Composite Design Optimization Research Articles

Model Adequacy in Assessing the Predictive Performance of Regression Models in Pharmaceutical Product Optimization: The Bedaquiline Solid Lipid Nanoparticle Example

This study aimed to assess the predictive performance of first- and second-order regression models in optimizing bedaquiline (BQ) solid lipid nanoparticle (SLN) formulations. A three-step central composite design and graphical optimization process was employed. A design of experiments method was used to evaluate the impact of BQ, Tween 80 (T80), polyethylene glycol (PEG), and lecithin on the formulations’ response variables, including Z-average (PSD), polydispersibility index (PdI), and Zeta potential (ZP). Secondly, we quantified the relationship between experimental variables using the regression model coefficients. Lastly, we predicted the responses and verified the models’ adequacies to ensure accurate representation and effective optimization. The first-order polynomial showed poor model adequacy and required further refinement due to its lack of explanatory power and significant predictors. Conversely, the second-order models provided superior fitness, sensitivity to variability, complexity, and prediction consistency. The optimized formulation achieved a desirability value of 0.9998, indicating alignment with the desired criteria. Specifically, the levels of BQ (19.4 mg), T80 (25.2 mg), PEG (39.2 mg), and lecithin (200 mg) corresponded to PdI (0.41), PSD (250.99 nm), and ZP (−25.95 mV). Maintaining a BQ concentration between 10 and 20% and T80 between 15 and 18% is vital for maximizing ZP and minimizing PdI and PSD, ensuring stable SLN formulations. This study underscores the significance of precise model selection and statistical analysis in pharmaceutical formulation optimization for enhanced drug delivery systems.

COMPOSITION DESIGN OF 316LN AUSTENITIC STAINLESS STEEL FOR LIQUID-HYDROGEN STORAGE BASED ON HIGH-FLUX PREPARATION

Designing multi-element alloy compositions to achieve target performance was the first step in the development of modern materials, but the traditional trial-and-error experiment seriously restricted the development of new materials due to its low efficiency. In this investigation, the composition design of 316LN austenitic stainless steel for enhanced liquid-hydrogen storage using multi-crucible synchronous metallurgy in a high-flux experiment was proposed. Sixteen groups of as cast austenitic stainless steel samples with different compositions were smelted using high-flux material preparation. Then, through the observation of their microstructures, the chemical composition that best matched the target performance was finally selected. The results show that high-throughput experiments can greatly improve the efficiency of composition design optimization of new stainless steel products. At the same time, this investigation also analyzed the elemental composition of δ-ferrite and the method for effectively controlling the δ-ferrite content. In 316LN stainless steel, Cr and Mo were easily enriched in ferrite grains or grain boundaries, forming Cr and Mo enriched regions. This resulted in a gradient transition of Cr and Mo from the ferrite region to the austenite region, forming galvanic corrosion. In this study, the distribution of Cr, Mo and other elements in 316LN stainless steel was studied by means of a metallographic microscope, electron probe microanalysis, transmission electron microscope and scanning electron microscope. In addition, the relationship between the ferrite content and chemical composition was explored. Finally, it was determined that high-temperature, long-term sensitization treatment is an effective method for controlling the δ-ferrite content.

Design optimization of continuous fiber composites with thermo-mechanical coupling and load uncertainties

This paper introduces a theoretical framework for the design optimization of continuous fiber composites reinforced with continuous fiber trajectories subject to thermo-mechanical coupling and load uncertainties. Different uniform temperature variations are applied in the structure to investigate the influence of ambient temperature change on the structural performance. To consider the external load uncertainties, a robust design optimization model is proposed where the loads are modeled as hybrid variables, namely magnitudes as random variables and directions as interval variables, with the robust objective determined through a hybrid orthogonal polynomial expansion method. Furthermore, we use a level-set function to represent the structural boundary, with its evolution driven by shape derivatives calculated based on uncertainty analysis. The continuous fiber paths are subsequently determined by the level-set isoline extracted from the structural boundary, which in turn influences the structural mechanical performance due to the material anisotropy of composites. The continuity of continuous fiber and the equal space between adjacent trajectories largely ensure the additive manufacturability of the composites. Three numerical examples are presented to demonstrate the effectiveness of the developed framework. The results show that the ambient temperature variations and load uncertainties largely impact the optimized topology and fiber infill patterns of composites, thus are important to be considered in the design stage. Moreover, the optimized structure can have a 5-fold stiffness per unit mass compared with the initial design thus largely increasing the material efficiency in carrying external uncertain loads.

