Mihaela Banu

Geometric deviations remain a major barrier to the widespread industrial adoption of incremental sheet forming (ISF). Compared with conventional toolpath compensation that rely on extensive data generation and trial-and-error procedures, variation of toolpath styles offers a more direct and efficient strategy for mitigating geometric defects. In this study, multiple curvilinear toolpath strategies were investigated for a standard closed-contour ISF part to evaluate their effectiveness in reducing geometric deviations. Six toolpaths were examined, including three established types – convex, concave, and wavy – and three novel toolpaths proposed in this work: adaptive, cusp, and sine. The convex toolpath achieved the largest side-wall springback reduction relative to the linear baseline but introduced a significant bottom pillow effect and reduced formability. While the cusp toolpath effectively suppressed both springback and pillow formation, it resulted in local thickening and degraded surface finish. Overall, the sine toolpath provided the most balanced performance, achieving effective reduction of all major geometric defects. Numerical simulations reveal an inherent tradeoff between side-wall springback reduction and bottom pillow formation, as positive residual bending moments formed in the pillow region contribute to springback mitigation by promoting outward bending of the side walls.

This special edition focuses on contributions that detail the results of new developments and provide a deeper understanding of incremental and sheet material forming processes. Research concerns the design of novel processes, in-process measurement techniques, innovative tooling, methods for the analysis and modelling of friction phenomena and methods for the optimisation, robust design and control of incremental and sheet forming processes, etc. The edition will be helpful for specialists in mechanical engineering.

Additive Manufacturing (AM) has gained wide attention in the past two decades and emerged as a significant method in the manufacturing sector. Advancements in AM have enhanced productivity, reduced lead times, and improved part quality while maintaining cost-effectiveness. Despite advancements in materials, technologies, and parameter optimization, the widespread AM adoption is limited by a lack of skilled workforce. This research presents a hybrid learning approach to address this gap through curricula and hands-on training. The proposed framework includes hybrid educational and training approaches in polymer-based FFF, SLA, SLS, and Metal FFF technologies toward the development of a workforce skilled in AM

Emotional Intelligence plays a crucial role in the well-being and over all personality development of the students’ community which means, being able to understand and manage self as well as others. As self-awareness (SA) takes the center stage, this paper attempts to assess the level of SA among medical college students in Tamil Nadu. The sample size of this cross-sectional study was 197 drawn on convenient sampling basis. The data were collected through Structured Questionnaire based on Daniel Goleman’s ESCI (Emotional and Social Competency Network) model. The study concluded that most of the students were well aware of self and there exists significant difference between gender and age of the students and the domains of self-awareness except emotional awareness. It is also found that locality does not play a significant role in the concept of knowing thyself and the factors influencing self are not strongly connected to geographical differences.

While nanoparticle uptake by plants has primarily been studied for environmental remediation, its purposeful use to enhance plant structural and mechanical properties remains unexplored. We hypothesized that cellulose nanofibers (CNFs), introduced into the growth medium, could be absorbed by flax stems, reinforcing their cell walls formation and, as a consequence, improve the mechanical performance. Thus, in this paper it is proved that flax plants treated with a 0.2% w/v CNF solution after root excision showed increased stem diameter, reduced pith size, and significantly accelerated root regeneration (~ 7 cm in 20 days) compared to controls treated with autoclaved deionized water. Analyses of the CNF-treated stems showed an increase in the mechanical performance of the stems revealing up to 50% increase in energy to fracture, 22% increase in Young's modulus, and approximate 33% improvement in stiffness at a reduced density compared to the non-treated stems. To explain these effects, morphology analysis of stems was conducted. Fourier-transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC) revealed enhanced hydrogen bonding, cellulose crystallinity, and thermal stability. This study developed a novel in vivo approach by incorporating CNFs into plants during early growth to trigger structural reinforcement, which in turn improves mechanical performance.

