Design Of Experiments Approach Research Articles

Analysis Effect of Parameters of Genetic Algorithm on a Model for Optimization Design of Sustainable Supply Chain Network Under Disruption Risks

Abstract Over the last decade, our world exposed to many types of unpredictable disasters (recently Coronavirus). These disasters have clearly shown the uncertainty and vulnerability of supply chain systems. Also, it confirmed that adopting Just-in-Time (JIT) strategy to reduce the logistic chain cost may lead to inbuilt complexity and risks. Efficient tools are therefore needed to make complexity optimized supply chain decisions. Evolutionary algorithms, including genetic algorithms (GA), have proven effective in identifying optimal solutions that address the trade-offs between total supply chain cost and carbon emissions regulatory policy represented by carbon tax charges. These solutions pertain to the design challenges of supply networks exposed to potential disruption risks. However, GA have a set of parameters must be chosen for effective and robust performance of the algorithms. This paper aims to set the most suitable values of these parameters that used via GA – ased optimization cost and risk reduction model in firms using a JIT as a delivery system. The model has been conceptualized for addressing the design complexities of the supply chain, referred to as SCRRJITS (Simultaneous Cost and Risk Reduction in a Just-in-Time System). A complete analysis of the different parameters and operators of the algorithm is carried out using design of experiments approach. The algorithm performance measure used in this study is convergence of solutions. The results show the extent to which the quality of solution can be changed depending on selection of these parameters.

Enhancing aircraft crack repair efficiency through novel optimization of piezoelectric actuator parameters: A design of experiments and adaptive neuro-fuzzy inference system approach

This study addressed the critical problem of repairing cracks in aging aircraft structures, a safety concern of paramount importance given the extended service life of modern fleets. Utilized a finite element (FE) method enhanced by the design of experiments (DOE) and adaptive neuro-fuzzy inference system (ANFIS) approaches to analyze the efficacy of piezoelectric actuators in mitigating stress intensity factors (SIF) at crack tips—a novel integration in structural repair strategies. Through simulations, we examined the impact of various factors on the repair process, including the plate, actuator, and adhesive bond size and characteristics. In this work, initially, the SIF estimation used the FE approach at crack tips in aluminum 2024-T3 plate under the uniform uniaxial tensile load. Next, numerous simulations have been performed by changing the parameters and their levels to collect the data information for the analysis of the DOE and ANFIS approach. The FE simulation results have shown that changing the parameters and their levels will result in changing of SIF. Several DOE and ANFIS optimization cases have been performed for the depth analysis of parameters. The current results indicated that optimal placement, size, and voltage applied to the piezoelectric actuators are crucial for maximizing crack repair efficiency, with the ability to significantly reduce the SIF by a quantified percentage under specific conditions. This research surpasses previous efforts by providing a comprehensive parameter optimization of piezoelectric actuator application, offering a methodologically advanced and practically relevant pathway to enhance aircraft structural integrity and maintenance practices. The study innovation lies in its methodological fusion, which holistically examines the parameters influencing SIF reduction in aircraft crack repair, marking a significant leap in applying intelligent materials in aerospace engineering.

Sheet Metal Forming Analysis of Aluminium Alloy AA6061 using |Altair INSPIRE Form Simulation

Altair INSPIRE Form is a computer simulation developed for designing and modelling sheet metal forming processes. Within the modules, the design and formability prediction can be simulated and optimized. Thus, this study focuses on analysing the formability of sheet metal Aluminium alloy AA6061 by simulating numerically a multistage forming of an end-wall process where the material properties and process parameters gained from the literature were used as input. The geometrical model was validated by comparing its formability with the literature that was simulated by Altair HyperForm. Then, to statistically analyse the influence of forming parameters on formability, Analysis of Variance (ANOVA) was applied with the design of experiment approach using the Response Surface Methodology (RSM). The simulation results observed similar strain rate distributions between Inspire Form and HyperForm under a safe forming process. For the formability of Aluminium alloy AA6061, loose metal zones with wrinkles, excessive thinning up to 23.13%, and the maximum equivalent stress at 414.6 MPa were expected to be generated. These findings clearly explained that forming parameters such as blank holder force and punch velocity have to be carefully controlled for this ductile material. The ANOVA shows a significant influence of coefficient friction and punch velocity on thinning percentages. The likelihood of tearing to form at a higher thinning percentage and its appearance was observed in the Forming Limit Diagram which evaluates its formability. The validation of optimized parameters by simulation work is concluded to be successful as no fracture appeared in this simulation with a small percentage error at 2.19% between the simulation and the predicted value.

