Friction Parameters Research Articles

Investigation of the Process Parameters in Rotary Friction Welded Dissimilar AA7075/AA5083 Aluminum Alloy Joints on Fatigue Initiation using FEA and ANN

The rotary friction welding (RFW) method is one of the most widespread methods in the world for producing bimetallic components that require high mechanical strength. Simulations play a vital role in improving energy efficiency and reducing environmental impact, aligning with the sustainability goals of modern industry. A neural network (NN)-based incremental learning system was developed to predict crack growth and fatigue for AA5083 and AA7075 aluminum alloys. The results indicate the ability of this method to accommodate the input temperatures and the S-N curve and provide reliable predictions of expected fatigue. This method can reduce labor costs and time spent on crack propagation tests, enhancing the effectiveness of production processes and reducing process costs. This work also reveals the ability of neural . It maynetworks (NN) in monotonic function extrapolation like the S-N curve, which may pave the way for a wide variety of monotonic function-predicting problems. In future studies, a neural network (NN)-based increment learning scheme could be trained with random parts of individual S–N curves and applied to predict the rest. Additionally, the verification utilizing AISI 2205 and AISI 1020 steel has observed that neural networks may obtain S-N curve values for another metal with less than an 8% error rate. Friction pressure increases temperature, deformation, and stress in welding processes. Friction pressure 17 MPa increases temperature to 355 degrees Celsius, while Friction pressure 23 MPa increases deformation to 0.020 mm. A friction pressure of 29 MPa increases equivalent stress to 110 MPa. The indication of the S-N curve shows that increasing welding pressure increases Alternating Stress. Friction pressure also increases life, with minimum life cycles reaching 171040 cycles at 17 MPa, 195560 cycles at 23 MPa, and 283690 cycles at 29 MPa. Comparing research and simulation results, convergence is less than 8%, reducing error.

A new calculation method for the response of active-passive bridge piles considering pile-soil interface friction

The pile foundation of bridges under unregulated surcharge loads experiences a complex force pattern, which is subject to active loads and passive loads. In this paper, a more comprehensive calculation method for the response of active-passive piles was proposed, which considered the effect of pile-soil interface friction and soil stress states on the passive loads. First, the Ito theory was improved by adopting the adhesion c a and external friction angle φ a to characterize the pile-soil interface friction. Then, the soil around the pile in elastic, elastoplastic, and plastic flow states was considered to calculate the variation of the passive load. Finally, a nonlinear p-y curve was introduced to establish the calculation model of active-passive piles, and the finite difference program CPAPP was prepared for the solution. The results show that the pile response calculated by CPAPP is consistent with the field measurement, indicating that the proposed calculation method has high accuracy. When the soil is in an elastic state, the pile-soil interface friction does not affect the passive load. When the soil is in a plastic state, the passive load is closely related to soil strength parameters and pile-soil interface friction parameters.

Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experiments

Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as a proxy for friction state variable. This PINN framework allows learning RSF parameters from stick-slip experiments rather than measuring them through a series of velocity step experiments. We observe that when the stick-slip cycles are irregular, the PINN models outperform the data-driven DL models. Transfer learning (TL) PINN models are also developed by pre-training on data collected at one normal stress level followed by forecasting shear failures and retrieving RSF parameters at other stress levels (i.e., with different recurrence intervals) after retraining on a limited amount of new data. Our findings suggest that TL models perform better compared to standalone models. Both standalone and TL PINN-estimated RSF parameters and their ground truth values show excellent agreements thus demonstrating that RSF parameters can be retrieved from laboratory stick-slip experiments using the corresponding acoustic data and that the transmitted wave amplitude provides a good representation of the evolving frictional state during stick-slips.

