Primary Process Parameters Research Articles

Introducing explainable artificial intelligence to property prediction in metal additive manufacturing

Industry demands not only accurate predictability, but also the ability to explain predictions made by machine learning models. In the same endeavor, this work introduces eXplainable Artificial Intelligence (XAI) to mechanical property prediction of additively manufactured materials. Historical data for as-built Ti-6Al-4 V alloy manufactured via selective laser melting (SLM) was mined to generate a comprehensive dataset. Robust Gaussian Process Regression (GPR) and Neural Network (NN) were built using a hefty 189 training data arguments. Alongside primary SLM process parameters, novel features such as sample porosity and build direction, which are known to have direct impact on strength, were also utilized. The optimized GPR model exhibited mean absolute errors of 23.9 MPa and 0.58 % when inferencing on test tensile strength and elongation, respectively. On the other hand, the optimized NN model performed slightly worse with errors of 28.24 MPa and 0.97 % for respective tensile properties. Beyond training and testing of the models, they were tested for explainability against different core levels of human-centric understanding put forth by XAI community. It was ultimately concluded that explainability may come at the cost of accuracy.

Optimizing High-Performance Predictive Modeling of the Medium-Speed WEDM Processing of Inconel 718

The purpose of this research was to create a predictive model for a medium-speed wire electrical discharge machine (WEDM) utilizing an artificial neural network (ANN). Medium-speed WEDM experiments were developed based on the I-optimal mixture design for machining, the Inconel 718 superalloy. During the experiment, the input parameters were the spark ontime, spark offtime, wire feed, and current, with the material removal rate (MRR) and surface roughness (Ra) selected as performance indicators. The ANN model was trained on experimental data and built using a feed-forward backpropagation neural network with a (4-8-2) structure and the Bayesian regularization (BR) learning approach. The model correctly predicted the relationship between the medium-speed WEDM’s primary process parameters and machining performance. An integrated ANN model and the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) were used to determine the ideal parameters for the MRR and Ra, resulting in a set of Pareto-optimal solutions. The confirmation experiment revealed that the mean prediction error between the experimental and ideal solutions had a maximum error percentage of 1% for the MRR and 2% for the Ra, which are within acceptable ranges. This showed that the best process–parameter combinations were better for the MRR and Ra.

Multi-objective optimization of Stinger PDC cutter breaking rock parameters under intrusion-cutting action using Taguchi-based gray relational analysis.

To enhance the efficiency of the Stinger Polycrystalline Diamond Compact (PDC) cutter in breaking hard rocks, this study focuses on optimizing the cutter intrusion-cutting rock breaking parameters. A numerical calculation model for the rotational breaking of granite by a Stinger PDC cutter was established. A comprehensive statistical examination was performed to assess the influence of various factors on intrusion ability (IA), tangential force (TF), and mechanical specific energy (MSE). The Taguchi method was used to determine the optimal settings for each factor, while analysis of variance was employed to assess the significance and relative impact of these factors on the target outcomes. In addition, the multi-objective function was optimized using the gray relational analysis method. The primary process parameters obtained for the various performance characteristics are the cone top angle (α), the cone top radius (r), the cutter diameter (d), the cutter back inclination angle (β), and weight on bit (P). The impact ratios of these parameters are 6.20%, 7.66%, 3.93%, 17.20%, and 65.02%, respectively. The optimal geometrical parameters are α = 60°, r = 2mm, and d = 15mm, while the optimal working parameters are β = 30° and P = 800 N. In the optimal case, IA and MSE were reduced by 55.335% and 15.809%, respectively, compared to the initial case. Despite a 15.706% increase in TF, the overall GRG increased for all three evaluation criteria, with an overall increase in efficiency of 18.229%. The results of this paper can provide guidance for the design of Stinger cutter PDC drill bits.

