Search results

Porous silicon (Si) was fabricated by using three different wet etching methods, namely, direct current photo-assisted electrochemical (DCPEC), alternating CPEC (ACPEC) and two-step ACPEC etching. This study aims to investigate the structural properties of porous structures formed by using these etching methods and to identify which etching method works best.

Si n(100) was used to fabricate porous Si using three different etching methods (DCPEC, ACPEC and two-step ACPEC). All the samples were etched with the same current density and etching duration. The samples were etched by using hydrofluoric acid-based electrolytes under the illumination of an incandescent lamp.

Field emission scanning electron microscopy (FESEM) images showed that porous Si etched using the two-step ACPEC method has a higher porosity and density than porous Si etched using DCPEC and ACPEC. The atomic force microscopy results supported the FESEM results showing that porous Si etched using the two-step ACPEC method has the highest surface roughness relative to the samples produced using the other two methods. High resolution X-ray diffraction revealed that porous Si produced through two-step ACPEC has the highest peak intensity out of the three porous Si samples suggesting an improvement in pore uniformity with a better crystalline quality.

Two-step ACPEC method is a fairly new etching method and many of its fundamental properties are yet to be established. This work presents a comparison of the effect of these three different etching methods on the structural properties of Si. The results obtained indicated that the two-step ACPEC method produced an etched sample with a higher porosity, pore density, surface roughness, improvement in uniformity of pores and better crystalline quality than the other etching methods.

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.

The purpose of present paper is to enlarge the knowledge about the performance of gypsum powder to realize complex molds or cores for aluminum casting.

The research was divided into two activities: simple; and complex-part production capability. In the simple-part step, the performance of gypsum powder and the minimum mold thickness that would withstand the casting process. In the complex-part step, the authors first investigated the powder removability as a function of geometry complexity and then binder jetting performance was evaluated for the case of lattice-structure fabrication.

All the geometries tested withstand the casting process demonstrating the benefits in terms of complexity part design; however, the process suffers of all the typical defect of casting as misrun, porosity and cold shut.

The results found in this research improve the benefits related to additive manufacturing application in industrial environment and in particular to the binder jetting technology and the rapid casting approach.

This paper aims to report on the findings of an investigation to compare three different three-dimensional printing (3DP) or additive manufacturing technologies [i.e. fused deposition modeling (FDM), stereolithography (SLA) and material jetting (MJ)] and four different equipment (FDM, SLA, MJP 2600 and Object 260) in terms of their dimensional process capability (dimensional accuracy and surface roughness). It provides a comprehensive and comparative understanding about the level of attainable dimensional accuracy, repeatability and surface roughness of commonly used 3DP technologies. It is expected that these findings will help other researchers and industrialists in choosing the right technology and equipment for a given 3DP application.

A benchmark model of 5 × 5 cm with several common and challenging features, such as around protrusion and hole, flat surface, micro-scale ribs and micro-scale long channels was designed and printed repeatedly using four different equipment of three different 3DP technologies. The dimensional accuracy of the printed models was measured using non-contact digital measurement methods. The surface roughness was evaluated using a digital profilometer. Finally, the surface quality and edge sharpness were evaluated under a reflected light ZEISS microscope with a 50× magnification objective.

The results show that FDM technology with the used equipment results in a rough surface and loose dimensional accuracy. The SLA printer produced a smoother surface, but resulted in the distortion of thin features (<1 mm). MJ printers, on the other hand, produced comparable surface roughness and dimensional accuracy. However, ProJet MJP 3600 produced sharper edges when compared to the Objet 260 that produced round edges.

This paper, for the first time, provides a comprehensive comparison of three different commonly used 3DP technologies in terms of their dimensional capability and surface roughness without farther post-processing. Thus, it offers a reliable guideline for design consideration and printer selection based on the target application.

This paper aims to investigate the quality of printed surfaces and manufacturing tolerances by comparing the cylindrical cavities machined in parts obtained by fused deposition modeling (FDM) with the holes manufactured during the printing process itself. The comparison focuses on the results of roughness and tolerances, intending to obtain practical references when making assemblies.

The experimental approach focuses on the comparison of the results of roughness and tolerances of two manufacturing strategies: geometric volumes with a through-hole and the through-hole machined in volumes that were initially printed without the hole. Throughout the study, both alternates are explained to make appropriate recommendations.