Effects of Arrhenius activation energy and melting heat transfer on Carreau fluid through a stretching sheet

ABSTRACT This article investigates the effect of activation energy on Carreau fluid flow in the presence of variable thermal conductivity, inclined magnetic field, melting heat transfer, and Soret and Dufour phenomena. A set of suitable similarity transformations is applied for transforming the governing partial differential equations (PDEs) considered for the physical phenomena into non-linear ordinary differential equations (ODEs). We obtained the results by solving the non-linear ODEs numerically which describe the behavior of the velocity, temperature, and concentration profiles of the fluid against the governing parameters and illustrated these graphically. The velocity distribution decreases for angle of inclination while it is increasing against higher melting parameter. The temperature distribution enhances with higher modified Dufour parameter and Prandtl number while it declines for thermal conductivity, activation energy, and melting parameters. The concentration distribution increases for melting parameter, Soret number, and activation energy parameter while decreasing for temperature difference parameter, Schmidt number, and reaction rate parameter. Skin friction, Nusselt and Sherwood numbers are evaluated numerically against the various governing parameters. Accuracy of the present work is illustrated and established through comparison carried out for skin friction versus magnetic parameter with existing results in the literature.

Glide-snow avalanches: a mechanical, threshold-based release area model

Abstract. Glide-snow avalanches release at the ground–snow interface due to a loss in basal friction. They pose a threat to infrastructure because of the combination of unreliable mitigation measures, limited forecasting capabilities, and a lack of understanding of the release process. The aim of this study was to investigate the influence of spatial variability in basal friction and snowpack properties on the avalanche release area distribution and the release location. We developed a pseudo-3D, mechanical, threshold-based model that consists of many interacting snow columns on a uniform slope. Parameterizations in the model are based on our current understanding of glide-snow avalanche release. The model can reproduce the power law glide-snow avalanche release area distribution as observed on Dorfberg (Davos, Switzerland). A sensitivity analysis of the input parameters showed that the avalanche release area distribution was mostly influenced by the homogeneity (correlation length and variance) of the basal friction and whether the basal friction was reduced suddenly or in small increments. Larger release areas were modeled for a sudden decrease and a more homogeneous basal friction. The spatial variability of the snowpack parameters had little influence on the release area distribution. Extending the model to a real-world slope showed that the modeled location of avalanche releases qualitatively matched the observed locations. The model can help narrow down the length scales and timescales for field investigations. Simultaneously, it can grow in complexity with our increasing understanding of glide-snow avalanche release processes. Input parameters such as the basal friction or snowpack parameters could potentially all be connected to the liquid water content. This would allow for the use of meteorological measurements to drive the model. The model has the potential to help identify potentially dangerous conditions for large or numerous avalanches which would help improve glide-snow avalanche forecasting.

Transverse squeeze flow of thermoplastic composite tape during in-situ consolidation via automated fiber placement

Transverse squeezing of thermoplastic composite tapes during automated fiber placement is a challenge in controlling gaps/overlaps of adjacent bands. A theoretical model may provide insights on direct effect of process parameters on deformation of tape. The developed models in this work evaluate non-Newtonian squeeze flow of molten tape using power-law viscosity under three different no slip, perfect slip, and imperfect slip boundary conditions at interface during in-situ consolidation, aiming to predict the final width of tape with minimal computational costs. The results predicted by models are verified using finite element analysis with close agreement. Subsequently, no slip and perfect slip assumptions underestimated and overestimated the experimental measurements of consolidated widths, respectively. However, the squeeze model with imperfect slip condition may effectively capture the trends in the experimental data. This model includes the effect of intimate contact development on the friction parameter during squeezing, utilizing a new non-Newtonian trapezoidal asperity model.

Reduction in wave shoaling over a linear transition bottom using a porous medium

Wave shoaling, which involves an increase in wave amplitude due to changes in water depth, can damage shorelines. To mitigate this damage, we propose using porous structures such as mangrove forests. In this study, we use a mathematical model to examine how mangroves, acting as a porous breakwater, can reduce wave shoaling amplitude. Shallow water equations are used as the governing equations and are modified to account for the presence of porous media. To measure the wave reduction generated by the porous media, the wave transmission coefficient is estimated using analytical and numerical approaches. The separation of variables method and the staggered finite volume method are utilized for each approach, respectively. The numerical results are then validated against the previously obtained analytical solutions. We then vary the friction and porosity parameters, influenced by the presence and extent of porous media, to evaluate their effectiveness in reducing wave shoaling.