Quantitative recovery and regeneration of basic deep eutectic solvent for biomass treatment with industrialized membrane-based strategy

As a new type of ionic liquids, basic deep eutectic solvents have been reported as emerging medium for biorefinery, extraction, catalysis and so on. Quantitative recovery with recycling of basic DES was important for the scale utilization of biomass and the environmental conservation. An electrodialysis-based method was designed for divisionally regenerating basic DES choline chloride-monoethanol amine (ChCl-MEA) after pretreatment of wheat straw. Influence of primary process parameters on recovery of ChCl-MEA was studied and recovery economy of ChCl-MEA was analyzed. Recovery ratio of ChCl and MEA varied within 89.2 %-96.0 % and 93.3 %-97.9 %. Energy consumption of ChCl-MEA recovery changed within 4.2–29.4 kW·h/kg. Transport rate of basic DES ChCl-MEA altered in the range of 12.2–464.7 mmol/(m2·min). Process economy analysis proved that recovery cost of basic DES ChCl-MEA was less than 7 % (0.81–7.07 $/kg) of DES purchase cost. Insight brought by this study indicated a technically feasible and economical approach for basic DES recycling after biomass pretreatment.

Predictive modelling of laser powder bed fusion of Fe-based nanocrystalline alloys based on experimental data using multiple linear regression analysis

This study harnessed bivariate correlational analysis, multiple linear regression analysis and tree-based regression analysis to examine the relationship between laser process parameters and the final material properties (bulk density, saturation magnetization (Ms), and coercivity (Hc)) of Fe-based nano-crystalline alloys fabricated via laser powder bed fusion (LPBF). A dataset comprising of 162 experimental data points served as the foundation for the investigation. Each data point encompassed five independent variables: laser power (P), laser scan speed (v), hatch spacing (h), layer thickness (t), and energy density (E), along with three dependent variables: bulk density, Ms, and Hc. The bivariate correlational analysis unveiled that bulk density exhibited a significant correlation with P, v, h, and E, whereas Ms and Hc displayed significant correlations exclusively with v and P, respectively. This divergence may stem from the strong influence of microstructure on magnetic properties, which can be impacted not only by the laser process parameters explored in this study but also by other factors such as oxygen levels within the build chamber. Furthermore, our statistical analysis revealed that bulk density increased with rising P, h, and E, while decreased with higher v. Regarding the magnetic properties, a high Ms was achievable through low v, while low Hc resulted from high P. It was concluded that P and v were considered as the primary laser process parameters, influencing h and t due to their control over the melt-pool size. The application of multiple linear regression analysis allowed the prediction of the bulk density by using both laser process parameters and energy density. This approach offered a valuable alternative to time-consuming and costly trial-and-error experiments, yielding a low error of less than 1 % between the mean predicted and experimental values. Although a slightly higher error of approximately 6 % was observed for Ms, a clear association was established between Ms and v, with lower v values corresponding to higher Ms values. Additionally, a further comparison was conducted between multiple linear regression and three tree-based regression models to explore the effectiveness of these approaches.

Scientific substantiation of complex extraction and processing of secondary resources for the development of alternative sources of energy

Scientific research on the integrated extraction and processing of secondary resources, such as mine water, technogenic waste, and others, in order to develop alternative energy sources in the diversification of coal mines is an urgent scientific task that will help solve the issues on the economic, social and environmental transformation of coal mining regions. In practice, it is necessary to consider several alternative options and assess the possibility of obtaining different end products from the targeted processing of secondary raw materials. The choice of a rational processing option to obtain the target product is very complicated due to the different composition of the feedstock, as well as a significant variety of primary processing methods and parameters of intermediate processes, which causes certain difficulties in forecasts and calculations. Developing a methodology for typifying actions when choosing options for integrated processing of recyclable materials of different states, defining the basic principles and its structure for comparative analysis of the input, intermediate, and output parameters in terms of composition, physical, chemical, and technological characteristics are urgent modern tasks.

