Design Of Experiments Research Articles

Efficient Optimization of Magnetic Properties of Fe–4.5Si Alloy in Laser Powder Bed Fusion (LPBF)

Abstract Existing studies have successfully identified optimal process parameters to enhance relative density and mechanical properties. However, these optimizations often incur high experimental costs due to the need for repetitive experiments. This research introduces an efficient optimization framework using two design of experiment (DoE) methods and machine learning algorithms, aimed at optimizing the magnetic properties of Fe–4.5Si material. The framework consists of three steps: (1) identification of the process parameter region that ensures high relative density using cubic specimens, (2) development of surrogate models to predict magnetic properties, such as maximum relative permeability and core loss, using toroidal specimens, and (3) multi-objective optimization using the surrogate models and a multi-objective genetic algorithm (MOGA) to enhance magnetic properties. The results demonstrate that this method can efficiently optimize process parameters with a limited number of experiments. Furthermore, the versatility of this framework allows its application to other materials in the early stages of research, even without prior knowledge of specific materials.

From Waste to Roads: Improving Pavement Performance and Achieving Sustainability with Recycled Steel Slag and Low-Density Polyethylene

The use of waste, recycled, and modified materials is increasingly popular in roadway construction for sustainability and pavement longevity. This research examines the combination of steel slag (SS) and low-density polyethylene (LDPE), commonly used in plastic bags and steel manufacturing by-products, to mitigate environmental pollution. LDPE was tested as a binder modifier in two bitumen grades, 60–70 and 80–100, at concentrations of 3%, 5%, and 7% by weight. SS was used as a replacement for coarse aggregate. The physical properties of both modified and unmodified bitumen grades and SS were analyzed before creating and testing hot-mix asphalt (HMA) samples. The dynamic modulus of these samples was measured at temperatures of 4.4 °C, 21.1 °C, 37.8 °C, and 54.4 °C with frequencies of 0.1 Hz, 0.5 Hz, 1 Hz, 5 Hz, 10 Hz, and 25 Hz. Master curves were developed, and the dynamic modulus data underwent design of experiment (DOE) and computational intelligence (CI) analyses. Using KENPAVE, a mechanistic–empirical tool, the analysis assessed the design life and enhancements in damage ratio for each modifier and grade. The results showed that adding LDPE increases the softening point and penetration grade but decreases ductility due to increased bitumen stiffness, leading to premature fatigue failure at higher LDPE levels. Both 3% LDPE and 3% SS-modified LDPE improved Marshall Stability and dynamic modulus across all temperature and frequency ranges. Specifically, 3% LDPE enhanced stability by 13–16% and 3% SS-LDPE by 30–32%. The KENPAVE results for 3% LDPE showed a design life improvement of 19–25% and a damage ratio reduction of 15–18%. In comparison, 3% SS-LDPE demonstrated a design life improvement of 50–60% and a damage ratio reduction of 25–35%. Overall, this study concludes that 3% LDPE- and 3% SS-LDPE-modified HMA in both bitumen grades 60–70 and 80–100 provide optimal results for improving pavement performance.

Optimization of FSW input parameters for dissimilar butt-welded joints of Al6061-AZ31 alloys by GRA

In this paper, the optimization of Friction Stir Welding (FSW) parameters used to produce dissimilar non-ferrous butt joints of Al6061 and AZ31 alloys is investigated carefully. FSW parameters including tool rotation speeds (TRS) ranging between 700–1100 rpm, traverse speed (TS) 40–80 mm/min, and axial load (LA) 7–11 KN are considered for finding suitable FSW parameters. The experiments are carried out at different combinations of FSW parameters and the results are measured using respective equipment. The L18 orthogonal array of Taguchi design of experiments is adapted for Grey relation analysis (GRA) multi-objective optimization aiming for high tensile strength and less wear rate with measured responses. Based on grey relational grade, TRS, TS, and axial load of 1100 rpm, 60 mm/min, 9KN are found to best suitable FSW parameters with reasonable tensile strength of 132.43 MPa, microhardness of 70.4 Hv, and lower wear rate of 5.156 mm3/m, respectively. The confirmatory test has been performed to verify the finding of the optimization study with a maximum error percentage of 2%. Furthermore, the corrosion analysis has also been performed for the produced FSW butt joint at a suitable parametric combination. Corrosion behavior is improved due to equiaxed grain distribution and formed hard Mg2Si precipitates that reduce the differential dissolution effect when embedded in Al matrix. It signifies the improvement in chemical inertness in application to extreme temperature environments.

