Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
3,349
result(s) for
"Lattice design"
Sort by:
Data-Driven Bi-Directional Lattice Property Customization and Optimization
Customizing and optimizing lattice materials poses a challenge to designers. This study proposed a data-driven generative method to customize and optimize lattice material. The method utilizes subdivision modeling to parametrically describe lattice morphologies and skeletons. Next, the homogenization method is employed to analyze elastic moduli for collecting a dataset. Then, a two-tiered machine learning (ML) framework is proposed to predict the elastic modulus for a forward design. The first-tier model employs polynomial regression to estimate relative density, which serves as an additional input feature for the second-tier model. The prediction accuracy of the second-tier model is improved through the additional inputs. The forward and reverse design strategies offer a flexible and accurate means of tailoring lattice properties to meet specific performance requirements. Two case studies demonstrate the practical value of the framework: customizing a lattice material to achieve a desired elastic modulus and optimizing the mechanical performance of lattice materials under relative density constraints. The results show that the prediction accuracy of the elastic modulus using the two-tiered ML model achieved an error of less than 10% compared to finite element analysis, demonstrating the reliability of the proposed approach. Furthermore, the optimization design achieved up to a 25% improvement in mechanical performance compared to conventional lattice configurations under the same relative density constraints. These findings underscore the advantages of combining generative design, machine learning, and genetic algorithms to navigate complex design spaces and achieve enhanced material performance.
Journal Article
Rational Engineering in Protein Crystallization: Integrating Physicochemical Principles, Molecular Scaffolds, and Computational Design
X-ray crystallography remains the gold standard for high-resolution structural biology, yet obtaining diffraction-quality crystals continues to pose a major bottleneck due to inherently low success rates. This review advocates a paradigm shift from probabilistic screening to rational engineering, reframing crystallization as a controllable self-assembly process. We provide a comprehensive overview of strategies that connect fundamental physicochemical principles to practical applications, beginning with contact design, which involves the active engineering of crystal contacts through surface entropy reduction (SER), introduction of electrostatic patches. Complementing these molecular approaches, we discuss physicochemical strategies that exploit heterogeneous nucleation on functionalized surfaces and gold nanoparticles (AuNPs) to lower the energy barrier for crystal formation. We also address scaffold design, utilizing rigid fusion partners and polymer-forming chaperones to promote crystallization even from low-concentration solutions. Furthermore, we highlight principles for controlling the behavior of multi-component complexes, based on our experimental experience. Finally, we examine de novo lattice design, which leverages AI tools such as AlphaFold and RFdiffusion to program crystal lattices from first principles. Together, these strategies establish an integrated workflow that links thermodynamic stability with crystallizability.
Journal Article
Conditional diffusion models for the inverse design of lattice structures
Inverse design, a critical area of mechanical design, focuses on determining the optimal configuration of a structure or material to achieve desired properties or performance. However, the vast array of design possibilities for manufacturable unit cells presents a significant challenge in inverse design: efficiently identifying a complex lattice that meets specific target properties. To address these challenges and, moreover, to offer a solution, we propose a simple yet effective framework that leverages conditional diffusion models, a class of generative models known for their ability to produce high-quality samples conditioned on specific input parameters. Our model, named LatticeOptDiff, enables the efficient exploration of the vast design space, including surface-based, truss-based, and hybrid surface-truss-based lattice structures, by guiding the generation process toward configurations that meet predefined criteria such as Young’s modulus, Poisson’s ratio, and volume fraction. Results indicate that (1) our method can generate various unit cells that satisfy specified material properties with higher accuracy compared to a state-of-the-art conditional generative adversarial network (GAN) and (2) the lattice structures generated through our method exhibit superior mechanical performance when compared to those generated by the GAN. The engineering applications are verified through finite element (FE) simulations and tests on 3D-printed lattice structures. By introducing LatticeOptDiff into the design of lattice structures, we show that conditional diffusion models can outperform GANs in engineering design synthesis, thereby broadening the scope for research and practical applications across diverse engineering fields.