Influence of processing parameters on the resin pocket geometry of smart composites embedded with Fiber Bragg Grating sensor and its fast prediction

AbstractSmart composites embedded with Fiber Bragg Grating (FBG) sensors are getting increasingly popular in aircraft structures due to their self‐sensing and monitoring capability. However, a resin pocket around the FBG sensor would be formed in the manufacturing process, which strongly impacts the mechanical properties of the composite host. Therefore, the influence of primary processing parameters (FBG orientation angle, preforming temperature, and preforming pressure) on the formation of resin pockets was investigated in this work. It is found that there is a sinusoidal relation between the resin pocket geometry and the FBG orientation angle. A negative linear relation is noted between the resin pocket geometry and the preforming temperature and pressure. A prediction model based on the backpropagation neural network (BPNN) was established, which was able to quickly and accurately predict resin pocket geometry subjected to given processing parameters. This work provides useful guidance for the processing control of smart composites embedded with FBG sensors.Highlights Resin pocket geometry was formed primarily in the preforming stage. Effect of processing parameters on resin pocket geometry was characterized. A BPNN model was established for the fast prediction of resin pocket geometry.

Valorization of bio-oil distillation residue through co-pyrolysis with moso bamboo to prepare biochar: Optimization study on effects of blend ratio, pyrolysis temperature, and residence time

In view of the high energy consumption and pollutant emissions in the cement production industry, biomass as the renewable carbon-neutrality energy is promisingly conducive to remitting cement carbon dioxide emissions and simultaneously realizing waste-to-energy initiatives. In this study, bio-oil distillation residue (DR), with high carbon content and heat value, is co-pyrolyzed with moso bamboo (MB) to optimize the bulk and combustion properties of biochar, aiming at yielding bio-fuel as an alternative fuel to replace coals for energy and heat supply in cement industry. The impacts of three primary process parameters on the carbonization reaction were investigated, while the parametric interactions were further estimated by using response surface methodology (RSM) to optimize the ultimate, proximate and fuel characteristics of biochars. The results showed the co-pyrolysis synergies between bio-oil distillation residue and moso bamboo endowed the maximum biochar yield of 44.62% and the maximum difference biochar yield of 7.56%. Meanwhile, the experimental carbon content, fixed carbon content and high heat value (HHV) were regulated through coupling parametric distribution and further increased by 1.96%, 2.59% and 1.23 MJ/kg, respectively. The parametric responses on combustion characteristics of biochar based on thermogravimetric analysis were predicted, and the optimal carbonization conditions for biochar production were also summarized. This study would offer an optimal biochar production scheme to adaptively yield coal-like biochar for reducing anthropogenic carbon dioxide emissions in cement industry.

Multivariate combined optimization strategy and comparative life-cycle assessment of biomass and plastic residues via microwave co-pyrolysis approach towards a sustainable synthesis of renewable hydrocarbon fuel

The present study revealed the optimum co-pyrolysis process condition and simultaneously evaluated the environmental impact potential during co-valorisation of electronic plastic waste (WEEP) with Hydnocarpus de-oiled seed cake (HDSC) to produce renewable hydrocarbon fuel. In this work, the primary co-pyrolysis process parameters such as operating temperature, mixture ratio, and catalyst loading were modeled and optimized using a combined approach of both Response Surface Methodology (RSM) and Artificial Neural Network (ANN). In the optimized condition, a maximum pyrolysis oil yield of 30.79 wt% was achieved at an operating temperature of 530 °C, a mixture ratio of 0.49, and catalyst loading of 20 wt%. The obtained co-pyrolysis products were characterized to evaluate their physiochemical properties. Finally, a detailed life cycle assessment (LCA) of the scaled-up microwave-assisted co-pyrolysis plant has been modeled, and the result revealed that the emission levels of 771.95 kg CO2 eq, 3.11 kg SO2 eq, 1.69 kg PO4 eq, 338.59 DCB eq, and 0.11 kg C2H4 eq were projected for processing 1 tonne of feedstock. Further, sensitivity analysis revealed that a decrease of 15% in electricity consumption results in a reduction of the overall environmental impact by 13.69%. Above results demonstrate that microwave co-pyrolysis of biomass with plastic waste holds great potential for positive energy production and negative emission technology when compared to other waste management methods. Hence, this investigation paves the way for the sustainable conversion of e-waste plastic and biomass residue into renewable fuel and helps to achieve the sustainability goal of reducing GWP emissions.