Concurrent multi-scale design optimization of fiber-reinforced composite material based on an adaptive normal distribution fiber optimization scheme for minimum structural compliance and additive manufacturing

Structural lightweight is a core technical requirement for the structural design of aerospace and new energy power equipment structures. For multi-scale variable stiffness design optimization of discrete fiber-reinforced composite laminates, one of the challenges is how to avoid the explosion of design variable combinations caused by the increase in the number of candidate discrete fiber laying angles. The Normal Distribution Fiber Optimization (NDFO) interpolation scheme has the numerical advantage that the number of design variables does not increase with an increase in the number of candidate discrete fiber laying angles. However, the traditional NDFO interpolation scheme uses uniform penalty parameters across all elements, which means that normalizing the penalty parameters for all the elements ignores the convergence differences of discrete fiber laying angles in different elements within the macro-scale structure topology. This leads to time-consuming and unstable optimization iteration of the macro-scale structural topology and micro-scale discrete fiber laying angle selection. Especially, it easily causes the micro-scale discrete fiber laying angle selection to fall into the local optimum prematurely. Therefore, considering the difficulties and challenges of the traditional NDFO interpolation scheme in the multi-scale variable stiffness design optimization of fiber-reinforced composites. This paper proposes an Adaptive Normal Distribution Fiber Optimization (ANDFO) interpolation scheme, and the feedback mechanism of the convergence rate of the element design variable and the objective function is introduced to achieve the adaptive reduction of the penalty parameters. Based on the proposed ANDFO interpolation scheme, a multi-scale design optimization model of fiber-reinforced composite laminates is established, considering the macro-scale structure topology and micro-scale discrete fiber laying angel selection. The explicit sensitivity of the objective function of minimizing structural compliance to the macro-scale topological design variables and the micro-scale fiber laying angle design variables is derived. Considering the manufacturability of additive manufacturing based on the optimized design results, a multi-scale nonlinear continuous filtering strategy for discrete fiber laying angle is adopted to improve the continuity of the local fiber laying path. Numerical examples systematically present the coupling effects of macro-scale structural topology and micro-scale fiber laying path, multi-scale nonlinear discrete fiber continuous filtering laying path structure, and continuous fiber additive manufacturing multi-scale optimized structure. The proposed ANDFO scheme provides a new theoretical and methodological approach for the lightweight and integrated multi-scale design and manufacturing of fiber-reinforced composite laminates through additive manufacturing technology.

3D printing for sodium batteries: From material design to integrated devices

Additive manufacturing, commonly known as 3D printing, is an innovative technique for fabricating batteries with arbitrary architectures. Understanding the intricacies of 3D printing designs in sodium battery materials is crucial for optimizing their electrochemical properties and unlocking the full potential of 3D printed sodium batteries. This review provides a comprehensive overview of the key aspects involved in the fabrication of 3D printed sodium batteries, encompassing material selectivity criterion, design considerations, and optimization strategies. Challenges and prospects for the fabrication of high-performance 3D printed sodium batteries are outlined, aiming to provide valuable insights into new conception and theoretical guidance for the design and performance optimization of composites by 3D printing for the practical application of sodium batteries in the future.

Green analytical comparison and central composite design optimization for simultaneous estimation of pain management drugs using RP-liquid chromatography

The Central Composite Design method was utilized to validate a precise RP-HPLC method for concurrently determining the quantities of Paracetamol (PC), Diclofenac Sodium (DS), and Eperisone Hydrochloride (EH) in tablet compositions. By employing Design of Experiment (DOE), the experimental parameters were fine-tuned, resulting in an optimized eluent consisting of methanol: water (90:10) with 0.1 % orthophosphoic acid at a eluent velocity of 1 mL/min. The method exhibited exceptional purities: PC (100.83 % ± 0.85), DS (102.01 % ± 0.90), and EH (100.49 % ± 1.29). Regression equations were formulated for PC, DS, and EH as follows: y = 479762x + 151907, y = 2182788x + 2409442, and y = 777144x − 1146334, respectively. The analytical method underwent comprehensive validation, including tests for: Accuracy, Precision, Linearity and Robustness. To assess the method’s environmental impact, several Green Analytical Chemistry (GAC) tools were employed. These tools provided a multifaceted evaluation of the method’s sustainability and eco-friendliness.

