes and targets, limitations, and advantages. There could be many limitations in each type of target object that will be digitized. For example, optical technology may encounter many difficulties with dark, shiny, reflective, or transparent objects. Another example, as using computed tomography scanning, structured-light 3D scanners, and LiDAR technology, there is a need to use non-destructive internal scanning technology for generating digital 3D models. 

Despite the rapid growth of 3D scanning applications, the accuracy, precision, and reproducibility of generated 3D models from 3D scanned data have not been thoroughly investigated. Especially if 3D scanned data are used for 3D printing it’s accuracy and precision are critical. Inaccuracies are due to errors that occur during the imaging, segmentation, postprocessing, and 3D printing steps. The total accuracy, precision, and reproducibility of 3D printed models are affected by the sum of errors introduced in each step involved in the creation of the 3D models. 

For the spreading of 3D printing applications, it is necessary to review and evaluate the various factors in each step of the 3D model printing process that contribute to 3D model inaccuracy, including the intrinsic limitations of each printing technology.

• In this context evaluation of the overall process of data processing will be critical.

• For minimization of cumulative errors of 3D printing life cycles using 3D scanned data the initial error should be assessed and corrected.

• The assessment methods of 3D scanned data for 3D printing are essential

There are many algorithms for 3D scanned data such as semi-automatic segmentation, deformable model based segmentation, and Convolutional Neural Network based segmentation. There are several well known errors during image-based modelling of Region of Interest (ROI) , which are over segmentation, under segmentation, outlier, inaccurate contour, and malalignment. Even though there are more than twenty metrics for evaluating 3D image segmentation there is no consistent definition of metrics and suitable combination of assessment metrics for 3D printing.

Segmentation assessment is the task of comparing two segmentations by measuring the distance or similarity between them, where one is the segmentation to be assessed and the other is the corresponding ground truth segmentation.

There are three major requirements (accuracy, precision, and efficiency) of assessment for 3D scanned data. The accuracy is the degree to which the segmentation results agree with the ground truth segmentation. The precision is a measure of repeatability, and the efficiency which is mostly related with time.

Here we are going to propose the assessment methods of 3D scanned data on how to assess qualityenhancing and error minimizing of 3D printing model.