The Effect of Changing Particle Size Distribution and Layer Thickness on the Density of Parts Manufactured Using the Laser Powder Bed Fusion Process

Powder Bed Fusion (PBF) is a metal additive manufacturing process where parts, described using a CAD data file, are fabricated layer by layer by melting metal powder. Selection of the ideal process parameters for the pulsed laser powder bed fusion (L-PBF) processes is of paramount importance, as there are a vast amount of parameters that have a direct impact on an eventual quality of fabricated parts. The aim of the present study is to present a systematic method of optimising the selection of the process parameters. The research comprehensively investigates the effect on final part density of changing (i) the particle size distribution of the primary powder, (ii) the layer thickness and (iii) the location of the fabricated part on the build platform. All these should help the prediction of the density/porosity of the parts and control their density for their desired applications. In previous studies, volumetric energy density (VED) or scan speed have been used as the control variables for selecting the appropriate applied energy. The VED is not always completely accurate and able to identify the optimum energy that leads to fully dense parts. Consequently it should be used more as a guideline for finding the region in which to operate. A similar value of VED can be obtained using various combinations of laser power, scan speed and layer thickness. However, some values of scan speed, for instance, are not able to produce sufficient melt. To produce components with acceptable mechanical performance requires a comprehensive understanding of process parameters and their interactions. In this work, the process parameters (layer thickness (LT), laser power (LP), point distance (PD), exposure time (ET) and hatching distance (HD)) were considered and studied comprehensively, and individually, to provide a better understanding of the effects of each process parameter on the final built part. Titanium alloy Ti-6Al-4V ELI and 316L Stainless Steel are used throughout this research. The Taguchi experimental design method and the Response Surface Method (RSM) were used to determine and optimise the effect of the selected input parameters, and also to investigate the impact of changing critical parameters on the density of parts manufactured. The influence on the part density was selected as the output to be measured to identify the most statistically significant parameters. This was then followed up by in-depth studies focusing on individual parameters. The results show the ideal combinations of process parameters which can provide fully or near fully dense parts. These process parameter combinations are used to fabricate samples at different layer thicknesses and from a different range of particle size distributions. The effect of changing the layer thickness and particle size distribution was then investigated. It was found possible to fabricate parts with relative density above 99% for layer thickness ranges from 30µm up to 100µm, with appropriate tuning of the process parameters. A clear correlation between the number and shape of pores and the process parameters was identified. Generally, large and irregular pores are usually a result of the lack-of-fusion process due to inadequate melt energy being applied, which can be a result of large PD, HD and LT or short ET. The small and circular pores are mainly due to a result of exaggerated overlapping in HD, PD or elongated ET. These relations are not only due to the effect of the individual process parameter (e.g. PD, HD etc.), but also the interactions between process parameters themselves. These were also found to critically affect the porosity. Then, the study develops regression models and verifies them experimentally. The proposed models were validated and used to accurately predict part density. Finally, a first-of-its-kind study about build location effect was conducted. This shows that part location has a significant impact on sample quality. Potential reasons for the effect of build location are discussed. Spatter was found to be the major factor that causes a variation of the density of parts built in different locations on the platform. The best build location for both materials was found to be close to the inlet of the gas flow. This position minimises the influence of spatter. The study provides a deeper understanding of the variation of the density of a part built in different locations on the build platform and the effect of that on reproducibility. The study also raises awareness about building in angled orientations. The results from this research offer a valuable understanding of ways to optimise the selection of processing parameters, for fabricating parts using PBF with high density/low porosity. It leads to a better understanding of concerns which need to be taken into account for optimal operation. The focus is not restricted to the process parameters of the PBF machine and the PSD of the primary powder used, but also highlights the significance of ensuring correct part orientation/location as all these parameters influence the part density of the fabricated part

Controlling the porosity of 316L stainless steel parts manufactured via the powder bed fusion process