Reliability-based composite pressure vessel design optimization with cure-induced stresses and spatial material variability

The future green hydrogen economy requires reliable and affordable composite pressure vessels. These vessels are expensive to manufacture, for a large part due to the high cost of carbon fibers in the composite layup. This study minimizes the thickness (and cost) of a composite pressure vessel layup without a reduction of its reliability. The study applies a multiphysics and multiscale uncertainty quantification framework that predicts the nondeterministic vessel burst pressure. A thermomechanical curing simulation accounts for cure-induced stress. It shows a good qualitative agreement with experimental measurements. A previously validated nondeterministic mechanical burst simulation accounts for experimentally measured spatial material variability of fiber misalignment, fiber volume fraction, and fiber strength. The workflow is coupled with a global optimization strategy that minimizes the layup thickness and retains the same 1% probability of failure pressure as a baseline pressure vessel. The optimization varies the number of layers in the layup, and their winding angle. A 27.3% thickness reduction is achieved.

RGO@SnO2/ZnO nanocomposite as a highly reactive heterogeneous catalyst toward biodiesel synthesis by electrolysis procedure: Central composite design optimization

This paper highlights the incorporation of catalyst choice, feedstock type, and production technique for biodiesel production, thereby addressing existing gaps in the current research. In this sense, the rGO@SnO2/ZnO nanocomposite as a novel heterogeneous catalyst was prepared and employed to generate biodiesel from waste frying oil (WFO) by electrolysis approach for the first time. The structural attributes of rGO@SnO2/ZnO nanocatalyst were evaluated by several analyses. According to these analyses, rGO@SnO2/ZnO nanocomposite has a specific area of 61.428 g/m2 and appropriate functional groups. Additionally, a CCD-based RSM approach was employed for achieving the highest biodiesel yield in optimal conditions (MeOH/WFO molar proportion=12:1, reaction time= 40 min, voltage=40 V, and catalyst concentration= 3.5 wt%) was 96.47 %. Likewise, the yield of biodiesel decreased from 96.58 % to 88.36 % after seven reuse cycles, which reveals the substantial stability of rGO@SnO2/ZnO nanocomposite in the transesterification procedure. Moreover, the thermodynamic behavior of biodiesel synthesis indicated that the transesterification employing rGO@SnO2/ZnO nanocomposite is endothermic. The activation energy and Arrhenius factor for the transesterification of WFO to biodiesel utilizing rGO@SnO2/ZnO nanocatalysts were determined to be 51.59 kJ/mol and 6.67×105 min−1, respectively. Due to the high biodiesel yield and remarkable reusability, rGO@SnO2/ZnO nanocomposite is strongly recommended for the transesterification procedure.

The structural stability and mechanical properties of doped Al2Cu precipitates with different elements by first-principles calculations

The Al2Cu precipitates (θ phase) is an important strengthening phase of Al- Cu alloy, which has a significant effect on the comprehensive mechanical properties of the material. This study employed first-principles calculations based on density functional theory to investigate the effects of doped elements X (X = Fe, Mn, Mg, Sc, and Zr) on the structural stability, mechanical properties, and electronic structure of the θ phase. The calculations of forming energy, cohesive energy, and phonon spectrum show that different doping elements have different substitution sites for Al or Cu in θ phase, and the doped θ phase has good structural stability. The calculation results of elastic constants show that doping Fe can enhance the hardness and stiffness of θ phase, reduced Mn content can improve its ductility, while the introduction of Mg can improve the shear modulus of θ phase. Sc is the most effective element to improve the anisotropy of θ phase, and the addition of Zr can makes θ phase better ductility. By electronic structure analysis, it is found that the incorporation of doping elements promotes covalent interactions with Al and Cu atoms, thus enhancing the stability of the θ phase. Charge density and Bader charge analysis reveal that the Fe and Mn atoms experience electron gain in the doped θ phase, whereas the Mg, Sc, and Zr atoms undergo electron loss. The results of this study provide useful information for the composition design and performance optimization of Al-Cu alloys.