The benchmark 2024 project on Incremental Sheet Forming (ISF), involving 15 research institutes in 13 experimental contributions, provided a unique opportunity to compare experimental outputs from various setups and forming strategies in ISF. This collaboration led to the development of uniform data exchange formats, measurement guidelines, and standardized nomenclature, fostering efficient future collaborations. The project addressed challenges in geometric accuracy when forming a relatively large part (400 × 400 mm) using Single Point Incremental Forming (SPIF) and focused on multiple common pitfalls in ISF, in particular the tent effect and pillow effect. Additionally, some experiments have been conducted using Two Point and Double Sided Incremental Forming (TPIF and DSIF). By combining the knowledge and experience of all participating institutes, this project aimed to provide insights into effective parameter choice and toolpath strategies, and shows the importance of multi-stage processes to increase the geometric accuracy. Despite the theoretical simplicity of SPIF setups, such multi-stage toolpath strategies directed toward improved geometric accuracy also add some new challenges. The study highlighted the need for multi-stage strategies that focus on local effects, as well as geometric compensation techniques to enhance ISF's industrial applicability. Alternative process variants like TPIF and DSIF, showed promising results, but they also had limitations and presented challenges, emphasizing the importance of predictive simulation tools to further increase geometric accuracy. The scalability of ISF experiments remains a significant challenge, necessitating further research into scale laws for process optimization.

In recent years, Additive Manufacturing (AM) has rapidly emerged as a significant manufacturing method. Recent advances in AM, especially in materials and processing techniques, have reduced lead times, enhanced productivity, improved print resolution, and increased cost-effectiveness. Despite these advancements, knowledge gaps remain a barrier to AM’s adoption in industry and academia, emphasizing the need for workforce training to equip users with the skills to address its various challenges. This research proposes a framework through hybrid educational programs using a novel training-based approach to address knowledge and skill gaps in AM. The proposed approach trains users in Design for Additive Manufacturing (DfAM), focusing on Computer-Aided Design (CAD) and simulation to optimize technology and process parameter selection. Additionally, the framework facilitates comprehensive AM education and hands-on training in Fused Filament Fabrication (FFF), Stereolithography (SLA), Selective Laser Sintering (SLS), and Laser Powder Bed Fusion (LPBF), promoting adoption through skill-based training in technology selection, material handling, and decision-making in additive manufacturing processes. Future work suggests integrating virtual and augmented reality modules to enhance user experience with interactive learning.

Abstract The design of battery packs has become a key factor influencing vehicle performance and safety. Current studies emphasize the role of Lithium-ion battery (LIB) separators in Electric Vehicles (EVs) and highlight the increasing occurrence of battery failures in vehicle crash conditions. The separators, positioned between the mounted battery packs attached to the vehicle floor, ensure structural stability and help mitigate potential hazards in crash scenarios. Additionally, they function as thermal and electrical insulators, preventing interactions between neighboring battery modules. At the same time, battery separators must remain lightweight to optimize energy efficiency. This paper focuses on predicting the loads on battery separators during simulated crash tests to determine the design boundary conditions for separators made of carbon fiber reinforced polymer composites (CFRP) and their manufacturing using pultrusion. The study includes numerical simulations to evaluate the bending and energy characteristics of various geometry profiles such as T-shape and hollow rectangle shape of the separators in a high-speed impact scenario such as crash. The preliminary design phase involves mechanical property testing and material validation using three-point bending and finite element simulations of rigid side-pole crash tests to predict bulk material behavior under different conditions. By comparing the performance of models with LIB-integrated separators, the study aims to incorporate simulation results for material selection, design, and the manufacturing of pultruded separators. T-shaped and hollow rectangular separators assembled into battery modules were investigated. For the same weight, the hollow rectangular separator absorbed 0.23 kJ more energy than the T-shaped separator. The bending behavior of the horizontal hollow rectangular separator was evaluated through a three-point bending test, revealing that a thickness of 4 mm provided better bending stiffness while maintaining a lightweight structure. Ultimately, this research focuses on analyzing a pultruded bulk separator that offers cost efficiency and effective mechanical properties, particularly in the context of rigid side-pole impact scenarios in EV crashes. It also explores optimization of wall thickness and web thickness.