Purpose: The pulsed-laser powder bed fusion (PBF) process is an additive manufacturing technology that uses a laser with pulsed beam to melt metal powder. In this case, stainless steel SS316L alloy is used to produce complex components. To produce components with acceptable mechanical performance requires a comprehensive understanding of process parameters and their interactions. This study aims to understand the influence of process parameters on reducing porosity and increasing part density. Design/methodology/approach: The response surface method (RSM) is used to investigate the impact of changing critical parameters on the density of parts manufactured. Parameters considered include: point distance, exposure time, hatching distance and layer thickness. Part density was used to identify the most statistically significant parameters, before each parameter was analysed individually. Findings: A clear correlation between the number and shape of pores and the process parameters was identified. Point distance, exposure time and layer thickness were found to significantly affect part density. The interaction between these parameters also critically affected the development of porosity. Finally, a regression model was developed and verified experimentally and used to accurately predict part density. Research limitations/implications: The study considered a range of selected parameters relevant to the SS316L alloy. These parameters need to be modified for other alloys according to their physical properties. Originality/value: This study is believed to be the first systematic attempt to use RSM for the design of experiments (DOE) to investigate the effect of process parameters of the pulsed-laser PBF process on the density of the SS316L alloy components. © 2019, Emerald Publishing Limited

High-performance visible light photodetectors based on inorganic CZT and InCZT single crystals

Herein, the optoelectrical investigation of cadmium zinc telluride (CZT) and indium (In) doped CZT (InCZT) single crystals-based photodetectors have been demonstrated. The grown crystals were configured into photodetector devices and recorded the current-voltage (I-V) and current-time (I-t) characteristics under different illumination intensities. It has been observed that the photocurrent generation mechanism in both photodetector devices is dominantly driven by a photogating effect. The CZT photodetector exhibits stable and reversible device performances to 632 nm light, including a promotable responsivity of 0.38 AW−1, a high photoswitch ratio of 152, specific detectivity of 6.30 × 1011 Jones, and fast switching time (rise time of 210 ms and decay time of 150 ms). When doped with In, the responsivity of device increases to 0.50 AW−1, photoswitch ratio decrease to 10, specific detectivity decrease to 1.80 × 1011 Jones, rise time decrease to 140 ms and decay time increase to 200 ms. Moreover, these devices show a very high external quantum efficiency of 200% for CZT and 250% for InCZT. These results demonstrate that the CZT based crystals have great potential for visible light photodetector applicationsAuthors from KKU express their appreciation to the Deanship of Scientifc Research at King Khalid University for funding this work through research groups program under grant number R.G.P. 2/42/4

EDM of Ti6Al4V under nano graphene mixed dielectric: A detailed roughness analysis

Surface finish has an essential role in superior performance of machined products which becomes crucial for sophisticated applications like invasive biomedical implants and aerospace components. Ti6Al4V is popular in these applications due to its exceptional characteristics of weight-to-strength ratio. However, Ti6Al4V is a difficult-to-cut material, therefore, non-traditional cutting techniques especially, Electric Discharge Machining (EDM) are widely adopted for Ti6Al4V cutting. The engagement of nano powders are used to upsurge the cutting rate and surface quality. Among the different powders a novel nano-powder additive i.e. graphene has not been tested in EDM of Ti6Al4V. Therefore, the potential of nano-graphene is comprehensively investigated herein for roughness perspective in EDM of Ti-alloy. The experimental design is based on Taguchi L18 orthogonal framework which includes six EDM parameters. The experimental findings are thoroughly discussed with statistical tests and physical evidence. The surface quality achieved with an aluminum electrode was found best amongst its competitors. Whereas, the worst surface asperities were noticed when brass electrode was used under graphene mixed dielectric. Moreover, it is conceived that the positive tool polarity provides lower roughness for all types of electrodes. Furthermore, optimal settings have been developed that warrant a reduction of 61.4 in the machined specimen's roughness compared to the average roughness value recorded during the experimentation

Characterization of microstructure, weld heat input, and mechanical properties of Mg−Al−Zn alloy GTA weldments