Optimisation of hybrid process to recover zinc from electric arc furnace dust using design of experiment approach

ABSTRACT Recoverable metals like zinc and iron are present in the dust from electric arc furnaces (EAF), a byproduct of the steelmaking process. These metals are mainly present as zinc ferrite (ZnFe2O4), magnetite (Fe3O4), and zinc oxide (ZnO) compounds. ZnO is easily dissolved in either an acidic or an alkaline solution; however, ZnFe2O4 is more difficult to dissolve. Therefore, our earlier paper suggested the thermodynamic analysis of a preliminary caustic-roasting treatment to transform the ZnFe2O4 from EAF dust into more soluble products of ZnO and NaFeO2. Finally, the present study carried out a hybrid process that involved the caustic roasting of EAF dust and a caustic leaching of the roasted product. At first, the effects of caustic-roasting process parameters on zinc-ferrite conversion were studied using the Taguchi method. The implication of Design of Experiment (DoE)-Taguchi analysis and the analysis of variance (ANOVA) shows the optimum caustic-roasting conditions are: roasting temperature of 350 oC, NaOH/ EAF dust molar ratio of 4, and roasting time of 60 minutes. The effect of temperature, reaction time, NaOH concentration, and solid/liquid (S/L) ratio was also examined using the same method in the subsequent caustic-leaching process. Similarly, the analysis shows that maximum Zn dissolution is possible at a NaOH concentration of 6M, a leaching temperature of 40 oC, a liquid-to-solid ratio of 60 ml/g, and a leaching time of 60 minutes. After the hybrid process, the zinc recovery was 83%, while most iron oxides remained in the leach residue.

Optimizing Capacitive Pressure Sensor Geometry: A Design of Experiments Approach with a Computer-Generated Model.

This study presents a comprehensive investigation into the design and optimization of capacitive pressure sensors (CPSs) for their integration into capacitive touch buttons in electronic applications. Using the Finite Element Method (FEM), various geometries of dielectric layers were meticulously modeled and analyzed for their capacitive and sensitivity parameters. The flexible elastomer polydimethylsiloxane (PDMS) is used as a diaphragm, and polyvinylidene fluoride (PVDF) is a flexible material that acts as a dielectric medium. The Design of Experiment (DoE) techniques, aided by statistical analysis, were employed to identify the optimal geometric shapes of the CPS model. From the prediction using the DoE approach, it is observed that the cylindrical-shaped dielectric medium has better sensitivity. Using this optimal configuration, the CPS was further examined across a range of dielectric layer thicknesses to determine the capacitance, stored electrical energy, displacement, and stress levels at uniform pressures ranging from 0 to 200 kPa. Employing a 0.1 mm dielectric layer thickness yields heightened sensitivity and capacitance values, which is consistent with theoretical efforts. At a pressure of 200 kPa, the sensor achieves a maximum capacitance of 33.3 pF, with a total stored electric energy of 15.9 × 10-12 J and 0.468 pF/Pa of sensitivity for 0.1 dielectric thickness. These findings underscore the efficacy of the proposed CPS model for integration into capacitive touch buttons in electronic devices and e-skin applications, thereby offering promising advancements in sensor technology.