Design optimization of advanced tow-steered composites with manufacturing constraints

Tow steering technologies, such as automated fiber placement, enable the fabrication of composite laminates with curvilinear fiber, tow, or tape paths. Designers may therefore tailor tow orientations locally according to the expected local stress state within a structure, such that strong and stiff orientations of the tow are (for example) optimized to provide maximal mechanical benefit. Tow path optimization can be an effective tool in automating this design process, yet has a tendency to create complex designs that may be challenging to manufacture. In the context of tow steering, these complexities can manifest in defects such as tow wrinkling, gaps, overlaps. In this work, we implement manufacturing constraints within the tow path optimization formulation to restrict the minimum tow turning radius and the maximum density of gaps between and overlaps of tows. This is achieved by bounding the local value of the curl and divergence of the vector field associated with the tow orientations. The resulting local constraints are effectively enforced in the optimization framework through the Augmented Lagrangian method. The resulting optimization methodology is demonstrated by designing 2D and 3D structures with optimized tow orientation paths that maximize stiffness (minimize compliance) considering various levels of manufacturing restrictions. The optimized tow paths are shown to be structurally efficient and to respect imposed manufacturing constraints. As expected, the more geometrical complexity that can be achieved by the feedstock tow and placement technology, the higher the stiffness of the resulting optimized design.

Statistical modeling and optimization of heavy metals (Pb and Cd) adsorption from aqueous solution by synthesis of Fe3O4/SiO2/PAM: isotherm, kinetics, and thermodynamic

In this study, magnetic material was synthesized using iron salts, then silicon-specific material was used to gain porosity, straight-chain polyacrylamide (PAM) was modified to give the surface functional properties, and the final product synthesized Fe3O4/SiO2/PAM nanocomposite material. Heavy metal (Pb and Cd) removal studies were carried out with the synthesized composite material, considering the central composite design and response surface methodology (CCD-RSM) optimization model. The effects of various parameters, for example, the initial concentration, pH, adsorbent dose, temperature and contact time, were investigated as a part of this study. To optimize these parameters, the CCD-RSM model was applied to design the experiments. Analysis of variance (ANOVA) was applied to evaluate statistical parameters and investigate interactions of variables. In the designed experimental set, the amount of adsorbent (30 mg), pH 7.0 value, temperature (40 °C), initial concentration of Pb (80 mg/L) and Cd (20 mg/L) and 90 min contact time were determined as the optimum conditions. The high coefficient of determination of both metals showed good agreement between experimental results and predicted values (R2 0.99; 0.95). TEM, SEM, XRD, FTIR, BET and Zeta potential analyses were performed to characterize the structure and morphology of the adsorbent. In Pb2+ and Cd2+ heavy metal removal studies, maximum adsorption capacities were determined as 66.54 and 13.22 mg/g, respectively. Additionally, adsorption isotherms, adsorption kinetics and thermodynamic modeling studies were conducted. Features such as large surface area and high adsorption capacity of the synthesized nanoparticles were observed. In this study, Fe3O4/SiO2/PAM demonstrated its potential as an effective adsorbent for the removal of heavy metal ions present in simulated wastewater samples. In particular, we can say that the material has a strong selectivity, as well as a high affinity for Pb(II) ions.

In-depth insight into the driving factors of the compressive strength development of MKPC based on interpretable machine learning methods

Magnesium potassium phosphate cement (MKPC) is a kind of Mg-chemically bonded phosphate ceramic commonly used for rapidly repairing dilapidated structures. In this study, a compressive strength dataset of MKPC was constructed, and four advanced machine learning (ML) algorithms (XGBoost, RF, GBDT and ANN) were selected to establish a high-precision compressive strength prediction model of MKPC. The SHAP and PDP methods are also used for interpretability analysis of ML-MKPC models. The XGBoost model has good generalizability and reliability while achieving high prediction accuracy. The RF and GBDT models performed similarly to the XGBoost model on the training set but performed poorly on the testing set. The ANN model is poorly trained on both the training and testing sets, with a risk of underfitting. The R2 of the XGBoost model at the different compressive strength stages still reaches above 0.80, indicating that it not only captures the complex relationships of the overall dataset well but also effectively predicts the staged strength dataset. Feature importance analysis revealed that the curing age (T), water-to-binder ratio (W/B), mineral admixtures-to-binder ratio (MA/B) and phosphate-to-magnesium ratio (P/M) are the principal variables affecting the compressive strength of MKPC. The partial interpretation shows that the optimum value range is determined when W/B is 0.10–0.18, MA/B is 0–0.20, P/M is 0.40–1.0, and R/M is 0–0.12. The composition of mineral admixtures with high-Ca, high-Si and low-Al systems seems to be more conducive to participating in the hydration reaction of MKPC. The ML-MKPC compressive strength prediction model developed in this study can provide theoretical support for the subsequent composition design and performance optimization of MKPC.