Lamellar particle self-assembling of graphene oxide into continuous thin and thick films by automated atmospheric pressure plasma jet deposition

This work shows the achievement of obtaining graphene oxide coatings deposited under ambient conditions using the plasma jet technique at atmospheric pressure in an automated system with robotic arms. Graphene oxide nanoparticles in an aqueous solution were sonicated to obtain a colloidal solution. This was projected in an ultrasonic mist perpendicularly through an air plasma of about 260 °C. The primary process parameters were deposition time, speed, concentration, and distance to obtain coating thickness from about 100 nm to microns. These were homogeneous and adherent coatings on aluminum, steel, glass, and polymers (PMMA and polycarbonate), showing similar characteristics on all these substrates. The highlight of this contribution is transforming nanometric thickness flakes into continuous layers with chemical bonding to surfaces with contrasting characteristics such as those mentioned. The GO obtained layer differs from those obtained by spin-coating, dipping, drop-casting, or electrophoretic deposition. We propose that APPJ eliminates OH radicals around the particles, allowing them to self-assemble by connecting them through bonds. This work probes the feasibility of depositing horizontally/vertically self-assembled GO on polymers and aluminum in a modular industrial facility. This achievement opens the possibility of transferring the characteristics of graphene oxide to surfaces for industrial use, making nanotechnology accessible for social benefit.

Root Cause Analysis of An Inverse Relationship Between The Ice Nucleation Temperature, Process Efficiency And Quality of A Lyophilized Product

The aim of this study was to probe an unexpected relationship between the ice nucleation temperature (TIN), process efficiency and product attributes in a controlled ice nucleation (CIN) lyophilization process. An amorphous product was lyophilized with (CIN-5 °C, CIN-7 °C or CIN-10 °C) or without (NOCIN) control of ice nucleation. Process parameters and product attributes were monitored and compared using a series of advanced in-line and off-line process analytical technology (PAT) tools. Unexpectedly, an indirect relationship was observed between TIN and primary drying efficiency for the CIN processes. Further, the CIN-5 °C process was associated with higher product resistance to mass flow than corresponding CIN-7 °C and CIN-10 °C processes. Surprisingly, the air voids in some NOCIN products were larger than CIN-5 °C products but comparable to CIN-7 °C. Heat flux analysis revealed an indirect relationship between TIN and the minimum hold time required to complete solidification. The heat flux analysis also revealed all products underwent complete solidification prior to primary drying. The order of homogeneity in water activity of the products was CIN-5 °C ≥NOCIN>CIN-7 °C. The higher homogeneity in water activity of CIN-5 °C than corresponding CIN-7 °C processes indicated that the lower process efficiency of CIN-5 °C could not be attributed to unsuccessful induction of ice nucleation during CIN-5 °C. High resolution micro-CT imaging and Artificial Intelligence Image analysis revealed cake wall deformation in CIN-7 °C and NOCIN products but not in CIN-5 °C. In addition, NOCIN products had bimodal distribution in air voids with median size range of 4–5 µm and 151.9–309 µm, respectively, hence the lower process efficiency of NOCIN despite the higher D90. Thus, the observed relationship between TIN and process efficiency may be attributed to microstructural changes post freezing. This hypothesis was corroborated by visible macroscopic cake collapse in NOCIN products but not in CIN products after lyophilization at a higher shelf temperature. In conclusion, the advantages of controlling the ice nucleation temperature of a lyophilization process may only be attained through a robust process design that takes into consideration the primary and secondary drying process parameters. Further, combined use of advanced in-line and off-line PAT tools for process and product characterization may hasten the at scale adoption of advance techniques such as CIN.