Modelling of a cargo oil pump turbine system to find the optimum pump operation capacities through the design of experiments approaches

ABSTRACT This study aims to thoroughly understand and analyze the Cargo Oil Pump Turbine (COPT) system on tanker ships through thermodynamic and numerical simulations. The goal is to find the best pump capacities to match different cargo discharge rates and properties. A parametric study has been performed on the COPT system by building a thermodynamic model including exergy analysis, and a numerical centrifugal pump model. The design of experiment applications which are factorial design and response surface methodology (RSM) have determined the the optimal pump capacity The findings demonstrate that the risky flow rate region for the cavitation, if the density and vapour pressure are close to limits, is between 3050-3500 m3/h. The factorial design finds the best operating flow rate for the high capacity demands at 4025 m3/h where the efficiencies are the highest, and the RSM calculates the optimum capacity for the low demands at 1460.34 m3/h.

Identification of volatile and non-volatile migrants released during sous vide cooking by UPLC-IMS-QTOF and DI-SPME-GC-MS using a design of experiments approach

The rise of new culinary advances combined with science has made it possible to find new optimum methods of cooking, such as vacuum cooking (Sous Vide), that allow achieving pleasant textures, preserving the genuine nature of the food and also its original flavour. Sous Vide involves the direct contact of plastic materials with food during cooking, causing the possible release of chemical compounds that may alter the properties of the food and pose a risk to human health. In this work, the migration kinetics that takes place during this cooking process have been estimated thanks to a design of experiments (DoE). DoE has been applied to 4 samples of vacuum cooking bags containing 3 food simulants (ethanol 10%, acetic acid 3% and isooctane) under typical cooking conditions of the culinary technique. The study also allowed the identification of the main volatile and non-volatile migrants that can be transferred from this particular packaging. Twenty-nine non-volatile species were identified by ultra-high performance liquid chromatography coupled to a quadrupole-time-of-flight ion-mobility separation mass spectrometer (UPLC-IMS-QTOF) and sixty-four volatile species were identified by solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS). Furthermore, a comprehensive literature analysis was conducted to explore the applications, origins, and potential toxicity of these compounds.

Development and evaluation of hot-melt–extruded diquafosol tetrasodium formulations for ophthalmic inserts: A design of experiments approach

This study aimed to develop, optimize, and evaluate hot-melt–extruded ophthalmic inserts capable of sustained release of diquafosol tetrasodium (DQS) via a design of experiments approach. DQS, a tear stimulant for dry eye management, faces challenges of frequent administration and low bioavailability. The developed insert uses biodegradable polymers in varied proportions to achieve sustained release. Optimized through mixture design, the insert completely dissolved within 24 h and maintained a stable drug content, thickness, and surface pH over three months at room temperature. In vitro corneal permeation studies on excised rabbit corneas demonstrated increased bioavailability, suggesting a reduced dosing frequency compared with conventional eye drops. Therefore, this insert has potential to enhance treatment outcomes by improving patient compliance and providing sustained drug effects.

Performance improvement of set of worm gears used in soot blower through profile modification

The present design of a set of worm gears used in a soot blower produced by a certain manufacturer has an efficiency of 68.8%. A soot blower is one of the most critical components in industrial applications for removing the large amounts of soot generated by boilers and is required to be operational 24×7. The energy consumption of the soot blower depends on its working efficiency and ultimately the design of its set of worm gears. This paper focuses mainly on the design and analysis of available industrial worm-gear sets used in soot blowers. The theoretical, experimental, and finite-element analysis approaches are validated for the stability of the worm gear set under typical input conditions. This paper also describes an analytical design of experiments (DOE) approach to identify the most significant factor for performance (efficiency) improvement and suggests some design improvements for the worm gear set using the profile modification approach. These ensure the efficiency improvement of the current industrial design of the set of worm gears used in a soot blower. The analytical DOE approach helped identify that the number of worm wheel teeth (Z2) and gear module (m) are the two most significant factors affecting performance. Accordingly, based on the improved design, the final efficiency increased from 68.8% to 74.6% (∼8.5% increment), resulting in lower power consumption during industrial application.