The present study investigated the influence of welding speed on the microstructure, hardness, and tensile properties of the AZ31 Mg alloy gas tungsten arc (GTA) welds that were prepared using alternating current (AC). A microstructural examination of the weld metal and base metal was performed using stereo, optical, and scanning electron microscopy (HR-SEM and EDS) techniques. The microstructure of all fusion zones consists of two parts: a columnar zone, adjacent to the fusion boundary, and equiaxed grains, in the centre of the weld fusion zone. It is shown that the average width of the equiaxed zone present at the centre of the fusion zone increases with increasing welding speed. Metallographic examination shows that the highest welding speed (5 mm/s) results in the smallest average grain size. The welds prepared with high welding speed exhibit an increase in strength, hardness, and ductility compared with other welding speeds, which is attributed to low heat input

Integrated Modelling for Supply Chain Planning and Multi-Echelon Safety Stock Optimization in Manufacturing Systems

Optimizing supply chain is the most successful key for manufacturing systems to be competitive. Supply chain (SC) has gotten intensive research works at all levels: strategic, tactical, and operational levels. These levels, in some researches, have integrated with each other or integrated with other planning issues such as inventory. Optimizing inventory location and level of safety stock at all supply chain partners is essential in high competitive markets to manage uncertain demand and service level. Many works have been developed to optimize the location of safety stock along supply chain, which is important for fast response to fluctuation in demand. However, most of these studies focus on the design stage of a supply chain. Because demand at different horizon times may vary according to different reasons such as the entry of different competitors on market or seasonal demand, safety stock should be optimized accordingly. At the planning (tactical) level, safety stock can be controlled according to each planning horizon to satisfy customer demand at lower cost instead of being fixed by a decision taken at the strategic level. On the other hand, most studies that consider safety stock optimization are tied to a specific system structure such as serial, assembly, or distribution structure. This research focuses on formulating two different models. First, a multi- echelon safety stock optimization (MESSO) model for general supply chain topology is formulated. Then, it is converted into a robust form (RMESSO) which considers all possible fluctuation in demand and gives a solution that is valid under any circumstances. Second, the safety stock optimization model is integrated with tactical supply chain planning (SCP) for manufacturing systems. The integrated model is a multi-objective mixed integer non-linear programming (MINLP) model. This model aims to minimize the total cost and total time. A case study for each model is provided and the numerical results are analyzed

Design for Additive Manufacturing: A Systematic Review

The last few decades have seen rapid growth in additive manufacturing (AM) technologies. AM has implemented a novel method of production in design, manufacture, and delivery to end-users. Accordingly, AM technologies have given great flexibility in design for building complex components, highly customized products, effective waste minimization, high material variety, and sustainable products. This review paper addresses the evolution of engineering design to take advantage of the opportunities provided by AM and its applications. It discusses issues related to the design of cellular and support structures, build orientation, part consolidation and assembly, materials, part complexity, and product sustainability

Compression Performance and Failure Analysis of 3D-Printed Carbon Fiber/PLA Composite TPMS Lattice Structures

Triply periodic minimum surface (TPMS)-based lattice structures have gained interest for their outstanding capacity to absorb energy, their high load-bearing capacity, and their high surface-to-volume ratio. This study considered three TPMS cell topologies, including Diamond, Gyroid, and Primitive. The FDM process was used to print the lattice structures with two materials: pure polylactic acid (PLA) and carbon fiber-reinforced PLA (PLA + CF). The influence of carbon fiber (CF) incorporation, unit cell type (topologies) and size, and relative density (RD) on mechanical properties and failure patterns were explored comprehensively under uniaxial compression testing. The results demonstrate a change in the compressive modulus (0.09 to 0.47 GPa), compressive strength (2.98 to 13.89 MPa), and specific energy absorption (SEA) (0.14 MJ/m3/g to 0.58 MJ/m3/g) due to the influence of CF incorporation, cell type and size, and RD. Results indicate that the Diamond structure outperformed both Primitive and Gyroid structures in terms of compressive modulus and strength, and SEA. All the CF-based TPMS structures showed a higher compressive modulus. Compressive strength and energy absorption capacity were both slightly enhanced in most PLA + CF-based Diamond structures. On the contrary, Gyroid and Primitive structures showed better performance for pure PLA-based structures in terms of compression strength and specific absorption energy