Regioselective Hydroformylation of α,β‐Unsaturated Esters: Impact of Reaction Parameters & Reaction Optimization

The hydroformylation of α,β‑unsaturated esters represents a highly atom‐economical route towards β‑aldehydes with the potential application as precursors for derivatization to pharmaceuticals such as (R)‑Baclofen or Pregabalin. Methyl 4‐chlorocinnamate as an α,β‑unsaturated ester has been rarely studied in hydroformylation, particularly with regard to a parameter screening for reaction optimization. In this work, the regioselective hydroformylation of methyl 4‑chlorocinnamate to the desired β‑aldehyde was investigated with a particular focus on the identification of parameters with a significant effect on the regioselectivity and selectivity to the β‑aldehyde amongst all products. The reaction time, solvent, and catalyst loading were initially screened followed by a systematic screening using design of experiments (DoE). The reaction output was analyzed in terms of pressure, temperature and the CO/H2 composition with an unmodified Rh catalyst revealing that while either regioselectivity or overall selectivity (termed selectivity) can attain high values, neither parameter can simultaneously reach optimal levels. As predicted by the model, the experimental results under two different conditions demonstrated excellent regioselectivity (96%) with medium selectivity (57%) and a high selectivity (78 %) with good regioselectivity (93%). Expansion of substrate scope revealed a significant influence of the substituents and thus the electronic effect on the regioselectivity and selectivity.

Design of experiments method into upgrading pyrolytic oil for sustainable aviation fuel additives by hydrotreating and hydrocracking.

Recycling waste to produce liquid fuels for the automotive and aviation industries is a major global concern, especially in light of the ongoing energy crisis. Because waste is used in thermal conversion processes, the resulting liquid products often require additional processing to reduce their density and viscosity, and to remove oxygenated compounds or pollutants that hinder further utilization. Catalytic hydrogenolytic reactions such as hydrodeoxygenation (HDO) and hydrocracking (HC) have been extensively applied to upgrade pyrolysis oils. Selecting the appropriate catalyst and optimizing the process operating conditions are crucial for yielding high-quality fuel. Design of experiments (DOE) and analysis of variance (ANOVA) can identify the primary factors of the process and their possible interactions. This research focuses on the conversion of pyrolysis oil derived from car tires into jet fuel and aims to determine the optimal HDO and HC conditions to maximize the concentration of the kerosene fraction. Hydrodeoxygenation and hydrocracking reactions using NiMo/γ-Al2O3 catalysts are examined under varying temperature, pressure, and time conditions. The compositions of the raw tire pyrolysis oil (TPO) are mainly characterized by heteroatom content, aromatic compounds, olefins and acetylenes, alkanes, and cycloalkanes, which play key roles during HDO and HC procedures. Subsequently, the distillation and separation of the fuel fractions are carried out to determine the quality of the product.

Species-specific optimization of oxylipin ionization in LC-MS: a design of experiments approach to improve sensitivity.

Oxylipins are diverse bioactive signaling molecules, which occur in very low concentrations in complex matrices, posing challenges in achieving consistent and sensitive analysis. UHPLC-MS/MS is the preferred technique to separate and quantify these molecules, often optimized using a time-consuming trial-and-error approach. In this study, we applied the design of experiments (DoE) approach to systematically investigate the ionization properties of multiple oxylipin species. Fractional factorial and central composite designs were employed to detect relevant instrument parameters and optimize signal intensity in ESI-MS/MS analysis. Response surface modeling revealed distinct ionization and fragmentation behaviors between polar and apolar oxylipins, driven by their responses to interface temperature and collision-induced dissociation (CID) gas pressure. Particularly, prostaglandins and lipoxins benefit from higher CID gas pressure and lower temperatures compared to the lipophilic HODEs and HETEs to achieve optimal intensity in multiple reaction monitoring analysis. While global source parameters were optimized, analyte-specific entrance/exit potentials and collision energies required individual adjustments. The final method was applied to analyze seven oxylipin classes including leukotrienes, prostaglandins, lipoxins, resolvins, HETEs, HODES, and HoTrEs. Although improvements in lower limits of quantification were modest (< 1pg on-column), signal-to-noise ratios increased two-fold for lipoxins and resolvins and three- to four-fold for leukotrienes and HETEs, enhancing detection at trace levels. This DoE-guided strategy provides a powerful tool to improve UHPLC-MS/MS analysis of oxylipins across various instrument vendors, guiding the way towards inter-laboratory comparability.