Investigation on welding defects of alloys using TIG and MIG welding

This study is to look at the welding procedures of tungsten inert gas (TIG) and metal inert gas (MIG). This study focuses on welding flaws with different types of welding joints for AISI-1020 mild steel when welding with tungsten inert gas (TIG) and metal inert gas (MIG). The effects of various process factors on weld mild steel were investigated. The primary process parameters were modified in this research, and the influence on impact strength and hardness was investigated using destructive tests (tensile test). In a tensile test, the yield stress, elastic stress, breaking stress, and breaking force were all measured. Non-destructive (NDT) testing was also carried out. The ultrasonic and liquid penetration tests were carried out without causing any damage. Additionally, non-destructive testing (NDT) was done. The liquid penetration and ultrasonic tests were conducted using non-destructive testing. A comparison of the two testing approaches also brought attention to the need of outstanding weld quality. Fig. 12 shows the stresses in the tensile test for MIG welding, which are 204.793 N/mm2 for yield, 27688 N/mm2 for elastic behavior, and 180 N/mm2 for breaking behavior. The welding link was severed at 180 N/mm2 for MIG welding. The stresses for TIG welding are depicted in Fig. 13 as 313.633 N/mm2 (yield), 16506.2 N/mm2 (elastic), and 257.890 N/mm2 (breaking). The welding connection has failed at 257.890 N/mm2. Before welding, AISI-1020 mild steel had a breaking stress of 420 N/mm2. After welding, the breaking stress was reduced and is now 257.890 N/mm2.

Microwave-Assisted Nickel and Vanadium Removal from Crude Petroleum Oil Using Bis(2-ethylhexyl)phosphoric Acid: A Kinetic Study

Among the various metals in crude oils, nickel (Ni) and vanadium (V) exist as oil-soluble organic compounds, making them extremely difficult to remove. The presence of these metals has been proven to have deleterious effects, such as catalyst poisoning and corrosion in crude refinery processes. This study aimed for Ni/V removal from Iranian crude oil using bis(2-ethylhexyl)phosphoric acid (D2EHPA) under microwave heating. The impact of primary process parameters, such as temperature, residence time, and D2EHPA dosage, on Ni and V removal efficiencies was studied. The demetallization reaction kinetics and mechanism were investigated at 230–250 °C and optimized conditions. The findings demonstrated that at low microwave powers of 40–50 W, Ni and V removal efficiencies could reach up to 63% and 72%, respectively, after a reaction time of 1 h at 250 °C. The kinetics of demetallization reactions was found to be of first-order, and the activation energies and pre-exponential factors for Ni and V were estimated to be 29.8 and 34.7 kJ/mol and 16.5 and 66.2 min–1, respectively. A well-fitted kinetic model was proposed based on the coefficients of determination. The reaction between the metalloporphyrins in crude oil and D2EHPA resulted in the substitution of Ni and V with protons provided by D2EHPA, leading to the formation of metal salts that are soluble in water. The formation of N–H bonds in the treated oil, as confirmed by the FTIR analysis results, indicated the occurrence of the demetallization reaction.

3D printing of hollow geometries using blocking liquid substitution stereolithography

Micrometer scale arbitrary hollow geometries within a solid are needed for a variety of applications including microfluidics, thermal management and metamaterials. A major challenge to 3D printing hollow geometries using stereolithography is the ability to retain empty spaces in between the solidified regions. In order to prevent unwanted polymerization of the trapped resin in the hollow spaces—known as print-through—significant constraints are generally imposed on the primary process parameters such as resin formulation, exposure conditions and layer thickness. Here, we report on a stereolithography process which substitutes the trapped resin with a UV blocking liquid to mitigate print-through. We investigate the mechanism of the developed process and determine guidelines for the formulation of the blocking liquid. The reported method decouples the relationship between the primary process parameters and their effect on print-through. Without having to optimize the primary process parameters to reduce print-through, hollow heights that exceed the limits of conventional stereolithography can be realized. We demonstrate fabrication of a variety of complex hollow geometries with cross-sectional features ranging from tens of micrometer to hundreds of micrometers in size. With the framework presented, this method may be employed for 3D printing functional hollow geometries for a variety of applications, and with improved freedom over the printing process (e.g. material choices, speed and resulting properties of the printed parts).