Journal Article
Non-Conventional Wing Structure Design with Lattice Infilled through Design for Additive Manufacturing
Lightweight structures with a high stiffness-to-weight ratio always play a significant role in weight reduction in the aerospace sector. The exploration of non-conventional structures for aerospace applications has been a point of interest over the past few decades. The adaptation of lattice structure and additive manufacturing in the design can lead to improvement in mechanical properties and significant weight reduction. The practicality of the non-conventional wing structure with lattices infilled as a replacement for the conventional spar–ribs wing is determined through finite element analysis. The optimal lattice-infilled wing structures are obtained via an automated iterative method using the commercial implicit modeling tool nTop and an ANSYS workbench. Among five different types of optimized lattice-infilled structures, the Kelvin lattice structure is considered the best choice for current applications, with comparatively minimal wing-tip deflection, weight, and stress. Furthermore, the stress distribution dependency on the lattice-unit cell type and arrangement is also established. Conclusively, the lattice-infilled structures have shown an alternative innovative design approach for lightweight wing structures.
Journal Article
Simulation and Experimental Assesment of acrylonitrile butadiene styrene polymer based new lattice design
High permeable sandwich structure (foam) or scaffolds are prerequisites in the field of biomedical, aerospace, and automotive sector, due to its energy absorbing characteristics and tissue formation or cell generation properties. The architecture was previously distant to manufacture but is now feasible, thanks to additive manufacturing methodology or 3d printing technology. Nonetheless, in order to reduce the weight of the building and save on resources by adopting a porous construction, one crucial aspect must be addressed to suit the aim. The objectives of this article are to compare lattice strength with different lattice designs and lattice porosity (ranging from 20 to 78%), as well as to assess the structure of load capacity behaviour in different unit lattice designs made of acrylonitrile butadiene styrene (ABS) material, and to study the behaviour of different lattice cell structure (LCSs) at post-yielding stages depending on its lattice design. As a result, four distinct lattice designs are considered: honeycomb structure, simple cubic square lattice, simple cubic pyramidal lattice and simple cubic double circular ring lattice. The finite element analysis models are designed to describe the compressive deformation behavior of these different LCSs. Such simulations are used to better understand as well as provide accurate data on failure causes as well as the interaction between the different layers for the compressive deformations and lattice architectures. Strength diminishes with rising porosity, based on the simulation and earlier studies with a vertical or z-direction force under quasi-static conditions, strength reduces as the porosity increases. The results show that the finite element analysis derived compressive behavior closely fits the experimental results, confirming the accuracy of the FEA models and that strain and plastic dissipation energy are not distributed uniformly throughout each layer. The results of this analysis are critical in assisting designers in selecting appropriate design and porosity before manufacturing.
Journal Article
Formulation and Physical Evaluation of Hydrogel Extract Combination of Centella asiatica and Moringa oleifera using Simplex Lattice Design Method
This research aims to develop an optimal hydrogel formula using Design Expert 13.0 and the Simplex Lattice Design (SLD) method. The hydrogel combines Centella asiatica and Moringa oleifera extracts with carbopol 940 as a gelling agent and triethanolamine (TEA) as an alkalizer. Hydrogels were chosen due to their ease of use, cooling effect, and superior biocompatibility compared to other topical formulations like ointments or creams. The study focused on optimizing the formula to meet Indonesian National Standards (SNI). After eight hydrogel formula trials using the SLD method, physical evaluations were conducted, including organoleptic tests, pH measurements, spreadability, and viscosity tests. The optimal formula contained 0.6% Carbopol and 0.4% TEA, validated with three replications. The hydrogel exhibited a deep green color, a distinctive extract aroma, and a thick texture, meeting SNI standards with an average pH of 7.1±0.29, spreadability of 5.7±0.21 cm, and viscosity of 20,080±0.35 cPs. Stability tests confirmed its quality after cycling and centrifugal evaluations. This study promotes sustainable and eco-friendly practices by utilizing natural extracts, reducing reliance on synthetic chemicals, and contributing to environmentally conscious topical drug innovations. The development of such hydrogels aligns with sustainability goals, offering biodegradable and safe formulations that minimize environmental impact.