A disrupted learning mechanism in standard quantum systems followed by their self-organizing process

Recently, the fusion between quantum mechanics and machine learning has gained much attention, where classical machine learning algorithms are adapted for quantum computers to significantly amplify data analysis by leveraging the unique effects of quantum reality. In this short paper, by focusing on the quantum trajectories of particles, we find that under general requirements, quantum systems follow a disrupted version of the gradient descent model, a basic machine learning algorithm, where the learning is distorted due to the self-organizing process of the quantum system. Such a learning process is possible only when we assume dissipation, i.e., that the quantum system is open. The friction parameter determines the nonlinearity of the quantum system. We then provide an empirical demonstration of the proposed model.

МОДЕЛИРОВАНИЕ ПУСКА ДВС АВТОМОБИЛЯ С БЕССТУПЕНЧАТОЙ КОРОБКОЙ ПЕРЕДАЧ

The aim of the work is to build a computer dynamic model of moving internal combustion engine parts and a continu-ously variable transmission to determine the starting characteristics of the internal combustion engine. The paper objective is to define the characteristics of moving parts of the internal combustion engine and continuously variable transmission when starting the internal combustion engine with an electric starter. The research method is computer modelling of moving internal combustion engine and transmission parts when starting with an electric starter. The novelty of the work lies in building a computer model of moving parts of an internal combustion engine and a contin-uously variable transmission, connected by a torsional vibration damper, considering changes in friction parameters. The results are in developing a computer model to determine the starting characteristics of a four-cylinder in-line four-stroke internal combustion engine with spark ignition and a continuously variable multitronic® 01J transmis-sion. To check the computer model adequacy, the author uses the values of the average friction pressure in accord-ance with GOST R 54120-2010 and empirical data on the unevenness of the angular velocity of the crankshaft. Simu-lating a four-stage start of an internal combustion engine shows that opening the cylinder valves at the 1st stage in-creases the angular velocity of the crankshaft by 4%. Temporary switching on the reverse clutch for 1.4 s reduces the amplitude of angular oscillations of the solar shaft by 2.22 times, eliminates the beating of the contacting gearbox teeth, but slows down the crankshaft speed by 7.6%. After the reverse clutch is turned off, periodic changes in the an-gular velocity of gearbox parts occur.

Computational influences of convection micropolar fluid influx and permeability on characteristics of heating rate and skin friction over vertical plate

This manuscript merits at examining of an embedded orthogonal plate inside porous domain exposed to uniform heat influx to elaborate on how micro polar fluid factors and permeability characteristic affect the local skin friction, heating rate, and angular velocity inside boundary layer. The plate is subjected to forced convective micro polar fluid influx under steady, incompressible, and viscous circumstances. To facilitate dependable numerical solution, similarity approach is implemented to mutate set of coupled governing equations relevant to the adopted study into a constrained dimensionless differential equations. Computational analysis has been executed hiring Runge-Kutta scheme by Matlab function bvp4c to settle the governing equations. Study's results are highlighted graphically the impact of micro polar fluid factors on the local skin friction, heating rate, and angular velocity curves. High degree of acceptability of present findings compare with prior research results. It is found that the rising of Darcy parameter drives to decrease linearly both heating rate and local skin friction. Among the examined factors, the benchmark parameter of decreasing skin friction is the porosity. Additionally, it is found that an increasing of Prandtl number and micro rotation element lead to enhance Nusselt number. Once curves of micro rotation are interfered at certain distance from the plate due to increase in porosity, Darcy, Forchheimer's, and microelement rotation, the micro rotation curves are inverted.

Adjoint-based inversion for stress and frictional parameters in earthquake modeling

We present an adjoint-based optimization method to invert for stress and frictional parameters used in earthquake modeling. The forward problem is linear elastodynamics with nonlinear rate-and-state frictional faults. The misfit functional quantifies the difference between simulated and measured particle displacements or velocities at receiver locations. The misfit may include windowing or filtering operators. We derive the corresponding adjoint problem, which is linear elasticity with linearized rate-and-state friction and, for forward problems involving fault normal stress changes, nonzero fault opening, with time-dependent coefficients derived from the forward solution. The gradient of the misfit is efficiently computed by convolving forward and adjoint variables on the fault. The method thus extends the framework of full-waveform inversion to include frictional faults with rate-and-state friction. In addition, we present a space-time dual-consistent discretization of a dynamic rupture problem with a rough fault in antiplane shear, using high-order accurate summation-by-parts finite differences in combination with explicit Runge–Kutta time integration. The dual consistency of the discretization ensures that the discrete adjoint-based gradient is the exact gradient of the discrete misfit functional as well as a consistent approximation of the continuous gradient. Our theoretical results are corroborated by inversions with synthetic data. We anticipate that adjoint-based inversion of seismic and/or geodetic data will be a powerful tool for studying earthquake source processes; it can also be used to interpret laboratory friction experiments.

