Selection Of Optimal Process Parameters Research Articles

Bayesian optimization for stable properties amid processing fluctuations in sputter deposition

We introduce a Bayesian optimization approach to guide the sputter deposition of molybdenum thin films, aiming to achieve desired residual stress and sheet resistance while minimizing susceptibility to stochastic fluctuations during deposition. Thin films are pivotal in numerous technologies, including semiconductors and optical devices, where their properties are critical. Sputter deposition parameters, such as deposition power, vacuum chamber pressure, and working distance, influence physical properties like residual stress and resistance. Excessive stress and high resistance can impair device performance, necessitating the selection of optimal process parameters. Furthermore, these parameters should ensure the consistency and reliability of thin film properties, assisting in the reproducibility of the devices. However, exploring the multidimensional design space for process optimization is expensive. Bayesian optimization is ideal for optimizing inputs/parameters of general black-box functions without reliance on gradient information. We utilize Bayesian optimization to optimize deposition power and pressure using a custom-built objective function incorporating observed stress and resistance data. Additionally, we integrate prior knowledge of stress variation with pressure into the objective function to prioritize films least affected by stochastic variations. Our findings demonstrate that Bayesian optimization effectively explores the design space and identifies optimal parameter combinations meeting desired stress and resistance specifications.

Investigation into hydraulic fracture propagation behavior during temporary plugging and diverting fracturing in deep coal seam

In this study, we conducted indoor temporary plugging and diverting fracturing (TPDF) experiments on samples of natural deep and shallow coal seams using a small-scale true triaxial fracturing simulation system. By integrating high-precision CT scanning technology and crack reconstruction techniques, and relying on the quantitative characterization of the crack complexity coefficient, we investigated the differences in internal structures between deep and shallow coal seam samples and the variations in the expansion of TPDF cracks. Furthermore, the study primarily focused on exploring the influence of temporary plugging agent (TPA) parameters (quantity and particle size) on the expansion patterns of TPDF cracks in deep coal seam coal-rock samples. The experimental results reveal that in shallow coal samples, artificially induced fractures are notably longer and extend to various surfaces of the samples. Conversely, in deep coal samples, the expansion of artificially induced fractures is influenced by well-developed cleavage and cleats. The crack complexity coefficient of artificial fractures in deep coal samples is 1.54 higher than that in shallow coal samples, with an increased crack width of 98 μm. However, the expansion distance of the fractures is shorter and does not extend to the S3 and S6 surfaces. Increasing the dosage of the TPA is advantageous for inducing frequent redirection of fractures and communication with previously untouched areas. When the dosage of the TPA is increased from 30 g/L to 60 g/L, the expansion distance of artificial fractures significantly increases, extending to various surfaces of the samples. The crack complexity coefficient increases by 1.1, and the crack width enlarges by 84 μm. Appropriate particle sizes of the TPA can effectively form seals. However, small particle sizes (200/400 mesh) struggle to seal initial fractures, while large particle sizes (10/20 mesh) can lead to excessive wellhead blockage, causing rapid shut-in pressure, destructive fracturing in deep coal samples, and the generation of a large amount of coal fines. This study holds significant guidance for the optimal selection of process parameters in the temporary plugging hydraulic fracturing of deep coal rock.

Optimizing lyophilization primary Drying: A vaccine case study with experimental and modeling techniques

