How Can We Provide Additively Manufactured Parts with a Fingerprint? A Review of Tagging Strategies in Additive Manufacturing

Additive manufacturing (AM) is rapidly evolving from “rapid prototyping” to “industrial production”. AM enables the fabrication of bespoke components with complicated geometries in the high-performance areas of aerospace, defence and biomedicine. Providing AM parts with a tagging feature that allows them to be identified like a fingerprint can be crucial for logistics, certification and anti-counterfeiting purposes. Whereas the implementation of an overarching strategy for the complete traceability of AM components downstream from designer to end user is, by nature, a cross-disciplinary task that involves legal, digital and technological issues, materials engineers are on the front line of research to understand what kind of tag is preferred for each kind of object and how existing materials and 3D printing hardware should be synergistically modified to create such tag. This review provides a critical analysis of the main requirements and properties of tagging features for authentication and identification of AM parts, of the strategies that have been put in place so far, and of the future challenges that are emerging to make these systems efficient and suitable for digitalisation. It is envisaged that this literature survey will help scientists and developers answer the challenging question: “How can we embed a tagging feature in an AM part?”.

Neural Network Based Deep Learning Method for Multi-Dimensional Neutron Diffusion Problems with Novel Treatment to Boundary

In this paper, the artificial neural networks (ANN) based deep learning (DL) techniques were developed to solve the neutron diffusion problems for the continuous neutron flux distribution without domain discretization in advance. Due to its mesh-free property, the DL solution can easily be extended to complicated geometries. Two specific realizations of DL methods with different boundary treatments are developed and compared for accuracy and efficiency, including the boundary independent method (BIM) and boundary dependent method (BDM). The performance comparison on analytic benchmark indicates BDM being the preferred DL method. Novel constructions of trial function are proposed to generalize the application of BDM. For a more in-depth understanding of the BDM on diffusion problems, the influence of important hyper-parameters is further investigated. Numerical results indicate that the accuracy of BDM can reach hundreds of times higher than that of BIM on diffusion problems. This work can provide a new perspective for applying the DL method to nuclear reactor calculations.

Comparing full-field data from structural components with complicated geometries

A new decomposition algorithm based on QR factorization is introduced for processing and comparing irregularly shaped stress and deformation datasets found in structural analysis. The algorithm improves the comparison of two-dimensional data fields from the surface of components where data is missing from the field of view due to obstructed measurement systems or component geometry that results in areas where no data is present. The technique enables the comparison of these irregularly shaped datasets without the need for interpolation or warping of the data necessary in some other decomposition techniques, for example, Chebyshev or Zernike decomposition. This ensures comparisons are only made between the available data in each dataset and thus similarity metrics are not biased by missing data. The decomposition and comparison technique has been applied during an impact experiment, a modal analysis, and a fatigue study, with the stress and displacement data obtained from finite-element analysis, digital image correlation and thermoelastic stress analysis. The results demonstrate that the technique can be used to process data from a range of sources and suggests the technique has the potential for use in a wide variety of applications.

Prediction of Yield Strength of Selective Laser Melted Ti-6Al-4V Alloy Using Melt Pool Geometry

Additive manufacturing (AM) method has attracted huge interest in the past decade due to its ability in building complicated geometries with a much lower cost than conventionally produced parts. In AM, which the part is produced in a layer by layer manner, by controlling the AM process parameters, the final mechanical properties can be controlled. In other words, the AM process parameters define the amount of energy that is transferred into the powder, which has a direct relationship with the final mechanical properties. The amount of energy for any sets of AM process parameters affects the melt pool geometry. In this study, the correlation between melt pool geometry and mechanical properties of selective laser melted (SLM) Ti-6Al-4V samples is investigated

A normal equation-based extreme learning machine for solving linear partial differential equations

This paper develops an extreme learning machine for solving linear partial differential equations (PDE) by extending the normal equations approach for linear regression. The normal equations method is typically used when the amount of available data is small. In PDEs, the only available ground truths are the boundary and initial conditions (BC and IC). We use the physics-based cost function used in state-of-the-art deep neural network-based PDE solvers called physics informed neural network (PINN) to compensate for the small data. However, unlike PINN, we derive the normal equations for PDEs and directly solve them to compute the network parameters. We demonstrate our method's feasibility and efficiency by solving several problems like function approximation, solving ordinary differential equations (ODEs), steady and unsteady PDEs on regular and complicated geometries. We also highlight our method's limitation in capturing sharp gradients and propose its domain distributed version to overcome this issue. We show that this approach is much faster than traditional gradient descent-based approaches and offers an alternative to conventional numerical methods in solving PDEs in complicated geometries.