Design optimization of polymer composites for lower suspension arms of automotive vehicles

Design optimization of polymer composites for lower suspension arms of automotive vehicles

Data-driven composition design and property optimization of solid solution and precipitation simultaneously strengthened non-oriented silicon steel

Limitations on data volume and quality are key bottlenecks in using machine learning for property prediction and property-oriented composition design of non-oriented silicon steel. In response, this study employed a Gaussian Mixture Model (GMM) to generate virtual samples for enhancing the in-house experimental data, by which the generated virtual data well captured the distribution of the raw experimental data. As a result, compared with the model without data augmentation, the enhanced prediction model (composition → property) fitted by virtual samples improved its R2 value from 0.52 to 0.86. Based on this model, a multi-properties-oriented (yield strength σs, magnetic induction B50, and iron loss P10/400) composition prediction model (property → composition) was established to rapidly discover high-performance non-oriented silicon steel. Experimental characterization and theory calculation of the explored candidate alloys exhibited that their yield strength was above 750 MPa, which primarily results from solid solution strengthening (50 %) and precipitation strengthening (36 %). Moreover, the alloys possessed magnetic induction B50 of over 1.72 T and low core losses with P10/400 of 12.1 W/kg (0.2 mm) and 17.0 W/kg (0.35 mm). Further microstructural characterization exhibited that such satisfactory performance is associated with copper (Cu)-rich nanoprecipitates that dramatically improved the yield strength without deteriorating the magnetic properties.

Cananga odorata (Ylang-Ylang) Essential Oil Containing Nanoemulgel for the Topical Treatment of Scalp Psoriasis and Dandruff.

This research aimed to evaluate the efficacy of a nanoemulgel (NE) containing Cananga odorata (Ylang-Ylang) oil for managing scalp psoriasis and dandruff through various assessments. The study involved phytochemical screening, characterization, stability testing, in vivo performance evaluation, dermatokinetic analysis, central composite rotatable design (CCRD) optimization, in vitro release profiling, and antioxidant and antimicrobial activity assessment of the NE. The NE exhibited excellent stability and maintained physical parameters over a three-month period. In vivo studies showed no skin irritation, maintenance of skin pH (4.55 to 5.08), and improvement in skin hydration (18.09 to 41.28 AU) and sebum content (26.75 to 5.67 mg/cm2). Dermatokinetic analysis revealed higher skin retention of C. odorata in the NE (epidermis: 71.266 µg/cm2, dermis: 60.179 µg/cm2) compared to conventional formulations. CCRD optimization yielded NE formulations with the desired particle size (195.64 nm), entrapment efficiency (85.51%), and zeta potential (-20.59 mV). In vitro release studies indicated sustained release behavior, and antioxidant and antimicrobial properties were observed. This study demonstrates the stability, skin-friendliness, therapeutic benefits, and controlled release properties of the NE. The NE presents a promising option for various topical applications in treating bacterial and fungal diseases, potentially enhancing drug delivery and treatment outcomes in pharmaceuticals and cosmetics.

Improving the Groundnut Oil Extraction Efficiency using RSM and Central Composite Design (CCD) Optimization Techniques

Improving the Groundnut Oil Extraction Efficiency using RSM and Central Composite Design (CCD) Optimization Techniques

Achieving improved dielectric energy storage properties and temperature stability in 0.91Na0.5Bi0.5TiO3-0.09K0.7La0.1NbO3 based ferroelectric ceramics by composition design and processing optimization

