Dimensional Surface Research Articles

A comparative analysis between backpropagation and general regression neural networks to predict the tensile properties of fused filament fabricated parts

The Fused Filament Fabrication (FFF) process comprises a large number of process parameters that affect the resultant mechanical properties of the parts, generating uncertainties during the Design for Additive Manufacturing (DfAM) practice. Several studies have used Artificial Neural Networks (ANN) based on classification machine learning techniques such as Backpropagation Neural Network (BPNN), to evaluate the dimensional accuracy, surface roughness, compressive, flexural and tensile strength of FFF parts. As an alternative, in this paper a new General Regression Neural Network (GRNN) approach, based on a regression machine learning technique, is proposed to estimate the tensile structural properties of polylactic acid (PLA)-FFF parts using variable process parameters. The performance of the new proposed GRNN is compared with the performance of a BPNN. The comparison and evaluation are based on their capability to accurately predict the experimental Ultimate Tensile Stress (UTS) and the Elastic Modulus ( E) of FFF parts. The outcomes of this evaluation have shown that although the GRNN and the BPNN are able to estimate with high accuracy the structural behaviour of FFF parts, the performance of the proposed GRNN is superior (0.001 Mean Square Error, MSE) than the BPNN (0.0031 MSE). Moreover, the new proposed GRNN fits the experimental test values with a minimal average error of 0.62%. Thus, the proposed GRNN can be used during the design of components intended to be manufacture by FFF.

Optimization of Wax Model Printing using a DLP 3D Printer for Castable Wax Resin material

Wax material can be used to make craft items as models that are printed using 3D printing. Custom and mass production processes for craft items can be carried out according to customer needs. This research aims to find the best process parameters in optimising the wax printing process as a printed model using 3D Printing DLP technology to have the best dimensional accuracy, geometric accuracy, and surface roughness. The method used is a design of experiment with factors in the form of exposure time and layer thickness, each of which has three levels to produce the best response: dimensional accuracy, geometry, and surface roughness. The research results show that print parameters with an exposure time value of 16 seconds and a layer thickness of 0.06 are the best experiments in producing dimensional accuracy of the print results. This is indicated by the smallest shrinkage percentage value of 0.41%. Meanwhile, to obtain smooth print results with minimal defects, printing is carried out with an exposure time parameter of 14 seconds with a layer thickness of 0.06 mm.

On-surface synthesis - Ullmann coupling reactions on N-heterocyclic carbene functionalized gold nanoparticles.

Organic on-surface syntheses promise to be a useful method for direct integration of organic molecules onto 2-dimensional (2D) flat surfaces. In the past years, there has been an increasing understanding of the mechanistic details of reactions on surfaces, however, mostly under ultra-high vacuum on very defined surfaces. Herein, we expand the scope to gold nanoparticles (AuNps) in solution via an Ullmann reaction of aryl halides connected via N-heterocyclic carbenes (NHCs) to AuNps. Through design and syntheses of various organic precursors, we address the influence of the contact angle, reactivity of the halogen and the proximity of the entire coupling partner on on-surface reactivities, thus, establishing general parameters governing organic on-surface syntheses on AuNps in solution, in comparison with the reactivity on defined surfaces under ultra-high vacuum. The retention of such halogenated Nps even at higher reaction temperatures holds great promise in the fields of materials engineering, nanotechnology and molecular self-assembly, while expanding the toolbox of organic chemistry synthesis in accessing various covalent architectures.

Investigation of the effects of energy density in Laser Sintering of polypropylene on morphological and dielectric properties for high-frequency antennas

Advancements in the Internet of Things demand more capable antenna systems, which benefit from the complex geometries enabled by additive manufacturing. Novel approaches, such as external metallization of polymers, enable the design of these geometries but require the processing of low dielectric-loss polymers in additive manufacturing. Arising from prevailing limitations on additively manufactured low-loss components, this study investigates the effect of energy density on polypropylene’s dielectric properties in powder bed fusion processes. Variations in laser power significantly influenced material density and mechanical characteristics. Both insufficient and excessive energy densities resulted in lower quality parts due to issues like fused particles or inadequate melting. Optimal dimensional accuracy and surface quality were achieved with energy levels slightly below those for peak density. While dielectric loss remained mostly stable, relative permittivity decreased at very low densities, where partial melting occurred. Relying on derived optimized parameters, the functionality of passive high-frequency components was demonstrated.

