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arxiv: 2605.09371 · v1 · submitted 2026-05-10 · ⚛️ physics.app-ph

Recognition: no theorem link

From Angle of Repose to Heap Morphology: Full-Field Calibration of DEM for Granular Powders

Olivier Gaboriault , Antonella Succar , Cl\'eo Del\^etre , Anatolie Timercan , Roger Pelletier , David Melancon , Bruno Blais
Authors on Pith no claims yet

Pith reviewed 2026-05-12 01:51 UTC · model grok-4.3

classification ⚛️ physics.app-ph
keywords DEM calibrationgranular powdersheap morphologyangle of reposefull-field analysisadditive manufacturingmetal powdersimage processing
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The pith

Full-field heap morphology provides a more reliable calibration for DEM models of granular powders than angle of repose measurements.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Traditional calibration of discrete element method models for powders relies on matching the angle of repose, but this scalar measure fails to capture the complete shape of a powder heap and often allows multiple parameter sets to fit the data, particularly for cohesive materials. The paper instead advocates comparing the full average profile of experimental heaps with those from simulations by aligning their pixel-wise grayscale intensities. This method is demonstrated using two common metal powders in additive manufacturing. A sympathetic reader would care because accurate DEM parameters are essential for reliable simulations of powder handling and processing in manufacturing applications.

Core claim

The authors claim that averaging repeated experimental heap profiles and comparing them pixel by pixel in grayscale to averaged numerical heap profiles from DEM simulations yields unique and reliable parameter calibrations, overcoming the non-uniqueness problem inherent in angle of repose based methods for cohesive granular powders such as Ti6Al4V and Al6061.

What carries the argument

The pixel-wise grayscale comparison between the average experimental heap profile and the average numerical heap profile, which serves as the objective function for tuning DEM parameters.

If this is right

  • Parameter sets for DEM models of cohesive powders become unique instead of non-unique.
  • Calibration accounts for the entire heap morphology rather than just a single angle value.
  • Simulations of powder behavior in additive manufacturing become more predictive.
  • The approach is validated as applicable to real industrial metal powders.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This method could be applied to other types of granular materials beyond metals.
  • Future work might integrate this full-field approach with additional metrics like particle size distribution for even tighter calibration.
  • Improved calibrations may lead to better predictions of powder flow in industrial equipment.

Load-bearing premise

That matching the average experimental and numerical heap profiles through pixel-wise grayscale comparison alone is sufficient to identify unique DEM parameter sets without needing further validation metrics or error analysis.

What would settle it

Demonstrating that distinct DEM parameter combinations can generate average numerical heap profiles that match the experimental profile equally well in pixel-wise grayscale but produce different results in independent experiments such as shear cell tests or dynamic flow measurements.

Figures

Figures reproduced from arXiv: 2605.09371 by Anatolie Timercan, Antonella Succar, Bruno Blais, Cl\'eo Del\^etre, David Melancon, Olivier Gaboriault, Roger Pelletier.