Investigating the influence of geometry on the performance of a wave-powered marine observation buoy: The wave-to-wire approach

As an important marine monitoring platform, marine observation buoys have been widely used in marine environmental monitoring, marine resource surveys, etc., attracting great attention worldwide. However, the lack of a reliable power supply is a crucial problem that seriously restricts buoys’ operation lifetime and carrying capacity. The floating buoy with an inbuilt wave energy harvester is a promising solution to the long-term and sustainable energy supply problem. This study develops a fully coupled wave-to-wire (W2W) mathematical model where the relevant friction parameters and power-take-off system parameters are identified from flume experimental results. Besides, the influence of geometry on the buoy’s electric power performance is investigated under regular and irregular waves, respectively. Numerical results imply that the cylinder-cone-shape buoy produces the most electric power when the three shapes have the same geometric sizes, while the cylinder-sphere-shape buoy performs the best W2W efficiency among the three shapes with the same mass. Moreover, a high W2W efficiency of up to 23.13% is gained under a regular wave. Finally, according to the similarity theory, the averaged electric power of a full-size buoy is estimated at 4.84kW in realistic sea states.

የቦሎቄ (Phaseolus vulgaris L.)የምህንድስና ባህሪያት ለመውቂያ ማሽን ዲዛይን በአካላዊ እናፍራክሽናል መለኪያዎች አንፃር

When designing appropriate machinery systems, equipment, and infrastructures for interacting with, cultivating, gathering, and agriculture-related processing, it is required to have an understanding of the engineering characteristics of agricultural products. This unpredictability makes it difficult to design or develop machines that can efficiently and effectively manage a wide range of product characteristics. Experimental analysis was used to accomplish the study's objective, which was to investigate the implications of variation on the physical characteristics and frictional parameters of common beans (Phaseolus vulgaris L.) concerning the design of the threshing machine. One hundred bean seeds from each variety were randomly selected and their three primary dimensions were measured with a digital vernier caliper (least count 0.01 mm) and a micro-screw gauge in order to determine the dimensional parameters. The remaining parameters (elongation at the width, thickness, and vertical orientation, geometrical mean diameter, arithmetic mean diameter, square mean diameter, equivalent mean diameter, roundness, sphericity, flakiness ratio, aspect ratio, cross-sectional area, projected area, transverse surface area, and the seed volume) were calculated using mathematical models. Gravimetric characteristics true density and seed volumes were calculated using the toluene displacement method. The data were subjected to analysis of variance (ANOVA), and the Duncan multiple range test was used to separate the means. Significance was accepted at 95% confidence interval (p< 0.05).The results data are required for predicting loads in agricultural storage structures, and to establish useful sources for the development of machinery for handling, cleaning, storing, transporting and drying, among other things.

Studying Binary Formation under Dynamical Friction Using Hill’s Problem

Using the equations of motion from Hill’s problem, with added accelerations for different forms of dynamical friction, we provide the (to-date) broadest scale-free study of friction-driven binary formation in gaseous disks and stellar clusters. We focus mainly on binary formation between stellar-mass black holes in active galactic nuclei (AGNs), considering both gas dynamical friction (GDF) from AGN disks and stellar dynamical friction (SDF) from the nuclear star cluster. We first find simple, dimensionless friction coefficients that approximate the effects of standard models for GDF and SDF. We perform extensive simulations of Hill’s problem under such friction, and we present a picture of binary formation through encounters between single stars on nearby orbits, as a function of friction parameter, eccentricity, and inclination. Notably, we find that the local binary formation rate is a linear function of the friction coefficient so long as the friction is weak. Due to the dimensionless nature of our model problem, our findings are generalizable to binary formation at all scales (e.g., intermediate-mass black holes in a star cluster, planetesimals in a gaseous disk).