Micro ECDM process comparison using different tool feed methods of constant gravity and spring-force

Quartz glass has been widely used in multiple frontier fields of science and technology owing to its excellent chemical and mechanical properties, such as optical communication, semiconductor, photovoltaic power, and aerospace. ECDM (electrochemical discharge machining) is a non-traditional material removal process suitable for machining non-conductive materials of high hardness and brittleness. The tool electrode feed method is a key factor affecting the ECDM process. Experimental research was carried out for comparing the conventional gravity feed method and a newly-developed spring-force feed method. Micro tool electrodes of Φ150 µm were fabricated by combining the method of TF-WEDG (tangential feed-wire electrical discharge grinding) and reverse micro EDM (electrical discharge machining). Machined microstructures of blind holes, channels, squares, and patterns were compared by the gravity feed method and the spring-force feed method respectively. The experimental results show that the spring-force feed method can improve the micro ECDM process considering the aspects of dimensional accuracy, overcuts, deteriorated edges, surface topography, and tool wear.

Finite element analysis of drilling-induced damage responses on FRP hybrid bio/composite laminates

Machining/drilling is crucial in the joining process of composites. However, drilling composite materials often leads to hole dimensional inaccuracy, surface roughness and delamination, among others. These drilling-induced damage (DID) responses are associated with torque and thrust (principal drilling forces), and rampant in fibre-reinforced polymeric (FRP) hybrid bio/composite laminates. Therefore, a three-dimensional (3D) numerical approach, using finite element method (FEM) was undertaken to analyse the DID responses of FRP hybrid bio/composite laminates. Drilling simulation was performed at spindle velocity and feed rate of 3000 rpm and 5 mm/s, respectively. Both thrust force and torque were produced, computed and interpreted. From the results obtained, it was evident that drilling of synthetic (glass, G and carbon, C) FRP hybrid composites demanded higher or substantial torque and thrust forces, due to their superior strengths, exemplified by the (2G+2F)13 samples, when compared with the weaker natural/plant (flax, F; jute, J and hemp, H) FRP hybrid bio/composites, especially (J+F)27 samples. In addition, the investigation established the advantages of dual layup methods over single arrangements. Carbon and glass FRP hybrid composites exhibited excellent hybridisation properties, but their brittleness necessitated careful management of torque and thrust forces to prevent high DID responses. Summarily, this investigation provided a guide for FRP hybrid bio/composites design and drilling, in addition to opportunities for additional research on other process parameters towards further study.

Parameter optimization of 3D printing process for ceramic sculptures

Abstract In this paper, for the problems of ceramic sculpture 3D printing process, such as the accuracy is difficult to predict and the process control parameters are difficult to choose, we propose to establish an accuracy prediction model using BP neural network optimized by particle swarm algorithm, and put forward the optimization algorithm of the printing process parameters based on genetic algorithm, and construct the fitness function by using the method of the weight coefficients, to achieve the search for the molding angle under the optimal surface quality and to improve the printing efficiency. In the simulation experiments of printing accuracy prediction, the average errors of dimensional error and surface roughness prediction of the PSO-BP network model proposed in this paper are 5.59% and 5.02%, respectively, and the prediction is accurate and reliable. In the 3D printing parameter optimization experiments, the average deviation of the Y and Z dimensions of the physical size printed with the optimized parameters is 0.0496mm and 0.058mm, respectively, which is smaller than the deviation before optimization. Parameter optimization has also reduced surface roughness in the molded parts of the porcelain sculpture. The extrusion head and hot bed temperature were adjusted to reach the temperature target after maintaining hollow finger precision within 3 degrees.

Quantum rotational dynamics of l-C4(3Σ-g) by H2 at low temperatures employing a machine learning augmented potential energy surface.

A new four dimensional (4D) ab initio potential energy surface (PES) is generated for the collision of C4(3Σg-) with H2(1Σg), considering both molecules as rigid rotors. A supervised neural network model is created to augment the ab initio PES and to get the missing data points. Furthermore, space fixed expansion of the augmented PES is carried out using a least squares fit over two spherical harmonics terms, resulting in radial coefficients (λ1, λ2, and λ). The centre of symmetry in both C4 and H2 forces λ1 and λ2 to have even values, respectively. Moreover, the rotational states of C4 are only populated by odd levels due to its ground state triplet symmetry and the nuclear spin (I = 0) of 12C. The cross-sections and rate coefficients with para and ortho H2 partners are studied for various odd state transitions, where the rate coefficients of the ortho are 10-20% higher than those of the latter. The de-excitation rates obtained by the para H2 collisions are also compared to those of He and are found to be ∼1.7-2.8 times the He rates, across various order transitions. The simple scaling of He rates using a factor of 1.38 proves insufficient to describe para H2 rates. Therefore, these results show the importance of explicitly studying H2 as an important colliding partner, governing the kinetics of various rotational processes in the interstellar space.