In this study, we present the lyophilization process development efforts for a vaccine formulation aimed at optimizing the primary drying time (hence, the total cycle length) through comprehensive evaluation of its thermal characteristics, temperature profile, and critical quality attributes (CQAs). Differential scanning calorimetry (DSC) and freeze-drying microscopy (FDM) were used to experimentally determine the product-critical temperatures, viz., the glass transition temperature (Tg’) and the collapse temperature (Tc). Initial lyophilization studies indicated that the conventional approach of targeting product temperature (Tp) below the Tc (determined from FDM) resulted in long and sub-optimal drying times. Interestingly, aggressive drying conditions where the product temperature reached the total collapse temperature did not result in macroscopic collapse but, instead, reduced the drying time by ∼ 45 % while maintaining product quality requirements. This observation suggests the need for a more reliable measurement of the macroscopic collapse temperature for product in vials. The temperature profiles from different lyophilization runs showed a drop in product temperature following the primary drying ramp, of which the magnitude was correlated to the degree of macroscopic collapse. The batch-average product resistance, Rp, determined using the manometric temperature measurement (MTM), decreased with increasing dried layer thickness for aggressive primary drying conditions. A quantitative analysis of the product temperature and resistance profiles combined with qualitative assessment of cake appearance attributes was used to determine a more representative macro-collapse temperature, Tcm, for this vaccine product. A primary drying design space was generated using first principles modeling of heat and mass transfer to enable selection of optimum process parameters and reduce the number of exploratory lyophilization runs. Overall, the study highlights the importance of accurate determination of macroscopic collapse in vials, pursuing aggressive drying based on individual product characteristics, and leveraging experimental and modeling techniques for process optimization.

Experimental investigation on tool pin profile for defect-free multi-layered laminates using friction stir additive manufacturing

Friction stir additive manufacturing (FSAM) is a sub-set of solid-state additive manufacturing technique that falls under sheet lamination additive manufacturing. This technique employs friction stir welding (FSW) to produce large, multi-layered parts via the plate addition method. Sufficient heat generation and material mixing are the basic requirements of FSAM, which are primarily dependent on the tool pin profile and the optimal selection of process parameters. Therefore, three tools with distinct pin profiles were selected (plain cylindrical, cylindrical threaded, and taper threaded) at variable tool transverse speeds (20, 40, 60 mm/min). The current study investigates the influence of pin profile and welding speed on material mixing and heat generation, specifically in terms of defects produced between the bonded layers of aerospace aluminum alloy Al-7075. A tool rotational speed of 500 rpm and 1200 rpm with a tool tilt angle of 2.5° were employed for all three tools to develop a 4-layered laminate. It was observed that among all, taper threaded tool pins produced defect-free laminates at 500 and 1200 rpm for all three welding speeds. This was attributed to the higher microhardness with grain size ranging from 0.96 μm to 1.47 μm. Overall, a 95.61 % reduction in grain size was achieved using the taper threaded tool pin profile compared to the as-received base material. Microhardness was enhanced with the increase in transverse speed and building height, furtherer highlighted with statistical analysis.

Selection of Optimal Process Parameters to Obtain Defect-Free Builds in IN718 Made by Laser Powder Bed Fusion

Selection of Optimal Process Parameters to Obtain Defect-Free Builds in IN718 Made by Laser Powder Bed Fusion

Study on the significance of process parameters in improvising the tensile strength of FDM printed carbon fibre reinforced PLA

The manufacturing industry is undergoing a significant transformation due to the advent of 3D printing technology, which has the ability to fabricate parts with high precision and speed. Carbon fibre reinforced PLA is a commonly utilised material in the realm of 3D printing. This composite material is composed of both PLA and carbon fibres, which synergistically enhance the strength and durability of 3D printed components. Notwithstanding, there exists a considerable amount of research yet to be conducted regarding the optimisation of process parameters to enhance the tensile strength of Fused Deposition Modelling (FDM) printed Poly Lactic Acid (PLA) reinforced with carbon fibres. The objective of this investigation is to assess the importance of process parameters, namely temperature, speed, and layer thickness, in relation to the tensile strength of the 3D printed components. Fused deposition modelling (FDM) is a widely used technique for the additive manufacturing of polymers, which is gaining traction in engineering domains due to its capacity to rapidly fabricate intricate components. The optimal selection of process parameters is crucial for determining the mechanical properties of 3D printed components. Initially, Additive Manufacturing (AM) technology was developed with the purpose of facilitating rapid prototyping and design verification. Developing functional components for end-users using Fused Deposition Modelling (FDM) technology, however, presented a challenging task. This study investigated the impact of three significant process variables, namely infill density, printing speed, and layer thickness, on the tensile properties of polylactic acid (PLA) samples. The Taguchi design of experiment approach is employed to restrict the quantity of tests and ascertain the optimal parameters for attaining maximum mechanical properties, minimal weight, and reduced printing time. The optimal process parameters for achieving the desired values of modulus of elasticity and ultimate tensile strength were determined via experimental investigation.