Impact of probiotics, prebiotics, and synbiotics on digestive enzymes, oxidative stress, and antioxidant defense in fish farming: current insights and future perspectives

There has been a surge of research in the aquaculture industry investigating probiotic, prebiotic, and synbiotic interventions on the physiological mechanisms of fish, specifically digestive enzymes, oxidative stress, and antioxidant defense. In fish, probiotics have been shown to improve nutrient utilization and growth performance by stimulating digestive enzymes. Meanwhile, probiotics, prebiotics and synbiotics have also been studied for their ability to modulate oxidative stress and antioxidant defense mechanisms in fish, highlighting their multifaceted health benefits. This review identified current trends, research gaps, and future considerations in this evolving field. Although promising findings have been made, a significant research gap exists in understanding the specific role of probiotics prebiotics, and synbiotics in modulating digestive enzymes, oxidative stress, and antioxidant defense systems in a variety of fish species. As this study investigate into the existing body of literature, it becomes evident that while certain aspects of these interactions have been elucidated, a nuanced and comprehensive understanding still needs to be discovered. The variations in experimental design, species-specific responses, and the lack of standardized methodologies contribute to the complexity of the field. Digestive physiology and antioxidant defense mechanisms vary among different fish species, so future research should focus on species-specific responses to probiotic, prebiotic, and synbiotic formulations. It will also be possible to establish robust correlations between dietary interventions and observed effects through a systematic experimental design and methodology approach. Accordingly, further research is needed to understand the interactions between probiotics, prebiotics, and synbiotics in fish and digestive enzymes, oxidative stress, and antioxidant defense. Identifying research gaps and adopting standardized methodologies can help develop tailored strategies to optimize aquaculture fish health and growth performance.

An Investigation into the Effects of Processing Factors on the Properties and Scaling-Up Potential of Propranolol-Loaded Chitosan Nanogels.

Chitosan-triphosphate (TPP) nanogels are widely studied drug delivery carrier systems, typically prepared via a simple mixing process. However, the effects of the processing factors on nanogel production have not been extensively explored, despite the importance of understanding and standardising such factors to allow upscaling and commercial usage. This study aims to systematically evaluate the effects of various fabrication and processing factors on the properties of nanogels using a Design of Experiment approach. Hydrodynamic size, polydispersity index (PDI), zeta potential, and encapsulation efficiency were determined as the dependent factors. The temperature, stirring rate, chitosan grade, crosslinker choice, and the interaction term between temperature and chitosan grade were found to have a significant effect on the particle size, whereas the effect of temperature and the addition rate of crosslinker on the PDI was also noteworthy. Moreover, the addition rate of the crosslinker and the volume of the reaction vessel were found to impact the encapsulation efficiency. The zeta potential of the nanogels was found to be governed by the chitosan grade. The optimal fabrication conditions for the development of medium molecular weight chitosan and TPP nanogels included the following: the addition rate for TPP solution was set at 2 mL/min, while the solution was then stirred at a temperature of 50 °C and a stirring speed of 600 rpm. The volume of the glass vial used was 28 mL, while the stirrer size was 20 mm. The second aim of the study was to evaluate the potential for scaling up the nanogels. Size and PDI were found to increase from 128 nm to 151 nm and from 0.232 to 0.267, respectively, when the volume of the reaction mixture was increased from 4 to 20 mL and other processing factors were kept unchanged. These results indicate that caution is required when scaling up as the nanogel properties may be significantly altered with an increasing production scale.