PURPOSE: Patients with pre-existing medical conditions that impair bone integrity face challenges in dental implant success due to compromised osseointegration. This study evaluates three titanium interconnected porous architectures: the TPMS solid gyroid, TPMS sheet gyroid, and Voronoi stochastic lattice. We aim to assess manufacturability, design controllability, and cellular interactions to identify an optimal architecture that enhances cellular behavior with the potential to strengthen bone-to-implant contact. METHODS: Three porous architectures were designed and compared: the two variants of the uniform, periodic triply periodic minimal surface (TPMS) gyroid, and the random, non-uniform Voronoi stochastic lattice. The porous constructs were fabricated using selective laser melting (SLM) and evaluated using microcomputed tomography (microCT) for porosity, manufacturability, and permeability. In vitro experiments used primary bone marrow stromal cells (BMSCs) isolated from 8-week-old wild type C57BL6/J mice. These cells were seeded onto the SLM-fabricated porous architectures and evaluated for adhesion using scanning electron microscopy (SEM) and RNA extraction. Cell trajectory was profiled using fluorescent confocal microscopy. RESULTS: Selective laser melting (SLM) successfully fabricated all three porous architectures, with the TPMS solid gyroid exhibiting the highest manufacturing resolution, controllability, and the most uniform pore distribution. Computational fluid dynamics (CFD) analysis showed that its permeability outperformed both the TPMS sheet gyroid and stochastic Voronoi architectures. In vitro cell culturing demonstrated superior cell behavior in the TPMS solid gyroid scaffold. RNA quantification after 72 h of culture showed that cells are most adherent to the TPMS solid gyroid, demonstrating a 4-fold increase in RNA quantity compared to the fully dense (control). Additionally, cell trajectory analysis indicated enhanced cell infiltration and cellularization within the pore channels for the TPMS solid gyroid architecture. CONCLUSION: This research demonstrates that inducing an interconnected porous architecture into a titanium construct enhances cellular behavior compared to a traditional dense implant. The TPMS solid gyroid architecture showed superior manufacturability, making it a promising solution to improve dental implant success in patients with compromised bone integrity.

Achievement is not simply the success, but is honest endeavour, persistent effort to do the best possible under any circumstances. All personal achievements start in the mind of the individual. Emotional intelligence plays a vital role and acts as a driving force that influences the success, achievement and happiness of all human beings in all fields. This holds good well in the life of the students too. The success of the students in their academic and non- academic performance depends more on their level of emotional intelligence than their intelligent quotient.

This paper presents a mathematical model designed to optimize inventory management by minimizing total inventory costs in a production setting. Traditional inventory models often fall short in addressing uncertainties that arise in real-world production environments. To overcome this, we propose an integrated inventory model that incorporates fuzzy numbers, specifically trapezoidal fuzzy numbers, to represent uncertain parameters. This approach captures the variability in production quantities, lead times, and demand rates. By applying the Graded Mean Integration (GMI) method for defuzzification, the model translates fuzzy data into actionable insights for inventory control. Using partial derivatives and optimization techniques, we derive optimal cycle time and cost parameters for managing production and inventory under uncertainty. Our results provide a robust framework for cost-efficient inventory management that adapts to the complexities of real-life production scenarios.

The desire for lightweight, carbon-negative materials has been increasing in recent years, particularly as the transportation sector reduces its global carbon footprint. Natural fibers, such as flax fiber and their composites, offer a compelling combination of properties including low density, high specific strength, and carbon negativity. However, because of the low modulus and high variability in performance, natural fibers can’t compete with glass fibers as structural reinforcements in polymer composites. In this study, flax technical fibers were treated in supercritical CO 2 (scCO 2 ), and the effects of this treatment on the morphology and properties of flax fibers are reported. Treatment in scCO 2 successfully resulted in higher fiber modulus and strength by 33% and 40%, respectively. Fiber porosity was reduced by 50% and morphological changes to the fibers were observed. Specifically, fiber lumen collapsed during treatment and micro/mesoporosity was reduced by 27%. Treated flax fibers were used to create 30 vol% unidirectional flax-epoxy composites. ScCO 2 treatment raised composite modulus and strength by 33% and 25%, respectively. Because of the dependence between technical fiber size and mechanical properties, the relationship between fiber modulus and fiber size were created and applied to the rule-of-mixtures. This relationship were found to be viable representations of the fiber performance within each composite. Overall, the treatment developed in this study has the potential to significantly improve natural fiber properties, enabling their consideration for use in lightweight, semi-structural composites.