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

The integration of medical signal processing capabilities and advanced sensors into Internet of Things (IoT) devices plays a key role in providing comfort and convenience to human lives. As the number of patients is increasing gradually, providing healthcare facilities to each patient, particularly to the patients located in remote regions, not only has become challenging but also results in several issues, such as: (i) increase in workload on paramedics, (ii) wastage of time, and (iii) accommodation of patients. Therefore, the design of smart healthcare systems has become an important area of research to overcome these above-mentioned issues. Several healthcare applications have been designed using wireless sensor networks (WSNs), cloud computing, and fog computing. Most of the e-healthcare applications are designed using the cloud computing paradigm. Cloud-based architecture introduces high latency while processing huge amounts of data, thus restricting the large-scale implementation of latency-sensitive e-healthcare applications. Fog computing architecture offers processing and storage resources near to the edge of the network, thus, designing e-healthcare applications using the fog computing paradigm is of interest to meet the low latency requirement of such applications. Patients that are minors or are in intensive care units (ICUs) are unable to self-report their pain conditions. The remote healthcare monitoring applications deploy IoT devices with bio-sensors capable of sensing surface electromyogram (sEMG) and electrocardiogram (ECG) signals to monitor the pain condition of such patients. In this article, fog computing architecture is proposed for deploying a remote pain monitoring system. The key motivation for adopting the fog paradigm in our proposed approach is to reduce latency and network consumption. To validate the effectiveness of the proposed approach in minimizing delay and network utilization, simulations were carried out in iFogSim and the results were compared with the cloud-based systems. The results of the simulations carried out in this research indicate that a reduction in both latency and network consumption can be achieved by adopting the proposed approach for implementing a remote pain monitoring system

Prediction of Mechanical Properties for Carbon fiber/PLA Composite Lattice Structures Using Mathematical and ANFIS Models

This study investigates the influence of design, relative density (RD), and carbon fiber (CF) incorporation parameters on mechanical characteristics, including compressive modulus (E), strength, and specific energy absorption (SEA) of triply periodic minimum surface (TPMS) lattice structures. The TPMS lattices were 3D-printed by fused filament fabrication (FFF) using polylactic acid (PLA) and carbon fiber-reinforced PLA(CFRPLA). The mechanical properties of the TPMS lattice structures were evaluated under uniaxial compression testing based on the design of experiments (DOE) approach, namely, full factorial design. Prediction modeling was conducted and compared using mathematical and intelligent modeling, namely, adaptive neuro-fuzzy inference systems (ANFIS). ANFIS modeling allowed the 3D printing imperfections (e.g., RD variations) to be taken into account by considering the actual RDs instead of the designed ones, as in the case of mathematical modeling. In this regard, this was the first time the ANFIS modeling utilized the actual RDs. The desirability approach was applied for multi-objective optimization. The mechanical properties were found to be significantly influenced by cell type, cell size, CF incorporation, and RD, as well as their combination. The findings demonstrated a variation in the E (0.144 GPa to 0.549 GPa), compressive strength (4.583 MPa to 15.768 MPa), and SEA (3.759 J/g to 15.591 J/g) due to the effect of the studied variables. The ANFIS models outperformed mathematical models in predicting all mechanical characteristics, including E, strength, and SEA. For instance, the maximum absolute percent deviation was 7.61% for ANFIS prediction, while it was 21.11% for mathematical prediction. The accuracy of mathematical predictions is highly influenced by the degree of RD deviation: a higher deviation in RD indicates a lower accuracy of predictions. The findings of this study provide a prior prediction of the mechanical behavior of PLA and CFRPLA TPMS structures, as well as a better understanding of their potential and limitations