Journal Article
Optimization and Formulation of Skin Lotion Contating Butterfly Pea ( Clitoria ternatea ) Flower Extract and Study on its Antioxidant Activity
Butterfly pea flower is rich in phenolic and flavonoid antioxidants that protect the skin from free radicals, which can lead to oxidative damage. This study aimed to optimize a lotion formulation containing butterfly pea flower extract (BPFE), using triethanolamine (TEA) and stearic acid as emulsifiers, and to evaluate its antioxidant activity. Eight lotion formulations were prepared using the Simplex Lattice Design method (Design Expert v13.0), focusing on optimizing the stearic acid to TEA ratio. Antioxidant activity was measured using DPPH (2,2-diphenyl-1picrylhydrazyl) at a wavelength of 516 nm. Key parameters such as spreadability, adhesion, pH, and viscosity were assessed. The maceration method yielded BPFE with a 49.66% extraction efficiency. The optimized formulation demonstrated a spreadability of 6.26±0.38 cm, adhesion of 3.2±0.2 s, a pH of 7.19±0.005, and a viscosity of 120 dPas. Both BPFE and the optimized lotion formulation exhibited strong antioxidant activity, with IC50 values of 59.87±0.802 µg/ml and 84.52±1.418 µg/ml, respectively. The optimized BPFE lotion formulation presents promising potential for development as a cosmeceutical product.
Journal Article
Optimization of β-Fructofuranosidase Production from Agrowaste by Aspergillus carbonarius and Its Application in the Production of Inverted Sugar
Research background. Microbial β-fructofuranosidases are widely employed in food industry to produce inverted sugar or fructooligosaccharides. In this study, a newly isolated Aspergillus carbonarius PC-4 strain was used to optimize the β-fructofuranosidase production in a cost-effective process and the sucrose hydrolysis was evaluated to produce inverted sugars. Experimental approach. Optimization of nutritional components of culture medium was carried out using simplex lattice mixture design for 72 and 120 h at 28 °C. One-factor-at-a-time methodology was used to optimize the physicochemical parameters. Crude enzyme was used for sucrose hydrolysis at different concentrations. Results and conclusions. The optimized conditions of enzyme production were achieved from cultivations containing pineapple crown waste (1.3 %, m/V) and yeast extract (0.3 %, m/V) after 72 h with an enzyme activity of 9.4 U/mL, obtaining R²=91.85 %, R²adjusted=85.06 %, highest F-value (13.52) and low p-value (0.003). One-factor-at-a-time used for optimizing the physicochemical conditions showed optimum temperature (20 °C), pH (5.5), agitation (180 rpm) and time course (72 h) with a 3-fold increase of enzyme production. The invertase-induced sucrose hydrolysis showed the maximum yield (3.45 mmol of reducing sugars) using 10 % of initial sucrose concentration. Higher sucrose concentrations caused the inhibition of invertase activity, possibly due to the saturation of substrate or formation of sucrose aggregates, making it difficult for the enzyme to access sucrose molecules within the created clusters. Therefore, a cost-effective method was developed for the invertase production using agroindustrial waste and the produced enzyme can be used efficiently for inverted sugar production at high sucrose concentration. Novelty and scientific contribution. This study presents an efficient utilization of pineapple crown waste to produce invertase by a newly isolated Aspergillus carbonarius PC-4 strain. This enzyme exhibited a good potential for inverted sugar production at high initial sucrose concentration, which is interesting for industrial applications.
Journal Article
Balanced Lattice Designs under Uncertain Environment
Balanced lattice designs are vital in numerous fields, especially in experimental design, where controlling variability among experimental units is crucial. In practical experiments, various sources of uncertainty can lead to ambiguous, vague, and imprecise data, complicating the analysis process. To address these indeterminacies, a novel approach using neutrosophic analysis within a balanced lattice design framework is proposed, termed the neutrosophic balanced lattice design (NBLD). This innovative method employs neutrosophic statistics to derive mathematical neutrosophic sums of squares and construct a neutrosophic analysis of variance (NANOVA) table. The effectiveness of the proposed NBLD is demonstrated through a numerical example, showing that it outperforms traditional methods in handling uncertainty.
Journal Article