Polypositroids

Abstract We initiate the study of a class of polytopes, which we coin polypositroids, defined to be those polytopes that are simultaneously generalized permutohedra (or polymatroids) and alcoved polytopes. Whereas positroids are the matroids arising from the totally nonnegative Grassmannian, polypositroids are “positive” polymatroids. We parametrize polypositroids using Coxeter necklaces and balanced graphs, and describe the cone of polypositroids by extremal rays and facet inequalities. We introduce a notion of $(W,c)$ -polypositroid for a finite Weyl group W and a choice of Coxeter element c. We connect the theory of $(W,c)$ -polypositroids to cluster algebras of finite type and to generalized associahedra. We discuss membranes, which are certain triangulated 2-dimensional surfaces inside polypositroids. Membranes extend the notion of plabic graphs from positroids to polypositroids.

OPTIMIZATION OF ACCURACY AND SURFACE ROUGHNESS OF 3D SLA PRINTED MATERIALS WITH RESPONSE SURFACE METHOD

3D printers are used frequently for rapid prototyping and production. SLA (stereolithographic) printers, widely used in areas requiring precision production, form the final shape by solidifying the liquid resin with UV rays. In SLA printing, the final figure is created by changing many printing parameters. For this reason, surface integrity and precision of measurements vary. Dimensional accuracy (DA) and surface roughness (SR) outputs should be investigated for precise printing. Therefore, the effects on SR and DA output parameters were investigated by changing the layer height, exposure time, and lift input parameters with the Response Surface Method (RSM). The effective parameters for both outputs are layer height and lift. As the layer height and lift increased, the SR and DA values of the printed parts increased. The predicted results calculated with the regression equations and the experimental results were quite close. Optimum input parameters were found by multi-response optimization. Accordingly, the 8th experiment, 0.05mm-4s-1.5mm, was the best parameter. The difference between the predicted and experimental values for multi-response optimization was 4.28% for SR and 0.27% for DA. Thus, effective parameters for SR and DA have been determined for precision production in SLA printers.

Effect of autoclaving on the dimensional stability and surface characteristics of 3D printed PVDF composite-based implants

In the past decade, a lot of work has been reported on the use of polyvinylidene fluoride (PVDF) based thermoplastic composites as energy storage devices, and implant materials. But hitherto little has been reported on the dimensional stability and surface characteristics of 3D-printed PVDF composites after autoclaving for implant applications. In this study, the effect of autoclaving on surface characteristics (Shore-D hardness, surface roughness (Ra), morphological characteristics), and dimensional stability of 3D printed PVDF-hydroxyapatite (HAp)- chitosan (CS) composite has been reported for implant applications. The signal-to-noise (S/N) ratio approach was used to ascertain the best setting of process parameters for 3D printing by fused filament fabrication (FFF) process. This study suggests that the best setting for the FFF process, for the 3D printing of PVDF composite (90%PVDF-8%HAp-2%CS) are the nozzle temperature (NT) of 225°C, raster angle (RA) 0°, and printing speed (PS) 40 mm/s, resulting in Shore-D hardness 48.5 HD (before autoclaving) and 55.0 HD (after autoclaving), dimensional deviation 0.01 mm (after autoclaving). The results are supported by scanning electron microscopy (SEM) and Fourier transmission infrared (FTIR) spectroscopy analysis.

Experimental Optimization of High-precision Turning Parameters of AL6061 Materials for Automotive Industry Based on Grey Relational Analysis

This research article aims to explore the relationship between the machining parameters of a Slant Bed Turning Centre Computer Numerical Control (SB/C/CNC) precision lathe and surface microhardness, dimensional error and surface roughness of AL6061. A technique called the central composite design (CCD) method with 13 experiments was used to evaluate the surface microhardness, dimensional error, and surface roughness after a turning operation using a micro-grooved texture tool. Separate prediction models were developed for each of these characteristics using the response surface method (RSM) in order to find the optimal process parameters for each characteristic. The analysis of variance revealed that the prediction models for surface microhardness, dimensional error, and surface roughness were highly significant, with p-values less than 0.0001. The process parameters that resulted in the highest surface microhardness were a cutting speed (Vc) of 154.363 m/min and a feed rate (fz) of 0.231 mm/rev. On the other hand, the process parameters that led to the lowest dimensional error and surface roughness were Vc = 154.363 m/min, fz = 0.1389 mm/rev, and Vc = 152.081 m/min, fz = 0.1025 mm/rev, respectively. The multi-objective prediction model based on gray relational analysis showed an error range of 1.5% to 3.1% and a minimum gray relational degree value of 0.3503 within the feasible process parameter range. The accuracy of this multi-objective prediction model was higher, with a stronger response to the cutting speed Vc compared to the feed rate fz. The determined feasible process parameter range serves as a useful reference for engineers working with AL6061 materials in turning operations.