Optimising Palm Olein-Based Betamethasone 17-Valerate Emulsions for Scalable Manufacturing and Stability

Introduction: Palm olein has been used as an excipient in the formulation of topical emulsions due to its rich source of natural antioxidants that can lead to better skin health and higher stability upon storage. Despite its potential as a topical drug delivery vehicle, the practical implementation of manufacturing 20% (w/w) palm olein-in-water emulsions for commercial purposes has not been explored extensively, and obtaining experimental data on scale-up studies would be helpful in facilitating this realisation. Methods: This research work established and optimised the manufacturing process parameters for the production of cream and lotion formulations containing betamethasone 17-valerate, utilising palm olein as the vehicle, with scale-up from lab-scale 5 kg batches to pilot-scale 80 kg batches. Design of experiments (DoE) where response surface methodology as well as three-level, two-factors (32) full factorial design were used to develop statistical models for representing the possible relationships between factors: homogenisation time and speed, and responses: particle size and phase separation. Results: The findings established that the quadratic model was the most suitable model as it could predict the interactions between factors and responses in an accurate manner as well as suggest the optimum operating conditions. The optimum homogenisation time and speed were found to be 40 minutes and 3400 rpm, respectively. These conditions produced emulsions with the smallest particle size (3.2 µm ± 0.03) and the least phase separation value (29.7% ± 0.35). Conclusion: The study successfully demonstrated the potential to scale up the manufacturing of 20% (w/w) palm olein-in-water emulsions for commercial purposes. The optimised parameters, obtained through DoE, facilitate the large-scale production of stable emulsions containing betamethasone 17-valerate.

Lithium and cobalt recovery from spent battery cathode by acid-reductive leaching using molasses: optimisation by response surface methodology

ABSTRACT The combination of molasses and sulphuric acid is reported for the first time as a leaching agent for lithium and cobalt recovery from spent lithium-ion batteries. The acid-reductive leaching process was optimised using a design of experiment with four input variables: sulphuric acid concentration, molasses concentration, leaching temperature, and leaching time. A response surface model with a central composite design determined the number of experiments, followed by quadratic regression modelling to analyze the relationship between input parameters and leaching recovery. ANOVA assessed the statistical significance of each variable, regression modelling, and optimal leaching conditions. The analysis showed temperature significantly affected lithium recovery, while both temperature and sulphuric acid concentration influenced cobalt recovery. The optimal conditions achieved 97.76% lithium and 99% cobalt recovery (2.5 M sulphuric acid, 60 g/L molasses, 54°C, 108 min). These findings highlight an eco-friendly and efficient approach to metal recovery.

Researchers’ perceptions of automating scientific research

Abstract Science is being transformed by the increasing capabilities of automation technologies and artificial intelligence (AI). Integrating AI and machine learning (ML) into scientific practice requires changing established research methods while maintaining a scientific understanding of research findings. Researchers are at the forefront of this change, but there is currently little understanding of how they are experiencing these upheavals in scientific practice. In this paper, we examine how researchers working in several research fields (automation engineering, computational design, conservation decision-making, materials science, and synthetic biology) perceive AI/ML technologies used in their work, such as laboratory automation, automated design of experiments, computational design, and computer experiments. We find that researchers emphasised the need for AI/ML technologies to have practical benefits (such as efficiency and improved safety) to justify their use. Researchers were also hesitant to automate data analysis, and the importance of explainability differed between researchers working with laboratory automation and those using AI/ML directly in their research. This difference is due to the different role AI/ML plays in different research fields: laboratory automation performs processes already defined by the researcher and the actions are visible or recorded, while in AI/ML applications the decisions that produced the result may be obscure to the researcher. Understanding the role AI/ML plays in scientific practice is important for ensuring that scientific knowledge continues to grow.