A study of the processing and characterization of RSM optimized YSZ-Inconel625 wear-resistant TIG weld cladding

The present investigation focuses on developing a wear-resistant Yttria Stabilized Zirconia (YSZ) reinforced Inconel-625 (IN625) based TIG weld composite cladding on the AISI 304 stainless steel substrate. TIG welding current, scanning speed, standoff distance and YSZ reinforcement % were the primary input process parameters for cladding deposition. For the optimization of process parameters, cladding was deposited at different parametric settings, and their microhardness and sliding wear resistance was calculated as the output responses, according to Box Behnken Design (BBD) under Response Surface Methodology (RSM). Feedstock powders, developed claddings and worn samples were examined to study their surface morphology, microstructure, element composition and different phases using the SEM, Optical Microscopy, EDS, and XRD techniques. The result revealed that welding current and scanning speed are the most significant factors which govern the mechanical and tribological properties of cladding. The cladding was deposited at an optimized welding current 70 A, scanning speed of 192 mm min−1, standoff distance of 2 mm, and 25 wt.% YSZ reinforcement concentration revealed the fine and dense microstructure, resulting in maximum hardness and minimum wear. The microstructure was observed to be columnar dendrites, which grew epitaxially from the substrate. The addition of YSZ particles into the IN625 matrix resulted in the formation of Cr2O3 and ZrO2 stable oxides along with γ-Ni (FCC) phases. The wear test showed that the weight loss in claddings was mainly due to abrasive wear.

Heat Index Based Optimisation of Primary Process Parameters in Friction Stir Welding on Light Weight Materials

In friction stir welding, tool shoulder diameter and its rotational speed are the major influencing parameters than others. A simple novel correlation is proposed to select the optimum range of tool shoulder diameter with respect to the chosen rotational speed and vice versa. The conditions to apply derived correlation were defined through process heat index number as the joint efficiency in the friction stir welding depends on the effective heat supply to the volume of material deformed in the stir zone. Weld speed is the key parameter through which generated heat can be regulated towards optimum heat supply to attain defect-free weld in the stir zone. Effective heat input also has obvious effect on grain growth and corresponding property eradication in the heat affected zone. The experimental study was carried out on AA2024-T3 plates to understand the effect of process heat index on the prescribed optimum range of tool shoulder and rotational speed defined in the correlation. Eventually, a novel relationship was attained between the first order process influencing parameters to deliver maximum joint efficiency.

Predicting the tensile strength of bleach washed denim garments by using fuzzy logic modeling

The prime intention of this study is to develop and validate the efficiency of a fuzzy logic model to predict the tensile strength of bleach washed denim garments. Denim washing is nowadays a widely used technology to give a worn-out appearance to denim garments and thus add esthetic to that garments’ appearance and outlook. In the case of regular overall fading of denim, bleach washing is very popular. At the same time, it is also a matter of concern that the process optimization of bleach washing is critical as bleaching agents have an adverse effect on the fabrics. Among others, bleach concentration, time, and temperature are the primary process parameters in bleach washing, which directly impact the final strength of the treated denim garments. Though their relationship is nonlinear, a fuzzy expert-based system has been constructed to model and illustrate the complex relationship among bleach concentration, time, and temperature in the input side, whereas tensile strength is the output. Strength is the most critical parameter of the final denim product, as it dominates the serviceability of that garments. Therefore, predicting the tensile strength without the trial-and-error method, which is being practiced now, can be a blessing for industrial practitioners. With the help of a laboratory trial, the developed model has been evaluated, and it has been found that the mean relative error was 2.82 and 3.92 for warp and weft direction tensile strength, respectively. On the other hand, the comparison exhibited a coefficient of determination ( R2) of 0.99 for both warp and weft direction tensile strengths. The authors found that the performance of the developed model is satisfactory enough to predict the tensile strength of the bleach washed denim garments.