The study shows the best combinations of technological parameters, both machining and three-dimensional printing, which have been decisive for obtaining successful results. These conclusive results allow enunciating recommendations for use in the industrial environment.

This paper fulfills an identified need to study the dimensional accuracy of the geometries obtained by additive manufacturing, as no experimental evidence has been found of studies that directly address the problem of the FDM-printed part with geometric and dimensional tolerances and desirable surface quality for assembly.

This paper aims to present an investigation into the variation of scan vector hatch rotation strategies in selective laser melting (SLM), focussing on how it effects density, surface roughness, tensile strength and residual stress.

First the optimum angle of hatch vector rotation is proposed by analysing the effect of different increment angles on distribution of scan vectors. Sectioning methods are then used to determine the effect that the chosen strategies have on the density of the parts. The top surface roughness was analysed using optical metrology, and the tensile properties were determined using uni-axial tensile testing. Finally, a novel multi-support deflection geometry was used to quantify the effects of rotation angles on residual stress.

The results of this research showed that the hatch rotation angle had little effect on the density, top surface roughness and strength of the parts. The greatest residual stress deflection was measured parallel to unidirectional scan vectors. The use of hatch rotations other than alternating 90° showed little benefit in lowering the magnitude of residual stresses. However, the use of rotation angles with a good suitability measure distributes stresses in all directions more evenly for certain part geometries.

All samples produced in this work were made from commercially pure titanium, therefore care must be taken when applying these results to other materials.

This paper serves to increase the understanding of SLM scanning strategies and their effect on the properties of the material.

This paper aims to investigate the wear behaviour of different materials for cylinder liners and piston rings in a linear reciprocating tribometer with special focus on the wear of the cylinder liner in the boundary lubrication regime.

Conventional nitrided steel, as well as diamond-like carbon and chromium nitride-coated piston rings, were tested against cast iron, AlSi and Fe-coated AlSi cylinder liners. The experiments were carried out with samples produced from original engine parts to have the original surface topography available. Radioactive tracer isotopes were used to measure cylinder liner wear continuously, enabling separation of running-in and steady-state wear.

A ranking of the material pairings with respect to wear behaviour of the cylinder liner was found. Post-test inspection of the cylinder samples by scanning electron microscopy (SEM) revealed differences in the wear mechanisms for the different material combinations. The results show that the running-in and steady-state wear of the liners can be reduced by choosing the appropriate material for the piston ring.

The use of original engine parts in a closely controlled tribometer environment under realistic loading conditions, in conjunction with continuous and highly sensitive wear measurement methods and a detailed SEM analysis of the wear mechanisms, forms an intermediate step between engine testing and laboratory environment testing.

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

Polyurethane (PUR) foam parts are traditionally manufactured using metallic molds, an unsuitable approach for prototyping purposes. Thus, rapid tooling of disposable molds using fused filament fabrication (FFF) with polylactic acid (PLA) and glycol-modified polyethylene terephthalate (PETG) is proposed as an economical, simpler and faster solution compared to traditional metallic molds or three-dimensional (3D) printing with other difficult-to-print thermoplastics, which are prone to shrinkage and delamination (acrylonitrile butadiene styrene, polypropilene-PP) or high-cost due to both material and printing equipment expenses (PEEK, polyamides or polycarbonate-PC). The purpose of this study has been to evaluate the ease of release of PUR foam on these materials in combination with release agents to facilitate the mulding/demoulding process.

PETG, PLA and hardenable polylactic acid (PLA 3D870) have been evaluated as mold materials in combination with aqueous and solvent-based release agents within a full design of experiments by three consecutive molding/demolding cycles.

PLA 3D870 has shown the best demoldability. A mold expressly designed to manufacture a foam cushion has been printed and the prototyping has been successfully achieved. The demolding of the part has been easier using a solvent-based release agent, meanwhile the quality has been better when using a water-based one.

The combination of PLA 3D870 and FFF, along with solvent-free water-based release agents, presents a compelling low-cost and eco-friendly alternative to traditional metallic molds and other 3D printing thermoplastics. This innovative approach serves as a viable option for rapid tooling in PUR foam molding.