°C/s, which increase solid solubility within aluminum alloys, shifting their eutectic composition to a larger value of silicon content. Consequently, the resulting microstructure combines a strengthened aluminum matrix with silicon fibers. This study focuses on the laser scanning of Al-Si aluminum alloy to reduce the size of aluminum matrix spacings and transform fibrous silicon particles from micrometer to nanometer dimensions. Analysis revealed that the eutectic structure contained 17.55% silicon by weight, surpassing the equilibrium eutectic composition of 12.6% silicon. Microstructure dimensions within the molten zones, termed 'melt pools', were extensively examined using Scanning Electron Microscopy (SEM) at intervals of approximately 20 μm from the surface. A notable increase in hardness, exceeding 50% compared to the base plate, was observed in the melt pool regions. Thus, it is exemplified that laser surface remelting introduces a novel strengthening mechanism in the alloy. Moreover, this study develops an in situ method for predicting melt pool properties and dimensions. A predictive model is proposed, correlating energy density and spectral signals emitted during laser remelting with mechanical properties and melt pool dimensions. This method significantly reduces characterization time from days to seconds, offering a streamlined approach for future studies in additive manufacturing.

Aluminum-steel joints are increasingly used in the automotive industry to meet the requirements for energy saving and emission reduction. Among various joining technologies, self-pierce riveting (SPR) and resistance spot welding (RSW) are two well-established technologies for fabricating dissimilar joints with stable and high mechanical performance. However, corrosion will occur in these joints inevitably due to different electrochemical properties, which can degrade the surface quality and the mechanical performance, such as strength. This paper presents a method of understanding the corrosion mechanisms in joining aluminum and steel. For this understanding, a hybrid method combining experimental observations, mechanical properties identification, and analytical approaches was used to assess the evolution of the impact of corrosion on the joining performance, such as traction separation curves. The study was conducted on common combinations used in the vehicles, e.g., a 1.2 mm thickness aluminum alloy (AA 6022) and 2.0 mm thickness hot deep galvanized steel (HDG HSLA 340) joined by SPR and RSW. After the fabrication of these joints, accelerated cyclic corrosion tests of up to 104 cycles were performed, which reproduced the environmental conditions to which a vehicle was exposed. By investigating the microstructural evolution within the joints, the corrosion mechanisms of SPR and RSW joints were revealed, including the initiation and propagation. Moreover, the intrinsic impact of the corrosion on the mechanical performance, including the strength, axial stiffness, and crashworthiness, was analyzed by performing a lap-shear test. It showed that as corrosion proceeds, the fracture modes and mechanical performance are affected significantly.

Abstract Medically compromised patients causing severe amounts of bone loss in the maxillary/mandibula experience low success rates with dental implants. The bone loss is caused by a weak interfacial connection between the implant and the maxillary/mandibula and because of a stiffness mismatch between bone and titanium. To address this challenge, functionally graded porous dental implants are proposed as a promising solution to mimic the natural tooth by matching the stiffness of the titanium support with the maxillary/mandibula. In this paper, we employed functionally graded materials to design a triply periodic minimal surface (TPMS) gyroid-graded porous structure with five distinct connected porosities leading to a solid core. The study aimed to develop a structure with mechanical properties similar to bone while maintaining mechanical strength to withstand physiological loading. An analytical model for a functionally graded porous structure was utilized to calculate a theoretical elastic modulus in relation to five different porosities along the gradient. The target porosities were determined as 10%, 20%, 30%, 40%, and 50%, with corresponding elastic modulus ranging from 103.94 GPa to 35.25 GPa. Five TPMS solid gyroid samples made of Ti-6Al-4V were designed for a specific stiffness corresponding to the bone, using an analytical model considering an exponential relationship between stiffness and porosity. These structures were then manufactured through selective laser melting, resulting in final porosities of 6.64%, 11.56%, 18.40%, 28.74%, and 36.23%, with corresponding pore sizes of 130 μm, 185 μm, 280 μm, 360 μm, and 420 μm. The stiffness of the designed and manufactured porous structures was validated through uniaxial compressive experiments. Digital Image Correlation (DIC) accurately recorded displacements to calculate strain during compression experiments up to failure. Microscopic analysis of the compression samples’ failure provided insights into the role of pores in sample damage. Results confirmed that the elastic modulus of the porous structures was reduced to the designed values between 21.7–65.3 GPa, compatible with bone. This represents a significant drop in stiffness compared to solid Ti-6Al-4V, for which stiffness is 114 GPa. Through this matching of stiffness between bone and the implant, the so-called stress shielding effect caused by a weak interfacial connection can be reduced, decreasing the risk of implant failure.