Adjoint-based sensitivity analysis and assimilation of multi-source data for the inference of spatio-temporal parameters in a 2D urban flood hydraulic model

This contribution presents a novel approach for the calibration of distributed parameters in a 2D urban flood hydraulic model. It focuses on the challenging issue of inferring distributed friction parameters from multi-source heterogeneous spatio-temporal observations of their hydraulic signatures in the context of an urban flash flood in a complex street network. A variational data assimilation algorithm is used to infer high-dimensional multi-variate parameters (spatialized friction and inflow discharge time series) using multi-source observations. This method relies on a differentiable 2D shallow water hydraulic model which enables to generate high-resolution sensitivity maps of local gradients and Derivative-based Global Sensitivity Measures (DGSM), enabling to guide adequate definition of parameter spatialization for the data assimilation process. Assimilated data include real local limnigraphic measurements and high-water marks collected after a major flood event, as well as modeled flow velocity used in twin experiments setups. This study is the first to leverage high-water marks with a variational method for the calibration of distributed parameters in an urban flood model. The multi-source data is used to infer inflow hydrographs and distributed friction parameters in setups of varying complexity. In the main setup, the complex structure of the street network, along with the sensitivity maps and hydraulics expertise led to define a model configuration with 45 friction patches. A high-dimensional parameter vector composed of these friction values and an upstream inflow is inferred simultaneously by assimilating real limnigraphic data and high-water marks. This leads to an increase in model fit to observations and satisfying parameter estimates.

Regional significant wave height forecast in the East China Sea based on the Self-Attention ConvLSTM with SWAN model

Accurate prediction of regional significant wave heights is crucial for marine resource development, especially in areas with limited wave observations. This paper proposes a method for predicting regional significant wave heights by integrating the numerical wave model Simulating Wave Nearshore (SWAN) with the spatio-temporal prediction network Self-Attention ConvLSTM. The SWAN model is used to simulate regional significant wave heights in the East China Sea. To improve simulation accuracy, calibration of wind input, whitecapping dissipation, quadruplet wave–wave interactions, and bottom friction parameterization schemes in the model source terms were conducted, resulting in a simulated correlation coefficient of 0.97 with measured significant wave heights. The regional significant wave height training dataset constructed using the SWAN model preserves the intrinsic relationships between waves, wind speed, and wind direction over a recent period. Subsequently, a regional prediction system is built based on the output of the SWAN model, integrating the self-attention mechanism into the spatio-temporal prediction model ConvLSTM to construct SA-ConvLSTM. This model extracts correlated features among regional waves from the training dataset to predict future significant wave heights at different time intervals. The prediction performance of four spatio-temporal forecasting networks was compared to verify the accuracy of the SA-ConvLSTM model. The average errors in predicting regional significant wave heights at 3, 6, and 12 h were 0.07 m, 0.09 m, and 0.09 m, respectively. The R-Square metrics for these three prediction intervals were 0.85, 0.78, and 0.7, respectively.

Effective parameter identification of the GMS friction model for feed systems in CNC machines

One of the main factors influencing machine tool feed system tracking performance is friction. By creating an accurate friction model and implementing feed-forward compensation based on the model, the negative impacts of friction can be efficiently reduced. The generalized Maxwell-slip (GMS) model is commonly used to model feed system friction; however, simple and effective parameter identification methods are lacking. In this paper, a parameter identification method based on a metaheuristic Gaussian swarm optimization (GSO) algorithm is proposed. The method divides the parameters into two parts via a theoretical derivation, and employs GSO to identify each part successively. The proposed GSO is a novel metaheuristic algorithm inspired by the Gaussian probability function. The excellent performance of the GSO ensures that the friction parameters can be accurately and quickly identified. The results of the simulation and physical identification experiments show that the proposed GSO-based identification method can accurately identify the parameters of the GMS model with average and maximum relative errors of 3.96% and 14.05%, respectively. The identified model can accurately predict the friction of the feed system. Additionally, after friction compensation, the tracking error was decreased by an average of 78.9%.