Figure 1
Figure 1. Figure 1: presents scanning electron microscopy (SEM) images and volume-based particle size distributions (PSD) for each powder. The titanium powder present predominantly spherical particles with few satellites (Figure 1a), with a PSD ranging from 45 to 111 µm for the 5th–95th percentile (D5-D95) (Figure 1b). The aluminium powder consists of spherical particles with some surface texture (Figure 1c) with a PSD rangin… view at source ↗
Figure 2
Figure 2. Figure 2: Experimental methodology flow chart. (a) 16 Binary images are taken for each experimental run perform on the GranuHeap. 30 runs are perform for a total of 480 binary images per preselected powder weight. (b) For every run, one grayscale image is computed by averaging the pixel values of the 16 associated binary images. The grayscale images are then post-process to compute their number of equivalent black p… view at source ↗
Figure 3
Figure 3. Figure 3: Powder weight effect on number of equivalent black pixel. Number of Equivalent Black Pixels (NEBP) as a function of the powder weight used during the GranuHeap experiment for (a) titanium and (b) aluminium powders. The day on which the experiment was performed is identified by blue (Day 1), orange (Day 2), and green (Day 3) markers. The average and standard deviation over the 30 experiments are indicated b… view at source ↗
Figure 4
Figure 4. Figure 4: Angle of Repose (AOR) measurement methods: (a) First method that relies on the ratio between the height of the powder heap and the radius at its base. (b) Second method using a linear regression using the entire slope of one side of the heap. (c) Third method using a linear regression across a fraction of the slope of one side of the heap, half of it in this case. For this specific example, these methods g… view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of the powder heap profiles obtained using each calibration metric for the Ti6Al4V powder. The top profile represents the Average experimental heap profile (AEHP). Each subsequent row corresponds to the profile with the best agreement with a specific calibration metric (SAD, Method 1, Method 2, and Method 3). The left column shows the average numerical heap profile (ANHP) obtained for the associ… view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of the powder heap profiles obtained using each calibration metric for the Al6061 powder. The top profile represents the Average experimental heap profile (AEHP). Each subsequent row corresponds to the profile with the best agreement with a specific calibration metric (SAD, Method 1, Method 2, and Method 3). The left column shows the average numerical heap profile (ANHP) obtained for the associa… view at source ↗
Figure 7
Figure 7. Figure 7: Top 10 propertie sets for each calibration metric for the Ti6Al4V powder. Each color-marker combination corresponds to a different calibration metric, while the star marker indicates the best-performing parameter set for that metric. Distinct clusters highlight that different metrics favor different combinations of DEM parameters. heap angles. This highlights the intrinsic limitations of scalar metrics in … view at source ↗
read the original abstract

The calibration of discrete element method (DEM) models is commonly performed by tuning model parameters to match an experimental measurements, most commonly the angle of repose (AOR). Although widely used, AOR-based calibration metrics do not adequately characterize the full heap morphology, particularly when dealing with cohesive granular materials. As a result, AOR-based calibrations often leads to non-unique parameter sets. In this work, we propose a DEM calibration procedure based on full-field image analysis of static powder heaps rather than scalar AOR measurements. The method compares an average experimental heap profile (AEHP), obtained from repeated GranuHeap experiments, with an average numerical heap profile (ANHP) generated from DEM simulations. This comparison is performed using pixel-wise grayscale intensity values of both average heap profiles. Two metal powders commonly used in additive manufacturing, Ti6Al4V and Al6061, are used to evaluate the proposed methodology. This work highlights the limitations of traditional AOR-based approaches and demonstrates that full-field heap morphology offers a more reliable framework for DEM calibration.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes a DEM calibration procedure for cohesive granular powders that replaces the conventional scalar angle of repose (AOR) metric with a full-field comparison of average experimental heap profiles (AEHP) obtained from repeated GranuHeap experiments against average numerical heap profiles (ANHP) generated by DEM simulations. The comparison is performed via pixel-wise grayscale intensity matching on 2D projections of the heaps. The method is applied to Ti6Al4V and Al6061 metal powders, with the central claim that AOR calibration produces non-unique parameter sets while the full-field morphology approach provides a more reliable and unique calibration framework.

Significance. If the uniqueness and superiority claims are quantitatively substantiated, the work would address a recognized limitation in DEM modeling of cohesive materials relevant to additive manufacturing, offering a more comprehensive calibration target that incorporates full heap shape rather than a single scalar value. The use of repeated experiments to form averaged profiles is a constructive step toward reproducibility.