Process parameter optimization in friction stir welding process using Taguchi method

Friction Stir Welding (FSW) process is an effective welding technique that is used to create high quality solid state joints in industries such as aerospace, automotive and shipbuilding. The significant advantages are no melting and recast issue. In FSW process, profile of tool pin plays a significant part which influence the performance of welded joints. In this investigation, experiments conducted to study the impact of tool pin profile and optimize the process variables in FSW process. Circular, square and triangular types are used as profiles of tool pin in this work. Taguchi orthogonal matrix is used for performing the experiments. Input parameters included are profile of tool pin geometry, rotational speed of tool and welding speed. Response includes tensile strength of the joint and weld’s micro hardness. Taguchi procedure is applied to analyzed the experimental results. Taguchi steps are unique optimization procedure for estimation of optimum selection of process parameters. The experimental result showed that enhanced tensile strength and microhardness with square tool pin profile. It is observed that weld quality is influenced by optimum selection of tool pin’s geometry in FSW process.

In-situ workpiece perception: A key to zero-defect manufacturing in Industry 4.0 compliant job shops

Job shop manufacturing is characterized by excellent job flexibility and highly customizable products. The dynamic nature of jobs in this manufacturing segment poses a relatively high challenge to actualizing zero-defect manufacturing (ZDM). The continuous emergence of new engineering materials and improved consumer’s contributions to product development driven by IIoT further complicates the uphill task. This study develops a methodological framework that positions in-situ workpiece perception and industrial recommender system as tools to trivialize the challenge of ZDM in job shops. In-situ workpiece perception was experimented using a case study of friction stir welding (FSW), a thermomechanical manufacturing process where existing algorithms for online quality assessment are material-specific. The novelty in this study is deciphering unique applications for the hitherto jettisoned sensor data acquired prior to the FSW tool traverse (the actual welding stage), ensuring that the material perception process does not induce defects in the products. This study presents two major contributions towards attaining first-time-right ZDM in job shops. First, a novel interlink for IIoT-driven production planning to aid the selection of optimal process parameters; second, the selection of appropriate quality assessment algorithms during the welding process.

Experimental validation of numerical model for thermomechanical performance of material extrusion additive manufacturing process: Effect of infill design & density

The optimum selection of process parameters, materials, and product design is essential to achieve the desired response of 3D-printed structures, especially in functional components. The current practices of the experimental optimization process require significant resources, which can be limited through numerical modeling and simulation techniques. In this study, a thermomechanical numerical model is used to predict the performance of the additive manufacturing (AM) process, i.e., fused filament fabrication (FFF). 3D printing (3DP) process simulations were performed for tensile testing coupons using carbon fiber-reinforced polyamide-6 (PA6-CF) material. The numerical model predicted the effect of infill patterns and densities on the deflections and distortions during the FFF process. The numerical model predictions were validated via experiments performed under similar conditions. The results conclude that the numerical model can adequately predict the process-induced deflections and distortions during the FFF process. Generally, higher dimensional control was observed for rectangular infill patterns and increased infill density. However, the numerical model overestimates the shrinkage as the stress-relaxation effect is not considered in the numerical model and underestimates the warpages as perfect build plate adhesion is assumed.