Tailoring structure and nanoscale surface topography in Mg–N doped CuCrO[formula omitted] thin films via post deposition annealing for optothermoelectric application

In this study, we explored how varying the annealing temperature and the duration of post-annealing affect the structural, optical, and thermoelectric characteristics of thin films doped with Mg–N in CuCrO2. As the annealing time increased, we observed that all the annealed films exhibited enhanced conductivity at room temperature. Additionally, both the power factor and Seebeck coefficient also exhibited increases. The quantitative assessment of the nanometric dimensional surface roughness and the surface nano-texture of the deposited film was done by atomic force microscopy analysis. The spiky peaks and positive roughness skewness (Rsk) value made the two-hour annealed film suitable for energy harvesting. Wide band gap and high transmission of more than 60% were found by UV–Vis spectroscopic analysis and Fermi energy of 7 meV is calculated and determined the possibility of the film from being used in ohmic contact with delafossite to prevent Schottky barriers and track the work function of the material.

Optimizing Surface Finish and Dimensional Accuracy in 3D Printed Free-Form Objects

3D printing of free-form objects presents inherent complexity due to their organic and intricate shapes. Designers engage with such objects, considering a range of factors including aesthetics, engineering viability, and ergonomic comfort. This research is focused on achieving the most effective printing parameters for a free-form object utilizing the Digital Light Processing (DLP) technique within a 3D printer. Within this study, a squeezed hexagonal tube-shaped CAD model was employed as an experimental subject, following the principles of the Response Surface Method (RSM). The research delved into the optimization of printing parameters, particularly layer thickness and exposure time, to enhance the dimensional accuracy and surface quality of the free-form model. Two levels were established for each factor: layer thickness was set at 0.06 mm (low) and 0.08 mm (high), while exposure time was tested at 6 s (low) and 8 s (high). The assessment of surface quality involved a qualitative evaluation employing a digital microscope to identify potential defects and imperfections in the print outcomes. The investigation culminated in the identification of the optimal printing parameters: a layer thickness of 0.0753 mm and an exposure time of 7.2143 seconds. This achievement not only enhances the understanding of 3D printing variables in the context of intricate free-form models but also contributes to the broader field of additive manufacturing parameter optimization.

On geometry of two dimensional surfaces in four dimensional Euclid space

On geometry of two dimensional surfaces in four dimensional Euclid space

New insight into the structural, elastic, dynamics and thermodynamic properties of Fe3P under high pressures

Many efforts have been devoted to developing the linkage between atomic characteristics and the mechanical properties. Equilibrium lattice parameters, elastic constants, dynamics and thermodynamic properties of Fe3P are systematically explored by density functional theory (DFT) calculations with a generalized gradient approximation (GGA) from Perdew-Wang's 1991 (GGA-PW91), Perdew-Burke- Erzenhof (GGA-PBE), local density approximation (LDA), GGA-PW91+U, GGA-PBE + U and (LDA) + U method (U is the Hubbard correction), respectively. It concluded that the lattice parameters and bulk modulus B from GGA-PBE + U results tend to be more compatible with experimental results than others. The elastic moduli of Fe3P is reported based on the Voigt-Reuss-Hill model. Meanwhile, the anisotropy index and the three dimensional (3D) surface profile of Young's modulus determine the anisotropy. The obtained results indicate that Fe3P exhibits strong elastic anisotropy under high pressure. In addition, the dynamical and mechanical stabilities of Fe3P are confirmed by calculating the phonon and elastic constants up to 40 GPa. Finally, we studied the thermodynamic properties of Fe3P under pressures based on quasi-harmonic approximation.