Limitations of Standard Rain Erosion Tests for Wind Turbine Leading Edge Protection Evaluation

Blade leading edge erosion (LEE) is a persistent challenge in the wind industry, resulting in reduced aerodynamic efficiency and increased maintenance costs, with an estimated total expense of GBP 1.3M over a 25-year turbine lifetime. To mitigate these effects, leading edge protection (LEP) systems are widely used, but their real-world performance often falls short of predictions based on the standard rain erosion test (RET). This study investigates the limitations of current RET practices, which are designed to accelerate testing but fail to replicate the diverse environmental conditions experienced by wind turbines. Two LEPs with contrasting viscoelastic properties were tested using a novel design of experiments (DoEs) approach. The study explored the droplet impact frequency, combination and sequencing of high or low rainfall intensities, recovery during the inspection period and droplet size effects on erosion behaviour, to uncover significant differences in material performance compared to standard RET conditions. Results, supported by dynamic mechanical analysis (DMA), indicated that the chosen LEPs undergo a transition between elastic and brittle failure modes at a critical impact frequency, influenced by the viscoelastic properties of the material. Importantly, the findings emphasise the need for revised testing protocols across a range of parameters that incorporate realistic environmental conditions to improve the predictability of LEP performance.

Manufacturing of Liposomes Using a Stainless-Steel Microfluidic Device: An Investigation into Design of Experiments.

Liposomes are highly beneficial nanocarrier systems due to their biocompatibility, low toxicity, and exceptional inclusiveness, which lead to improved drug bioavailability. For biological applications, accurate control over these nanoparticles' mean size and size distribution is essential. Micromixers facilitate the continuous production of liposomes, enhancing the precision of size regulation and reproducibility. In this research, the performance of a stainless steel 316L micromixer was evaluated by using COMSOL Multiphysics simulations. The liposomes were precisely optimized using design of experiments techniques in a microfluidic setup, and then dexamethasone sodium phosphate (DSP) was successfully encapsulated in liposome nanoparticles. The physicochemical characteristics of liposomes, such as their ζ-potential, size, DSP loading capacity, encapsulation efficiency, and drug release, were assessed. Transmission electron microscopy and dynamic light scattering analysis were used to examine the structures of the liposomes. The drug release kinetics study was conducted to analyze the drug delivery system, and the Higuchi equation was determined to be the most suitable equation. The microfluidic chip was shown to be capable of creating small-sized liposomes with a size as small as 130 nm, exhibiting monodispersed characteristics and low polydispersity liposome populations.

Establishing a scalable perfusion strategy for the manufacture of CAR‐T cells in stirred‐tank bioreactors using a quality‐by‐design approach

AbstractChimeric antigen receptor T cell (CAR‐T) therapies show high remission rates for relapsed and refractory leukemia and lymphoma. However, manufacturing challenges hinder their commercial viability and patient accessibility. This study applied quality‐by‐design principles to identify perfusion critical process parameters for CAR‐T expansion in stirred tank bioreactors to maximize yields. A design of experiments in the Ambr® 250 High Throughput Perfusion small‐scale bioreactor revealed that earlier perfusion starts (48 h vs. 96 h post‐inoculation) and higher perfusion rates (1.0 VVD vs. 0.25 VVD) significantly increased cytotoxic CAR‐T cell yields without compromising critical quality attributes. Optimizing perfusion improved growth kinetics and yields across donor samples, achieving densities &gt;21 × 106 cells/mL in 7 days, outperforming traditional fed‐batch and static flask cultures. This study underscores the importance of optimizing perfusion parameters to maximize CAR‐T yields and quality and highlights the utility of scale‐down models in reducing time, costs and risks associated with process development.