Optimal Parameters Selection in Advanced Multi-Metallic Co-Extrusion Based on Independent MCDM Analytical Approaches and Numerical Simulation

Multi-material co-extrusion is a complex thermo-mechanical forming process used to obtain bimetallic billets. Its complexity is due to the combination of diffusion phenomena in the interface of both materials together with the high temperature and pressure generated and the different flow stress characteristics created by the joining of dissimilar materials. Accordingly, the selection of optimal process parameters becomes key to ensure process feasibility. In this work, a comparison among different multi-criteria decision making (MCDM) methodologies, together with different weighting methods, were applied to the simulation results by using DEFORM3D© software to select the optimal combination of process parameters to fulfil the criteria of minimum damage, extrusion force, and tool wear, together with the maximum reduction in the average grain size.

Grinding parameters prediction under different cooling environments using machine learning techniques

ABSTRACT Selection of optimum process parameters is vital for performing a sound grinding operation on Inconel 751 alloy. This paper co-relates the relationship between the most influential input parameters like cutting velocity, depth of cut, feed rate, and environmental conditions to the output parameters, namely, tangential grinding forces, normal grinding forces, temperature, and surface roughness. Three types of machine-learning (ML) algorithms such as support vector machine (SVM), Gaussian process regression (GPR), and boosted tree ensemble techniques are employed to develop a ML model for predicting the output variables during grinding operation of Inconel 751. In order to develop a better ML model, K-fold technique is employed on a total of 81 datasets which are extracted from experimental studies. ML models developed from different algorithms are compared based on performance metrics like R2 score and root-mean-square error (RMSE). GPR algorithm exhibits best results with relatively better R2 score and RMSE value in predicting grinding forces and temperature at wheel work interface. From analyzing the ML models, it is found that cooling environments determined the output grinding parameters to a greater extent when compared with the input grinding parameters.

Selection of Optimal Process Parameters for a Mild Steel Weld using Technique for Order of Preference by Similarity to Ideal Solution

Welding is a major operation in many industries such as production and manufacturing, oil and gas, amongst others. Obtaining an optimal weld with the required properties; tensile strength and hardness is of high importance. The work used the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to select the optimal process parameters for a mild steel weld by employing five (5) standard steps in ranking 30 experimental steps. The result obtained gave an optimal tensile strength and hardness of 496.5MPa and 190.2 respectively for a combined input parameters of 170Amp, 20volts, 22l/min, and 3.2mm. These data is very important as it will provide the necessary information for evaluating the durability and lifespan of the weld product which are factors considered in assessing its quality.

Numerical Simulation and Experimental Study the Effects of Process Parameters on Filament Morphology and Mechanical Properties of FDM 3D Printed PLA/GNPs Nanocomposite.

The selection of optimal process parameters has a decisive effect on the quality of 3D printing. In this work, the numerical and experimental methods were employed to investigate the FDM printing deposition process of PLA/GNPs nanocomposite. The effect of process parameters on cross-sectional morphology and dimension of the deposited filament, as well as the mechanical property of the FDM printed specimens were studied. The extrusion and the deposition process of the molten PLA/GNPs nanocomposite was simulated as a fluid flow by the paradigm of CFD, the effects of printing temperature and shear rate on thermal-physical properties, such as viscosity and surface tension, were considered in models. Under the assumptions of non-Newtonian fluid and creep laminar flow, the deposition flow was controlled by two key parameters: the nozzle temperature and the nozzle velocity. The numerical model was verified by experiments from four aspects of thickness, width, area, and compactness of the deposited PLA/GNPs nanocomposite filament cross-section. Both the numerical simulation and experiment results show that with the increase of nozzle temperature and nozzle velocity, the thickness, area, and compactness of the deposited filament decreases. While the width of deposited filament increased with the increase of nozzle temperature and decrease of nozzle velocity. The decrease in thickness and the increase in width caused by the change of process parameters reached 10.5% and 24.7%, respectively. The tensile strength of the printed PLA/GNPs specimen was about 61.8 MPa under the higher nozzle temperatures and velocity condition, an improvement of 18.6% compared to specimen with the tensile strength of 52.1 MPa under the lower nozzle temperatures and velocity condition. In addition, the experimental results indicated that under the low nozzle velocity and nozzle temperature condition, dimensional standard deviation of the printed specimens decreased by 52.2%, 62.7%, and 68.3% in X, Y, and Z direction, respectively.