Solubilization Improvement using Non-Ionic Surfactants and Phospholipids Optimized by An Experimental Design: Case of Albendazole

The solubilization of insoluble or poorly water-soluble drugs is one of the major factors taken into account in the formulation of oral solutions and parenteral forms. The goal of this study is to evaluate the utility of mixture design in enhancement water solubility of Albendazole by the use of phospholipids and nonionic surfactants that are well tolerated by the parenteral route.in this work, a design of experiments approach was tested using a mixture design of nonionic surfactants (tween® 80 and Solutol® HS 15), A phospholipid (Lipoid® S75) and Albendazole. Solubility determination was based on the analysis of samples absorbance at 308nm. A particles size measurement was conducted using a dynamic light scattering at the point showing the maximum of solubility. these experiments show an improving of solubility, up to 90% with runs 1, 3 and 6 compared to the solubility of albendazole in water, without excipients, examined by run 7. Indeed, the analysis of the design space showed that the solubility of albendazole is affected by the association of tween® 80, Solutol® HS 15 and Lipoid® S75. Run 3 containing 1% of albendazole, 1,5% of tween® 80, 1, 5% of Solutol® HS 15 and 0,38% of Lipoid® S75 and water q.s. for 100% w/w improved albendazole solubility by about 90%, and showed an average particles size of 11nm.

An optimization-free Fisher information driven approach for online design of experiments

Developing mathematical models used to elucidate reaction kinetics plays a crucial role in the design, control, and optimization of chemical processes. One of the most challenging tasks in kinetic model identification is the precise estimation of unknown kinetic model parameters. This challenge can be effectively addressed through the application of Model-Based Design of Experiments (MBDoE) techniques, which enable the design of experiments facilitating precise model parameter estimation with minimal runs and analytical resources. Nevertheless, MBDoE techniques rely on an optimization procedure that is susceptible to parametric uncertainty, making the design procedure computationally intensive and prone to issues of local optimality. MBDoE techniques are also employed in online procedures to expedite the identification of kinetic models in autonomous platforms. As a result, ensuring rapid convergence becomes imperative to mitigate numerical issues during operational processes. In this paper a Fisher Information Matrix Driven (FIMD) approach is introduced to tackle these challenges. The methodology integrates a sampling-based experimental design approach with experiment ranking based on FIM to select the most informative experiment at each iteration. The effectiveness of the proposed design methodology is examined and discussed via two different case studies of increasing complexity: a fed-batch reactor in which the fermentation of baker’s yeast is carried out and a nucleophilic aromatic substitution in a flow reactor system.

Experimental design approach for the quantitative analysis of multicomponents by single marker and HPLC fingerprinting of Thunbergia laurifolia aqueous extract.

Thunbergia laurifolia is used in traditional Thai medicine to reduce fever and treat mouth ulcers. However, the quantitative analysis of chemical markers has not yet been officially defined. The objective of this study is to develop a high-performance liquid chromatography (HPLC) method using a design of experiment (DoE) for the quantitative analysis of multicomponents by single marker (QAMS) and fingerprinting of the T. laurifolia aqueous extract. Critical variables were screened using a two-level fractional factorial design, followed by the optimization of the selected variables using a central composite design. The validated method was applied for quality assessment based on QAMS and fingerprinting of the extract. Optimum conditions of DoE for the analysis of caffeic acid, vicenin-2, and rosmarinic acid were determined. The relative correction factors for caffeic acid and vicenin-2 were calculated using rosmarinic acid as an internal reference standard, and their contents in 30 samples were determined. The differences between the external standard method (ESM) and QAMS were compared. No significant difference was observed in the quantitative determination, proving the consistency QAMS and ESM. HPLC fingerprints of T. laurifolia were established with 8 of 12 characteristic peaks that were structurally characterized using HPLC-diode array detection-electrospray ionization/tandem mass spectrometry. The similarity of the fingerprints in all samples was ≥0.74, and the pattern recognition of the characteristic peaks was satisfied. The proposed method efficiently detected multiple components of the T. laurifolia extract. Thus, the method is beneficial in providing references for enhancing the quality control of other herbal medicines.