Design of Partial Population Experiments with an Application to Spillovers in Tax Compliance

Abstract We develop a framework to analyze partial population experiments, a generalization of the cluster experimental design where clusters are assigned to different treatment intensities. Our framework allows for heterogeneity in cluster sizes and outcome distributions. We study the large-sample behavior of OLS estimators and cluster-robust variance estimators and show that (i) ignoring cluster heterogeneity may result in severely underpowered experiments and (ii) the cluster-robust variance estimator may be upward-biased when clusters are heterogeneous. We derive formulas for power, minimum detectable effects, and optimal cluster assignment probabilities. All our results apply to cluster experiments, a particular case of our framework. We set up a potential outcomes framework to interpret the OLS estimands as causal effects. We implement our methods in a large-scale experiment to estimate the direct and spillover effects of a communication campaign on property tax compliance. We find an increase in tax compliance among individuals directly targeted with our mailing, as well as compliance spillovers on untreated individuals in clusters with a high proportion of treated taxpayers.

Designing scenario-based experiments in retail SCM: methodological approaches and practical insights

Purpose Considering the transformational impact of technological advances in modern retail on the consumer experience and the associated growth of experimental studies in consumer-centric supply chain management (SCM) research, this paper presents a practical overview of key steps in the design of scenario-based experiments (SBEs) in the context of retail SCM.Design/methodology/approach Following a conceptual approach, this paper discusses essential aspects in the designing process, including the connection to theory, vignette design considerations, experimental checks and ensuring managerial relevance.Findings This paper presents a resource for SCM researchers in their pursuit of designing rigorous, context-focused SBEs in consumer-centric retail SCM research. Major design considerations and potential pitfalls are highlighted.Practical implications A well-designed experiment, including its vignettes, manipulations and checks, offers strong potential to inform actionable guidance for managers in the feasibility, strategy design, customization and consumer segmentation of retail SCM strategies.Originality/valueThis paper connects the steps in the design of SBEs to consumer-centric retail SCM questions, supporting future research in this realm.

Disintegration of Bone Cement Using Pulsating Water Jet: A Comparative Study of Standard and Extended Nozzles

The paper deals with erosion of bone cement using a pulsating water jet at a pressure of 15 MPa, comparing the effectiveness of standard and extended nozzle with the length of 100 mm and diameter d = 0,3 mm. The research addresses the problem of optimizing bone cement removal techniques, which is critical for various medical applications, including revision surgeries and bone cement removal. The study employs an design of experiments where bone cement samples are subjected to erosion using a pulsating water jet system equipped with both standard and specially designed extended nozzles. Key parameters such as maximal depth, groove width and volume rate were measured and analyzed. Findings indicate that the extended nozzle significantly enhances the erosion process, achieving higher material removal rates and smoother surface finishes compared to the standard nozzle. The results demonstrate the potential of the extended nozzle design in improving the efficiency and precision of bone cement removal, offering valuable insights for medical practitioners and researchers in the field of orthopedic surgery.

Sensitivity Analysis of Unmanned Aerial Vehicle Composite Wing Structural Model Regarding Material Properties and Laminate Configuration

This study optimizes the structural design of a composite wing shell by minimizing mass and maximizing the first natural frequency. The analysis focuses on the effects of polyvinyl chloride (PVC) foam thickness and the fiber orientation angle of the inner carbon layers, with the outer layers fixed at ±45° for torsional rigidity. A Multi-Objective Genetic Algorithm (MOGA), well suited for complex engineering problems, was employed alongside Design of Experiments to develop a precise response surface model, achieving predictive errors of 0% for mass and 2.99% for frequency. The optimal configuration—90° and 0° fiber orientations for the upper and lower layers and a foam thickness of 1.05 mm—yielded a mass of 412 g and a frequency of 122.95 Hz. These findings demonstrate the efficacy of MOGA in achieving innovative lightweight aerospace designs, striking a balance between material efficiency and structural performance.

Optimization of Enterovirus-like Particle Production and Purification Using Design of Experiments

Hand, foot, and mouth disease (HFMD) represents an emerging health concern whose main causative agents are Coxsackievirus A6 (CVA6) and enterovirus A71 (EV71). The lack of a CVA6 vaccine and the rise of new HFMD-causing strains due to the containment of established HFMD-causing viruses necessitates the search for alternative vaccine technologies, including virus-like particle (VLP) vaccine candidates. While studies have demonstrated that production of enterovirus-like particles in various organisms can be achieved by expression of the viral P1 structural proteins and the 3CD protease, optimization based on the interplay between the three most commonly altered infection parameters (multiplicity of infection (MOI), viable cell density at the time of infection (VCD), and the infection period) is often not investigated. To address this challenge, we have performed Design of Experiments (DoE) to optimize the production of CVA6 and EV71 VLPs. Our results indicate that CVA6 VLP production peaks at high MOI, high VCD, and long infection periods. Our subsequent downstream purification processes yielded 38 mg and 158 mg of purified CVA6 and EV71 VLPs from 1 L crude harvest, respectively. This translates into thousands of potential vaccine doses and highlights the economic potential of enterovirus-like particles for vaccine purposes.