Selection of optimum process parameters for penetration depth improvement of welded mild steel joints

High and low heat input degrade weld and weaken bond of fabrications respectively resulting in poor mechanical properties and high failure rate. However, good penetration depth is important factor for quality weld of materials. The research examined various welds by central composite design and then analyzed by response surface methodology. The experiments were achieved using prepared 100 samples of mild steel welded plates measuring 6mm x 40mm x 80mm. The investigation was performed 20 times using 5 samples for each run and then the hardness was measured and analyzed. The results obtained show that the penetration depth terminating at the weld root favoured a high quality weld. The model produced numerical optimal solution of current 120amps, voltage 20volts and gas flow rate 12l/min will produce a welded structure having a penetration depth 5.510mm at a desirability value 95.7%.

Laser Powder Bed Fusion Applied to a New Biodegradable Mg-Zn-Zr-Ca Alloy.

The aim of the present paper is to apply the laser powder bed fusion process to a new biodegradable Mg-Zn-Zr-Ca alloy powder prepared via a mechanical alloying method from powder pure components. This additive manufacturing method is expected to allow for the obtaining of high biomechanical and biochemical performance. Various processing parameters for laser powder bed fusion are tested, with a special focus on laser energy density—E [J/mm3]—which is calculated for all experiment variants, and which represents an important processing parameter, dependent upon all the rest. The goal of all the trials is to find the most efficient schema for the production of small biodegradable parts for the medical domain, meaning the selection of optimal processing parameters. An important observation is that the most robust and homogeneous samples without cracks are obtained for lower values of the E, around 100 J/mm3. Thus, the most performant samples are analyzed by scanning electron microscopy, X-ray diffraction and by compression mechanical test.

Parametric Optimization of PMEDM Process with Aluminium Oxide Powder Suspended Dielectric for Copper Electrodes

Abstract: Electrical Discharge Machining is a commercially utilized technique for manufacturing of various shapes of materials that serve as the base material in a wide range of industries. This report presents the results of the experimental studies carried out on the influence of Powder Mixed Electrical Discharge Machining (PMEDM) input parameters on the various attributes of EDM process while using EDM oil and vegetable oil as dielectric fluid. The experiments performed under the designed full factorial procedure. Machining features, embracing material tool wear ratio, material removal rate and over cut with Ti-6Al-4V as workpiece are the parameters that were assessed in the study. The EDM input parameters - Peak Current, Duty factor and Gap voltage were studied. The results of this study could be utilized in the selection of optimum process parameters to achieve the desired EDM efficiency, surface roughness, and surface integrity when machining Ti-6Al-4V workpiece. Keywords: Electrical Discharge Machining (EDM), Outer Cut (OC), Tool Wear Ratio (TWR)

Physical Modeling of the Impeller Construction Impact on the Aluminum Refining Process.

Obtaining high-quality aluminum is associated with the use of an effective method of refining, which is argon-purging, in which gas bubbles are introduced into the liquid metal by means of rotary impellers. Various rotary impellers are used in the industry; however, if a newly designed impeller is constructed, it should be tested prior to industrial use. For this purpose, physical modeling is used, which enables the investigation of the phenomena occurring during refining and the selection of optimal processing parameters without costly research carried out in the industry. The newly designed rotary impeller was tested on the physical model of a URO-200 batch reactor. The flow rate of refining gas was: 10, 15 and 20 dm3·min−1, whereas rotary impeller speed was 300, 400 and 500 rpm. The research consists of a visualization test showing the schemes of the gas bubbles’ dispersion level in the liquid metal and experiments for removing oxygen from water, which is an analogue of removing hydrogen from aluminum.