Evolving Improved Sampling Protocols for Dose–Response Modelling Using Genetic Algorithms with a Profile-Likelihood Metric

Practical limitations of quality and quantity of data can limit the precision of parameter identification in mathematical models. Model-based experimental design approaches have been developed to minimise parameter uncertainty, but the majority of these approaches have relied on first-order approximations of model sensitivity at a local point in parameter space. Practical identifiability approaches such as profile-likelihood have shown potential for quantifying parameter uncertainty beyond linear approximations. This research presents a genetic algorithm approach to optimise sample timing across various parameterisations of a demonstrative PK-PD model with the goal of aiding experimental design. The optimisation relies on a chosen metric of parameter uncertainty that is based on the profile-likelihood method. Additionally, the approach considers cases where multiple parameter scenarios may require simultaneous optimisation. The genetic algorithm approach was able to locate near-optimal sampling protocols for a wide range of sample number (n = 3–20), and it reduced the parameter variance metric by 33–37% on average. The profile-likelihood metric also correlated well with an existing Monte Carlo-based metric (with a worst-case r > 0.89), while reducing computational cost by an order of magnitude. The combination of the new profile-likelihood metric and the genetic algorithm demonstrate the feasibility of considering the nonlinear nature of models in optimal experimental design at a reasonable computational cost. The outputs of such a process could allow for experimenters to either improve parameter certainty given a fixed number of samples, or reduce sample quantity while retaining the same level of parameter certainty.

Integrated Approach of Life Cycle Assessment and Experimental Design in the Study of a Model Organic Reaction: New Perspectives in Renewable Vanillin-Derived Chemicals.

This work is focused on performing a quantitative assessment of the environmental impacts associated with an organic synthesis reaction, optimized using an experimental design approach. A nucleophilic substitution reaction was selected, employing vanillin as the substrate, a phenolic compound widely used in the food industry and of pharmaceutical interest, considering its antioxidant and antitumoral potential. To carry out the reaction, three different solvents have been chosen, namely acetonitrile (ACN), acetone (Ace), and dimethylformamide (DMF). The syntheses were planned with the aid of a multivariate experimental design to estimate the best reaction conditions, which simultaneously allow a high product yield and a reduced environmental impact as computed by Life Cycle Assessment (LCA) methodology. The experimental results highlighted that the reactions carried out in DMF resulted in higher yields with respect to ACN and Ace; these reactions were also the ones with lower environmental impacts. The multilinear regression models allowed us to identify the optimal experimental conditions able to guarantee the highest reaction yields and lowest environmental impacts for the studied reaction. The identified optimal experimental conditions were also validated by experimentally conducting the reaction in those conditions, which indeed led to the highest yield (i.e., 93%) and the lowest environmental impacts among the performed experiments. This work proposes, for the first time, an integrated approach of DoE and LCA applied to an organic reaction with the aim of considering both conventional metrics, such as reaction yield, and unconventional ones, such as environmental impacts, during its lab-scale optimization.

Abrasive waterjet cutting of r‐GO infused magnesium fiber metal laminates: Experimental investigations and optimization through gorilla troops algorithm

AbstractLaminated metal‐composite structures, known as fiber metal laminates (FMLs), are modern lightweight materials extensively utilized in diverse industries such as aerospace and automotive manufacturing. These materials offer enhanced impact and fatigue resistance, making them invaluable for various applications. However, machining FMLs poses a challenge due to the occurrence of delamination and heterogenous in nature during conventional methods. Therefore, this study aims to investigate the quality characteristics such as kerf width, surface roughness and kerf taper of Magnesium based FMLs processed with an unconventional machining process namely abrasive water jet cutting (AWJC). These FMLs comprise alternately stacked kevlar/carbon fibers adhesively bonded with epoxy resin matrix filled with varying wt% of reduced graphene oxide (r‐GO), combined with AZ31B alloy sheet as the skin material. AWJC experiments were performed by varying the process parameters including waterjet pressure (275–325 MPa), stand‐off distance (2.5–3.5 mm), and cutting speed (600–800 mm/min) based on the box–behnken experimental design. The findings from the statistical analysis underscore the significant influences of stand‐off distance and waterjet pressure on kerf characteristics, while the inclusion of r‐GO is observed to notably affect the surface quality. In pursuit of enhancing cut quality, a multi‐response optimization was conducted employing the Gorilla Troops Optimization (GTO) algorithm. Comparative analysis with established metaheuristics like the gray wolf algorithm, dragonfly algorithm, and harmony search algorithm reveals GTO's superior performance across various metrics, including rapid convergence, diversity, and spacing values. Further, the cutting‐induced damages such as matrix plowing, fiber‐metal debonding and composite delamination were observed through microstructure analysis.Highlights Magnesium (AZ31B) fiber metal laminate comprising with various wt% of reduced graphene oxide was fabricated. Abrasive waterjet cutting of FMLs were performed based on box–behnken design experimental approach. Statistical analysis and influence of process parameters on the kerf width, surface roughness and kerf taper were investigated. Multi‐response optimization was carried out using metaheuristic‐based gorilla troops algorithm.