Qualitative evaluation of kerf taper angle of conventionally drilled holes in glass fiber epoxy composite

This study analyses the impact of process parameters on Glass Fiber Reinforced Polymer (GFRP) components produced through conventional drilling on a CNC drilling machine (VMC). GFRP’s challenging machinability is due to its high strength-to-weight ratio, durability, and customizable mechanical properties. The study aims to minimize kerf angles in drilled holes, identifying optimal parameter combinations based on Signal-to-Noise ratios. The best combination of parameters that used to reduce the value of the kerf angle was considered at 2,800 RPM spindle speed and 1,200 mm/rev feed rate. The experimental work reveals that the spindle speed significantly affects the kerf angle, while the feed rate and spindle speed both influence the variation in the kerf angle and show the best results at the ideal values of the two parameters. The study details an analysis of the process parameters of drilled holes on kerf angle in multiple holes in the specimen, applying Taguchi’s design of experiments and analysis of variance. Comparison of findings was made with existing literature on determination of the importance of spindle speed and feed rate for the determination of kerf angle. The best results were obtained when all these parameters set to the ideal values, where the minimum angular kerf of 0.1145° was observed.

Applicationof Full Factorial Planning to Control Nitrogen in a Water Resource

Objective: The aim of this work is to show the application of Full Factorial Planning to an environmental problem. Theoretical framework: Design of experiments is defined as a set of statistical techniques applied to the planning, conduct, analysis and interpretation of controlled tests, seeking to find (define) the factors that influence the values of a parameter or a group of parameters. Its basic principle makes it possible to vary all the levels of all the discrete or continuous variables (called factors) at once, for each experiment, in a programmed and rational way (Peixoto et al., 2008). Method: Experiments were carried out in an industrial plant in the south of Rio de Janeiro and the data was treated using Full Factorial Design. Final Considerations: It can be concluded that full factorial design is a very viable option for obtaining the optimum values or the optimum range of values to obtain the best possible response variable in a study. In this specific case of the company, it can be concluded that Line 1 and Line 3 are the most responsible for the concentration of nitrogen oxide discharged into the river and, therefore, the n-waste should be pre-treated. Implications of the research: The use of Factorial Planning is multiplying in the scientific literature and is proving to be highly effective in dealing with data where the assumptions of Normality are not confirmed. Originality/value: Despite being well-known statistical tools, Factorial Designs are widely used and can bring innovations to their application, as in the case of the company in question.

Optimal design of experiments in the context of machine-learning inter-atomic potentials: improving the efficiency and transferability of kernel based methods

Abstract Data-driven machine learning (ML) models of atomistic interactions are often based on flexible and non-physical functions that can relate nuanced aspects of atomic arrangements to predictions of energies and forces. As a result, these potentials are only as good as the training data (usually the results of so-called ab initio simulations), and we need to ensure that we have enough information to make a model sufficiently accurate, reliable and transferable. The main challenge stems from the fact that descriptors of chemical environments are often sparse, high-dimensional objects without a well-defined continuous metric. Therefore, it is rather unlikely that any ad hoc method for selecting training examples will be indiscriminate, and it is easy to fall into the trap of confirmation bias, where the same narrow and biased sampling is used to generate training and test sets. We will show that an approach derived from classical concepts of statistical planning of experiments and optimal design can help to mitigate such problems at a relatively low computational cost. The key feature of the method we will investigate is that it allows us to assess the quality of the data without obtaining reference energies and forces—a so-called offline approach. In other words, we are focusing on an approach that is easy to implement and does not require sophisticated frameworks that involve automated access to high performance computing.