Artificial Intelligence-Based Tools for Process Optimization: Case Study—Bromocresol Green Decolorization with Active Carbon

This study highlights the benefits of optimizing the decolorization of bromocresol green (a colorant/pH indicator widely used in the industry, whose degradation produces toxic byproducts) by adsorption on active carbon. A set of experiments were planned and performed based on the design of experiments methodology for the following parameters: the colorant concentration (0.009-0.045 g/L), the amount of adsorbent (0.5-3 g/L), and the contact time (60-240 min). Modeling and optimization strategies were employed to determine the working conditions leading to efficiency maximization. Using the response surface methodology, the optimum values of the primary process parameters were established. In addition, a modified bacterial foraging optimization algorithm was applied as an alternative optimizer in combination with artificial neural networks in order to determine multiple combinations of parameters that can lead to maximum process efficiency. Different solutions were obtained with the considered strategies, and the maximum efficiency obtained was >99%. The study emphasizes that adsorption on active carbon is an effective method for bromocresol green decolorization in wastewater that can be further improved using advanced optimization methods.

INFLUENCE OF TEMPERATURE, IMPINGEMENT ANGLE, VELOCITY AND STANDOFF DISTANCE ON THE EROSION CHARACTERISTICS OF INCONEL 718 AND Ti-6Al-4V

Erosion is the primary concern in gas turbines, power plants, fertilizer plants, jet aircraft and civil aircraft. The military, gas turbine blades are affected by dust clouds ingestion and impingement of slurry particles. In this study, we investigated the erosion rate of Inconel 718 and Ti-6Al-4V turbine blades in the air jet-erosion test rig by ([Formula: see text]m) silica erodent. The primary process parameters, such as velocity (100 and 150[Formula: see text]m/s), impingement angle (30–90∘), feed rate (5–10[Formula: see text]g/min), temperature (30–650∘C) and standoff distance (10–50[Formula: see text]mm) were optimized using response surface methodology (RSM). The design of experiments (DOE) and experimental screening tests were performed to identify the significant process parameters on the erosion of turbine blade materials. To optimize the process parameters, the experiment was conducted to obtain a lowest erosion rate. The ploughing and cutting mechanism of both Inconel 718 and Ti-6Al-4V materials were identified through a scanning electron microscope. As a result, the erosion rate increases with an increase in velocity and temperature; however, the erosion rate decreases with an increase in the impingement angle. The experimental analysis showed that the erosion rate ranged between 0.051–1.914[Formula: see text]mg/g for Inconel 718 and 0.02–1.0095[Formula: see text]mg/g for Ti-6Al-4V.

Side surface topography generation during laser powder bed fusion of AlSi10Mg

Additive manufacturing (AM) is a direct manufacturing process that makes it possible to fabricate “near net shape” freeform parts. Among the many metal AM techniques, laser powder bed fusion (LPBF) is the most effective at obtaining complex structures with internal cavities, such as tortuous heat exchangers or lightweight lattice structures. AM technologies have therefore attracted considerable attention, which has led to research and development in many industries. Nevertheless, the surface topographies obtained by current AM techniques are still limiting industrial implementation for parts with high requirements. In this work, surface generation during LPBF was studied and optimized. The main aims were i) to optimize both side surface roughness and material density by studying the influence of the primary process parameters and ii) to investigate the effect of process options on side surface roughness generation for optimization purposes. The roughness dispersion and process reproducibility were also monitored and evaluated. A relationship between top and side surface roughness and material density was established. As a result, both optimizations could be performed in parallel. Analysis of the process reproducibility revealed an important roughness dispersion, especially from one side to the other. Consequently, recommendations on surface measurements were proposed. Compensations and contour settings are key parameters that can help reduce the side surface roughness. Indeed, geometrical positioning of the different weld tracks is also an important issue that must be addressed to reduce surface roughness. Based on the findings of this study, it is possible to reduce the areal average roughness Sa from 40 to 10 µm.