Controlled Enzymatic Synthesis of Polyesters Based on a Cellulose-Derived Triol Monomer: A Design of Experiment Approach.

Regioselective enzymatic polycondensation of the bio-based cellulose derived polyol, Triol-citro, and dimethyl adipate using Candida antarctica Lipase B (CaLB) was investigated. A Design of Experiment approach with MODDE® Pro 13 was used to determine important factors in the branching behavior of this polymer, and reactant ratio, temperature, reaction time and enzyme wt % were the studied factors. Multifunctional polyesters with pendant hydroxy groups were synthesized and fully characterized using 2D NMR techniques to determine degree of branching. Branching was minimal, with a maximum of 16 % observed, and monomer ratio, temperature and reaction time were all determined to be significant factors. In this work, Mn of up to 13 kDa were achieved, while maintaining degree of branching below 15 %, resulting in a linear polyester with the potential to be further functionalized.

Enhancing Dimensional Accuracy in Fused Filament Fabrication: A DOE Approach

3D printing is widely used in various fields and industries and is one of the most popular methods. Most popular advantage of this method is its flexibility in printing complex shapes and sizes. Quality is one of the important aspects when using this kind of technology which has a high advantage of printing complex shapes as this includes many factors like temperature, speed, material, infill types etc. In this paper one such analysis has been done by 3D printing parts under different factors. Analysis is done on how scaling factors affect the dimensions like height and diameter of a 3D printed part and check factors affecting these variations using Design of Experiments (DOE) analysis

Influence of defect geometry on putty performance in pipeline composite repair assessments

The effective repair of pipelines is crucial to ensuring the integrity of infrastructure. However, the effect of defect geometries on the efficiency of pipeline composite repair systems is a major concern in the industry. This study investigated the effects of geometric properties on the performance of composite repaired pipes and putty components in the context of the efficiency of composite repair systems using parametric analysis with various defect geometries as well as two putty formulations. The study involved the development of a finite element model and the analysis of numerical simulations based on a statistical experimental design matrix. Specifically, a design of experiments approach with a specific emphasis on response surface methodology utilizing the Box–Behnken design was employed to identify factor settings tailored to different defect geometries. The analysis revealed that defect depth, length, and width had a significant negative impact on the strength of putty. Defect depth had a greater impact on the putty performance and steel pipe burst pressure compared to defect length and width. However, defect length and width had mixed influences on putty performance, with different geometries resulting in different responses for both types of putty, indicating the existence of complex interactions between these two parameters. The strength capacity of Putty-A in the repair system was significantly influenced by the interaction between defect width, depth, and length, while Putty-B, the interaction was more significant when it came to length and width of the defect. Further statistical analysis confirmed the individual significance of defect depth, length, and width, as well as their interactions on putty strength capacity. The increased sensitivity of Putty-A to changes in defect geometry compared to Putty-B introduces further complexity to material considerations. These findings highlight the importance of selecting appropriate putty properties depending on the defect geometry for effective pipeline repair. This research provides valuable insights that will guide material selection and the development of new putty material, improving the resilience and reliability of future pipeline repair technologies.