A Benchmarking Method to Rank the Performance of Physics-Based Earthquake Simulations

Abstract Physics-based earthquake simulators are an increasingly popular modeling tool in earthquake forecasting for seismic hazard as well as fault rupture behavior studies. Their popularity comes from their ability to overcome completeness limitations of real catalogs, and also because they allow reproducing complex fault rupture and interaction patterns via modeling the physical processes involved in earthquake nucleation and propagation. One important challenge of these models revolves around selecting the physical input parameters that will yield the better similarity to earthquake relationships observed in nature, for instance, the frictional parameters of the rate-and-state law—a and b—or the initial normal and shear stresses. Because of the scarcity of empirical data, such input parameters are often selected by trial–error exploration and predominantly manual model performance analyses, which can overall be time consuming. We present a new benchmarking approach to analyze and rank the relative performance of simultaneous earthquake simulation catalogs by quantitatively scoring their combined fit to three reference function types: (1) earthquake-scaling relationships, (2) the shape of the magnitude–frequency distributions, and (3) the rates of the surface ruptures from paleoseismology or paleoearthquake occurrences. The approach provides an effective and potentially more efficient approximation to easily identify the models and input parameter combinations that fit more closely to earthquake relations and behavior. The approach also facilitates the exhaustive analysis of many input parameter combinations, identifying systematic correlations between parameters and model outputs that can potentially improve the overall understanding of the physics-based models. Finally, we demonstrate how the method results agree with the published findings in other earthquake simulation evaluations, a fact that reinforces its overall usefulness. The model ranking outputs can be useful for subsequent analyses, particularly in seismic hazard applications, such as the selection of appropriate earthquake occurrence rate models and their weighting for a logic tree.

System identification and force estimation of robotic manipulator using semirecursive multibody formulation

AbstractForce estimation in multibody dynamics relies heavily on knowing the system model with a high level of accuracy. However, in complex mechatronic systems, such as robots or mobile machinery, the values of model parameters may be only roughly estimated based on design information, such as CAD data. The errors in model parameters consequently have a direct effect on force estimation accuracy because the estimator compensates the erroneous inertia, friction, and applied forces by changing the value of estimated external force. The objective of this study is to present the workflow of system identification and state/force estimation of an open-loop multibody structure. The system identification utilizes a linear regression identification method used in robotics adapted to the multibody framework. The semirecursive multibody formulation, in particular, is studied as a formulation for both system identification and force estimation. The multibody state/force estimator is constructed using extended Kalman filter. The specific aim of this paper is to demonstrate the utilization of these per se known modeling, identification, and estimation tools to address their current lack of integration as a complete toolchain in virtual sensing of multibody systems. The methodology of the study is tested with both artificial and experimental data of Stäubli TX40 robotic manipulator. In the experimental analysis, an openly available benchmark data set was used. Artificial data were created by running an inverse dynamics analysis with inertia and friction parameters taken from literature. The results show that the multibody inertia and friction parameters can be accurately identified and the identified model can be used to produce decent estimates of external forces. The proposed multibody system identification method itself opens new opportunities in tuning the multibody models used in product development. Moreover, effective use of system identification together with state estimation helps to build more accurate estimators. When the system model is accurately identified, the capability of state estimator to observe unknown inputs, such as external forces, is significantly enhanced.

An experimental and numerical study of the influence of the additive manufacturing process in packing properties of particles: the printed shape matters

Investigations into the various properties of granular matter composed of particles with defined shapes have gained increasing attention. Additive manufacturing, with its freedom of shape and rapid prototyping capabilities, has significantly contributed to these studies. However, this technique may introduce defects in the manufactured particles, which can significantly affect the properties of granular materials. The extent of these defects on particles of different shapes is investigated here. Particles of various shapes (cube, octahedron, quatropod, stellated octahedron, tetrahedron, and tetrapod) were manufactured and subsequently imaged using micro-Computed Tomography. The surface roughness, solidity, and convexity of the particles were quantified. Discrete element simulations of granular bed porosity, utilizing both idealized and real particle shapes, were conducted with different surface mesh resolutions and frictional parameters. A clear influence of the manufacturing process on the packing properties of 3D printed particles was identified. This influence is not uniform across all shapes and is directly correlated with the particle convexity. For numerical simulations, a shape-dependent correction of particle density and surface characteristics are imperative for each shape under consideration, despite the fact that the particles were manufactured using the same technique and material.Graphic abstract