Development of the geometrical feature extraction tool using DBSCAN clustering for toolpath generation in incremental forming

Incremental forming is an emerging manufacturing technique, which allows the forming of the components without product-specific dies. The process uses Computer Numerical Control (CNC) machine tools to form complicated geometries. A punch, mostly a ball end tool, follows the toolpath obtained from the 3D model of the desired geometry to deform a blank into the desired shape. The objective of the current research is to develop a geometrical feature extraction technology to generate the toolpaths for the incremental forming process. A novel geometrical feature extraction tool, developed using attribute clustering techniques is proposed here. The proposed technology extracts geometrical features from the sliced contoured data of the geometry using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering and convex hull algorithms. Initially, the DBSCAN clustering technique is used for parent feature extraction. Later, child features are extracted from the parent features with the help of a convex hull algorithm. This paper discusses in detail the algorithms developed to extract geometrical features. The performance of the developed algorithms is validated with three different multi-featured geometries representing different types of families like geometries with natural partitions (features are connected with edges), geometries with smoothly connected features, and free form geometries. The results show that the techniques work successfully with different complicated geometries to extract the features. The outcome of this research would help evolve a system for an automatic generation of the feature-based toolpaths for the incremental forming (and similar) processes.

A new well-behaved class of compact strange astrophysical model consistent with observational data

The main focus of this paper is to discuss the solutions of Einstein’s Field Equations (EFEs) for compact spherical objects study. To supply exact solution of the EFEs, we have considered the distribution of anisotropic matter governed by a new version of Chaplygin fluid equation of state (EoS). To determine different constants, we have represented the outer space-time by the Schwarzschild metric. Using the observed values of the mass for the various strange spherical object candidates, we have expanded anisotropic emphasize at the surface to forecast accurate radius estimates. Moreover, we implement various analysis to examine the physical acceptability and stability of our suggested stellar model viz., the energy conditions, cracking method, adiabatic index, etc. Graphical survey exhibits that the obtained stellar system fulfills the physical and mathematical prerequisites of the strange astrophysical object candidates Cyg X-2, Vela X-1, 4U 1636-536, 4U 1608-52, PSR J1903+327 to examine the various physical parameters and their effects on the anisotropic stellar model. The investigation reveals that complicated geometries arise from the interior matter distribution obeys a new version of Chaplygin fluid EoS and they are physically pertinent in the investigation of discovered compact structures.

Friction Characterization of UHMWPE Cross-Ply Composite Sheets for Thermoforming Processes

Thermoforming is an attractive process for the low-cost high-volume manufacture of textile-reinforced composite structures with complicated geometries. Tool/ply and ply/ply frictions play critical roles during forming. The friction between the binder ring and the blank induce an in-plane tensile stress that mitigates wrinkling. Unwanted wrinkling can develop across the part if the in-plane stresses are too low but tearing of the material can occur if the applied stresses are too high. Understanding the role that friction plays during thermoforming can give insight on how to mitigate these manufacturing-induced defects in the part. In the current work, the coefficients of friction for two unidirectional cross-ply ultra-high molecular weight polyethylene (UHMWPE) materials are characterized as a function of pressure, fiber orientation, side of material, and pulling rate for [0/90/0/90] cross-ply sheets. The materials are tested at multiple fiber orientations to understand the influence that fiber direction has with respect to the coefficients of friction and on each respective side of the material to understand how surface topology influences the coefficients of friction. The results of the testing are found to correlate with modified Hersey numbers.

On the Relation Between Pressing Energy and Green Strength at Compaction of Hard Metal Powders

The relation between pressing energy and green strength is examined experimentally and numerically using a commercially available design of experiment (DOE) software, at compaction of five hard metal powder materials. This is of substantial practical importance, in particular at pressing of complicated geometries when high values on the green strength is necessary. The compaction energy is here experimentally determined at uniaxial compaction of a cylindrical die, filled with powder material, by measuring punch force and compression. The corresponding measurements of the resulting green strength are performed using standard three-point bend (3PB) testing. The statistical analysis of the results shows that the relation between the two properties, pressing energy and green strength, is very close to a linear fit with the coefficient of determination R2 taking on the value 0.92. This suggests that the pressing energy is an important quantity for reaching a target value on the green strength and the linear relation is certainly convenient in particular when compaction of similar materials is at issue. In parallel with the experimental work finite element calculations are performed in order to evaluate the effect from friction between the powder and the die wall, and it was found that this feature has a limited effect on the pressing energy when similar materials are at issue and is not detrimental for the usefulness of the present correlation approach.

Temperature Distribution Analysis of Composite Heat Sink (Pin Fin) by Experimental and Finite Element Method

Design of machine components plays a vital role in the field of Engineering where it includes the shape of component, size, applied loads, position and materials used. Due to the applied loads namely static, thermal and combined loads etc., the component undergoes stresses and deformations which affect the life of component and also the system. The Finite Element Method (FEM) is a numerical tool used for solving problems of engineering and mathematical problems in the fields of structural analysis, heat transfer, fluid flow, mass transport etc., For problems involving complicated geometries, loadings and material properties, it is generally not possible to obtain analytical solutions. These solutions generally require the ordinary or partial differential equations. Because of the complicated geometries, loadings and material properties, the solution can’t be obtained easily. So, in FEM the complicated shape of the component is divided in to small entities called elements. Element characteristics are studied and then all the elements are combined to make a single system of component. In the present work, Experiments have been conducted to find the temperature distribution within the pin fin made of composite metals and steady state heat transfer analysis has been carried using a finite element software ANSYS to test and validate results. The temperature distribution at different regions of pin fin are evaluated by FEM and compared with the results obtained by experimental work. The results are in good agreement and thus validated.