Dry and Minimum Quantity Lubrication Machining of Additively Manufactured IN718 Produced via Laser Metal Deposition

Inconel 718 (IN718), a Ni-based superalloy, is immensely popular in the aerospace, nuclear, and chemical industries. In these industrial fields, IN718 parts fabricated using conventional and additive manufacturing routes require subsequent machining to meet the dimensional accuracy and surface quality requirements of practical applications. The machining of IN718 has been a prominent research topic for conventionally cast, wrought, and forged parts. However, very little attention has been given to the machinability of IN718 additively manufactured using laser metal deposition (LMD). This lack of research can lead to numerous issues derived from the assumption that the machining behavior corresponds to conventionally fabricated parts. To address this, our study comprehensively assesses the machinability of LMDed IN718 in dry and minimum quantity lubrication (MQL) cutting environments. Our main goal is to understand how LMD process variables and the cutting environment affect cutting forces, tool wear, surface quality, and energy consumption when working with LMDed IN718 walls. To achieve this, we deposited IN718 on SS309L substrates while varying the following LMD process parameters: laser power, powder feed rate, and scanning speed. The results unveil that machining the deposited wall closer to the substrate is significantly more difficult than away from the substrate, owing to the variance in hardness along the build direction. MQL greatly improves machining across all processing parameters regardless of the machining location along the build direction. Laser power is identified as the most influential parameter, along with the recommendation for a specific combination of power feed rate and scanning speed, providing practical guidelines for optimizing the machining process. While MQL positively impacts machinability, hourly energy consumption remains comparable to dry cutting. This work offers practical guidance for improving the machinability of LMDed IN718 walls and the successful adoption of LMD and the additive–subtractive machining chain. The outcomes of this work provide a significant and critical understanding of location-dependent machinability that can help develop targeted approaches to overcome machining difficulties associated with specific areas of the LMDed structure. The finding that MQL significantly improves machining across all processing parameters, particularly in the challenging bottom region, offers practical guidance for selecting optimal cutting conditions. The potential economic benefits of MQL in terms of tool longevity without a substantial increase in energy costs is also highlighted, which has implications for incorporating MQL in several advanced manufacturing processes.

A Methodological Framework for Assessing the Influence of Process Parameters on Strand Stability and Functional Performance in Fused Filament Fabrication.

With an ever-increasing material and design space available for Fused Filament Fabrication (FFF) technology, fabrication of complex three-dimensional structures with functional performance offers unique opportunities for product customization and performance-driven design. However, ensuring the quality and functionality of FFF-printed parts remains a significant challenge, as material-, process-, and system-level factors introduce variability and potentially hinder the translation of bulk material properties in the respective FFF counterparts. To this end, the present study presents a methodological framework for assessing the influence of process parameters on FFF strand stability and functional performance through a systematic analysis of FFF structural elements (1D stacks of FFF strands and 3D blocks), in terms of dimensional deviation from nominal geometry and resistivity, corresponding to the printability and functionality attributes, respectively. The influence of printing parameters on strand stability was investigated in terms of dimensional accuracy and surface morphology, employing optical microscopy and micro-computed tomography (mCT) for dimensional deviation analysis. In parallel, electrical resistance measurements were carried out to assess the effect of different process parameter combinations and toolpath patterns on functional performance. In low-level structural elements, strand height (H) was found to induce the greatest influence on FFF strand dimensional accuracy and resistivity, with higher H values leading to a reduction in resistivity of up to 38% in comparison with filament feedstock; however, this occurred at the cost of increased dimensional deviation. At higher structural levels, the overall effect of process parameters was found to be less pronounced, indicating that the translation of 1D strand properties to 3D blocks is subject to a trade-off due to competing mechanisms that facilitate/hinder current flow. Overall, the proposed framework enables the quantification of the influence of process parameters on the selected response variables, contributing to the development of standard operating procedures and recommendations for selecting optimal process parameters to achieve the desired process stability and functional performance in FFF.

On the influence of building position on dimensional accuracy and surface quality of aluminum blocks manufactured by L-PBF

On the influence of building position on dimensional accuracy and surface quality of aluminum blocks manufactured by L-PBF

Thermoelastoplastic hydrodynamic lubrication contact analysis of three dimensional rough tooth surface considering micro-scale effect and thermal effect

The contact properties of gear rough surfaces during meshing are influenced by various factors. The analysis of rough surface thermoelastoplastic hydrodynamic lubrication (TPEHL) considering micro-scale effect and thermal effect has not been addressed in the literature. This study establishes a new TEPHL model with multi-factor coupling of thermal field, fluid lubrication, stress-strain, and geometric profile. Based on strain gradient theory (SG), a new thermoelastoplastic material constitutive considering micro-scale effect is developed. Subsequently, the three-dimensional rough surface TPEHL-SG analysis of gear is conducted. The paper investigates the impact of thermal and micro-scale effects on various parameters during the meshing of gear in the full oil film lubrication condition, including surface pressure, oil film thickness, surface temperature, surface deformation, and friction coefficient.