In this study, a ternary solid solution was designed by incorporating varying concentrations of the B-site composite perovskite Ba(Mg1/3Ta2/3)O3 (BMT) into 0.91Na0.5Bi0.5TiO3-0.09K0.7La0.1NbO3 (NBT-KLN-based) ferroelectric ceramics to optimize their energy storage performance. The introduction of BMT disrupted the long-range ordered state of the A/B site in the original perovskite structure, resulting in increased structural disorder and enhanced relaxor characteristics. The viscous polymer processing (VPP) method was employed to attain a significantly denser structure. The 0.10BMT-vpp ceramic has ultimately achieved optimal energy storage performance, along with exceptional temperature and frequency stability. It boasts a high dielectric breakdown strength (BDS) of 440 kV/cm, an ultra-high effective energy storage density (Wrec) of 6.7 J/cm3, and an energy storage efficiency (η) of 82%. Moreover, the 0.10BMT-vpp ceramic exhibits robust pulse performance, characterized by an ultra-fast discharge time (t0.9) of only 77 ns. Furthermore, within the temperature range of −66 °C–354 °C, the temperature coefficient of capacitance (TCC) of the 0.25BMT ceramics undergoes a variation of no more than ±15%, in compliance with X9R specifications (−55 °C–200 °C). The introduction of BMT endows NBT-KLN-based ceramics with remarkable temperature stability and comprehensive energy storage performance, making it a ceramic material with practical potential for industrial applications.

Optimization and Deep Learning Modeling of the Yield and Properties of Baobab-Derived Biodiesel Catalyzed by Waste Banana Bunch Stalk Biochar.

The integration of optimization techniques and deep learning models, which offer a promising avenue for improving the efficiency and sustainability of biodiesel production processes from baobab seed oil (BSO), is rare. This study utilized a multi-input-multioutput (MIMO) deep learning technique and the most recent central composite design (CCD) optimization tool to model and optimize the yield and properties of biodiesel produced from BSO. First, the baobab seed oil was extracted using a solvent extraction method. BSO was subsequently analyzed and converted to biodiesel by reacting CH3OH catalyzed by waste banana bunch stalk biochar activated by KOH. Multiobjective optimization and prediction of the biodiesel yield (Y) and several key fuel properties, including the cetane number (CN), kinematic viscosity (VS), and purity (P), were achieved. With better correlation coefficients of 0.9709, 0.9464, and 0.9714 for response training, response testing, and response validation, respectively, and a root-mean-square error of 0.00755, the MIMO model on the logsig transfer function accurately predicted the biodiesel yield and properties more than did the MISO and response surface methodology models. The optimum Y (96 wt %), CN (48), VS (3.3 mm2/s), and P (98.3%) were concurrently accomplished at a reaction temperature of 56 °C, a reaction time of 115 min, a CH3OH/BSO molar ratio of 15:1, a catalyst dosage of 6 wt %, and a stirring speed of 400 rpm with 98% optimal validation accuracy. CCD sensitivity analysis revealed that the CH3OH/BSO ratio was the most sensitive (50.9%) input predictor among the other input variables studied.

Response Surface Methodology—Central Composite Design Optimization Sugarcane Bagasse Activated Carbon under Varying Microwave-Assisted Pyrolysis Conditions

Sugarcane bagasse (SB) is a widely available agro-industrial waste residue in China that has the potential to be converted into a cost-effective and renewable adsorbent. In this study, activated carbon (AC) was prepared from SB by microwave vacuum pyrolysis using H3PO4 as the activator. To enhance the sorption selectivity and yield, the pyrolysis process of SB-activated carbon (SBAC) should be well-designed. Central composite design was employed as an optimized experiment design, and response surface methodology was used to optimize the process parameters for maximized SBAC yield and its iodine number. The results showed that the optimized parameters obtained for the SBAC are 2.47 for the impregnation ratio (IR), 479.07 W for microwave power (MP), 23.86 mm for biomass bed depth, and 12.96 min for irradiation time, with responses of 868.7 mg/g iodine number and 43.88% yield. The anticipated outcomes were substantiated, revealing a marginal 5.4% variance in yield and a mere 1.9% discrepancy in iodine number from the forecasted values. The synthesized adsorbents underwent comprehensive characterization through instrumental methodologies, including FT-IR, BET, and SEM. The SBAC produced by the pyrolysis method contained a regular and homogeneous porous structure with a specific surface area of up to 1697.37 m2/g and a total 1.20 cm 3/g volume, which has favorable adsorption of toxic and harmful substances in the environment.

Multi-objective Design Optimization of Cylindrical Multi-material Composites Subjected to Multiple Loads

Multi-objective Design Optimization of Cylindrical Multi-material Composites Subjected to Multiple Loads