Effects of process parameters and annealing on the tensile strength of 3D printed carbon fiber reinforced polylactic acid

The major limitation of the widely used Additive manufacturing technology, Fused Filament Fabrication is the low mechanical strength of printed geometries. Investigations into the impact of process variables and thermal annealing on the tensile strength of composite carbon fiber polylactic acid thermoplastics to overcome the above-mentioned issue were the main aspects of this study. ASTM D638 Type IV tensile models of 20% carbon-infused polylactic acid were used for the investigations. The selected process parameters were infill density, infill pattern, nozzle temperature, layer height, and print speed. Results reveal that the specimen with 90% infill density, gyroid pattern of printing at 230 °C nozzle temperature with a layer height of 0.1 mm and print speed of 40 mm/s exhibited the maximum tensile strength of 37.27 MPa. Further, thermal annealing was carried out at 65 °C, 95 °C, 125 °C, and 155 °C considering the glass-transition temperature and melting point for a duration of 30, 60, 120, and 240 min as a post-treatment technique to improve the mechanical characteristics of the specimen. Annealing done at 95 °C for 120 min enhanced the tensile strength by 14%, thus giving an overall strength of 42.49 MPa. Hence, the optimum selection of process parameters and post-processing conditions could significantly improve the mechanical strength of fused filament fabricated thermoplastics.

ОСОБЕННОСТИ СТРУКТУРООБРАЗОВАНИЯ ПЕРЕХОДНОЙ ЗОНЫ В СЛОИСТОМ КОМПОЗИЦИОННОМ МАТЕРИАЛЕ, ПОЛУЧЕННОМ СВАРКОЙ ВЗРЫВОМ

The paper presents the research on special aspects of structure formation in the transition zones of a layer metal material made of structural carbon and alloy stainless steels with an internal protector. The authors specify the order of layers arrangement. As an industrial method of producing such a material, the explosion welding technology was selected, which ensures the production of three-, four- and six-layer materials with one and two internal protectors per one explosion. The selection of optimal process parameters was carried out using computer modeling in the LS-DYNA software product. By calculation, the authors determined the main technological parameters of the process, which provide in the contact zone at each interlayer boundary the ratio of the amplitude of the generated waves to their length in the range from 0.3 to 0.5. Mechanical tests of multilayer workpieces were carried out. The shear strength of layers was from 320 to 410 MPa, the ultimate tensile strength of the main layer was from 520 to 710 MPa, the impact resistance was from 290 to 740 kJ/m2, and the bending angle under static loading was 140 degrees and higher. The authors determined the phase composition and characteristics of the crystallographic structure of transition zones of a layer metal material with an internal protector. The study identified the presence of γ-Fe with a face-centered crystal lattice, two cubic structures, one hexagonal, and one orthorhombic. On the samples with artificial pitting, the authors determined their influence on the rate of anodic dissolution of a protective layer when contacting with an aggressive environment. The study shows that the interlayer boundaries with a homogeneous structure and minimal thickness have the highest corrosion resistance.

Parametric analysis of dry machining process using a novel integrated multi-attribute decision making approach

In most of the machining processes, the complexity arises in the selection of the right process parameters, which influence the machining process and output responses such as machinability and surface roughness. In such situations, it is important to estimate the inter-relationships among the output responses. One such method, Decision-Making Trial and Evaluation Laboratory (DEMATEL) is applied to study the inter-relationships of the output responses. Estimation of proper weights is also crucial where the output responses are conflicting in nature. In the current study, DEMATEL technique is used for estimating the inter-relationships for output responses in machining of EN 24 alloy under dry conditions. CRiteria Importance Through Inter-criteria Correlation (CRITIC) method is used to estimate the weights and finally the optimal selection of machining parameters is carried out using Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. The model developed guides the decision maker in selection of precise weights, estimation of the inter relationships among the responses and selection of optimal process parameters.