major comments (3)
  1. [Abstract and §3] Abstract and §3 (method description): The assertion that AOR-based calibrations 'often leads to non-unique parameter sets' is stated without any quantitative demonstration, such as explicit examples of multiple parameter combinations that match AOR within experimental tolerance but produce visibly different heap morphologies.
  2. [§4] §4 (results and comparison): No quantitative error metrics (e.g., RMSE, normalized pixel difference, or correlation coefficient) or parameter-identifiability analysis are reported for the pixel-wise grayscale matching; without these, it is not possible to verify that the full-field metric actually resolves non-uniqueness or yields statistically distinguishable parameter sets.
  3. [§2.2 and §4.1] §2.2 and §4.1: The assumption that 2D projected average profiles sufficiently capture 3D morphological differences (including azimuthal variation and internal structure) is not tested against multi-view imaging or 3D reconstruction; this leaves open the possibility that distinct 3D configurations produce indistinguishable 2D grayscale fields.
minor comments (2)
  1. [Method] Clarify the precise procedure for generating AEHP and ANHP, including number of repetitions, image alignment/registration method, and any thresholding applied to grayscale intensities.
  2. [Figures] Add scale bars, quantitative difference maps, and legend values to all heap-profile figures to allow direct assessment of the grayscale comparison quality.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed review. We address each major comment below and have revised the manuscript to incorporate quantitative demonstrations, error metrics, and expanded discussion of limitations where appropriate.

read point-by-point responses

  1. Referee: [Abstract and §3] Abstract and §3 (method description): The assertion that AOR-based calibrations 'often leads to non-unique parameter sets' is stated without any quantitative demonstration, such as explicit examples of multiple parameter combinations that match AOR within experimental tolerance but produce visibly different heap morphologies.

    Authors: We agree that the claim would be strengthened by a concrete quantitative example. In the revised manuscript, we have added to §3 an illustrative case using the Ti6Al4V powder parameters: two distinct sets (differing in cohesion energy density and rolling friction) that produce AOR values within 1.5° (within experimental tolerance) but yield visibly different heap shapes when their full-field profiles are compared. This example is supported by the corresponding ANHP images and intensity maps. revision: yes

  2. Referee: [§4] §4 (results and comparison): No quantitative error metrics (e.g., RMSE, normalized pixel difference, or correlation coefficient) or parameter-identifiability analysis are reported for the pixel-wise grayscale matching; without these, it is not possible to verify that the full-field metric actually resolves non-uniqueness or yields statistically distinguishable parameter sets.

    Authors: We acknowledge this gap. The revised §4 now reports RMSE and Pearson correlation coefficient values for the pixel-wise grayscale intensity differences between AEHP and ANHP for both powders. We have also included a parameter-identifiability analysis showing that small perturbations around the calibrated values produce statistically larger changes in the full-field metric than in AOR, with tabulated sensitivity results. revision: yes

  3. Referee: [§2.2 and §4.1] §2.2 and §4.1: The assumption that 2D projected average profiles sufficiently capture 3D morphological differences (including azimuthal variation and internal structure) is not tested against multi-view imaging or 3D reconstruction; this leaves open the possibility that distinct 3D configurations produce indistinguishable 2D grayscale fields.

    Authors: The GranuHeap instrument used here records 2D projections, consistent with standard powder characterization practice. While we have not performed multi-view or 3D reconstructions, the averaged 2D profiles already encode more morphological information (slope distribution, surface texture, and overall shape) than the scalar AOR. We have expanded the text in §2.2 and §4.1 to explicitly acknowledge this as a limitation of the current 2D setup and to note that extensions to multi-view imaging remain future work. We maintain that the 2D full-field approach still provides a more robust calibration target than AOR for the intended application. revision: partial

Circularity Check

0 steps flagged

No load-bearing circularity; empirical heap-profile comparison stands independently of inputs

full rationale

The paper proposes an empirical calibration method that compares average experimental heap profiles (AEHP) with average numerical heap profiles (ANHP) via pixel-wise grayscale intensities, then asserts this yields more reliable DEM parameters than scalar AOR. No derivation chain, equation, or self-citation reduces the central claim to a tautology or fitted input by construction. The non-uniqueness critique of AOR is presented as a known limitation supported by external literature rather than a self-referential definition. The full-field metric's superiority is demonstrated through direct experimental-simulation comparison on Ti6Al4V and Al6061 powders, without renaming known results or smuggling ansatzes via prior self-citations. Minor self-citation is possible in the methods but is not load-bearing for the uniqueness or reliability claim. This is the normal non-circular outcome for an applied calibration study.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; the approach appears to build on standard DEM contact models and basic image averaging without introducing new postulated quantities.

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