Abstract The use of ultrasonic vibrations in manufacturing has been demonstrated its potential pathway to replace heating-assisted forming with significant benefits, including lowering the carbon footprint and increasing cost-effectiveness. This technology utilizes high-frequency mechanical vibrations, typically in the ultrasonic range (20 kHz or higher), to enhance the deformation and flow of metals during drawing, tube bending, punching, equal channel angular pressing, and incremental sheet forming. The utilization of high-frequency vibration in these manufacturing methods has revealed a fascinating interplay between the mechanical properties of metals and the dynamic forces introduced by the ultrasonic vibration. While the application of ultrasonic vibration in metal forming processes has shown promise in surface finish and reducing forming forces, there is still a need for more in-depth studies on the macroscopic response of materials during conventional mechanical testing for optimizing the process and ensuring the quality and reliability of formed components. In this study, the cyclic effect of ultrasonic vibration on the macroscopic material response of commercially pure aluminum, namely Al1100-O, was investigated through the implementation of ultrasonic-assisted compression (UAC) tests. Cyclic vibrations were applied to the compression of Al1100-O around a strain of 10%, 20%, and 30% with increasingly higher vibration amplitudes. Acoustic softening results presented in this work were calculated at each engineering strain. Both acoustic softening and residual effect due to ultrasonic vibration are experimentally verified in commercially pure aluminum in the range of 1μm and 3μm amplitude with 20 kHz vibration frequency. The mechanism of acoustic softening is centered on the localization of acoustic energy at defect sites, such as dislocations. This localization reduces the critical energy required for slip. In contrast, acoustic residual hardening is primarily attributed to the increase in dislocation density, resulting from the multiplication of dislocations induced by ultrasonic vibration. To validate the proposed mechanism, electron backscatter diffraction (EBSD) tests were carried out on formed samples. Comparing the misorientation maps, the overall low angle grain boundaries (LAGB) misorientation density increased for the UA sample, implying a smaller subgrain size. The formation of LAGB is a common method to reduce the overall internal defect density. Greater intensities in the kernel average misorientation (KAM) and geometrically necessary dislocations (GND) maps support the supposition that greater subgrain boundaries are formed during forming. The findings confirmed the significant effects of high-frequency vibration on metal plasticity and provided a basis for understanding the underlying mechanisms of vibrationassisted forming.

In weld-based manufacturing processes such as welding and metal deposition additive manufacturing (AM), the weld bead is a direct indicator of manufacturing quality. For example, the geometry of the weld bead was optimized to a net shape which outperformed conventional geometries. Automatic monitoring of weld bead is thus of prime importance for welding process control and quality assurance. This paper develops a general-purpose convolutional neural network (CNN) for pixel-level detection and monitoring of beads, regardless of welding materials, machine, manufacturing conditions, etc. To achieve the generality, we collected a great variety of welding images containing 2677 single-line beads from 231 research articles, followed by pixel-wise hand-annotation. Consequently, the trained CNN can recognize different beads from various backgrounds at a pixel level. Case studies show that compared to the image-level classification in prior research, its pixel-level labeling permits real-time, complete characterization of weld beads (e.g., detailed morphology, discontinuity, spatter, and uniformity) for more informed process control. This research represents a significant step towards developing a truly human-like monitoring system with low-level scene understanding ability and general applicability.

This special issue presents a collaborative initiative between the ASME Manufacturing Engineering Division (MED) and the Design Engineering Division (DED) to promote research in sustainability within the design and manufacturing communities. As the need grows for methodologies and tools capable of supporting sustainable systems, this compilation presents recent research trends exploring the integration of sustainability principles into and progression toward sustainability goals by the design and implementation of engineered systems.We were pleased to receive submissions covering a wide range of design and manufacturing topics. The quality of the submissions and the enthusiastic participation of the community were notable. Following a rigorous peer-review process, 11 submissions were ultimately selected for publication. The papers are published within special sections of the Journal of Mechanical Design (JMD) and the Journal of Manufacturing Science and Engineering (JMSE).The chosen submissions for the special issue cover research within diverse sustainability-related topics categorized into six main groups: (1) design for remanufacturing, (2) advancements in sustainability assessment, (3) design interventions for user-sustainable behavior, (4) advancing disassembly practices, (5) techno-economic analysis of energy systems, and (6) innovative tools for teaching sustainability, as detailed below.Design for Remanufacturing: This category features two papers. The paper by Behtash and colleagues introduces a comprehensive framework, 'Reman Co-Design,' merging design and remanufacturing optimization for enhanced sustainability performance. The paper by Alves and co-workers addresses textile waste by proposing 12 design for sustainability and flexibility principles. The work showcases the development and testing of a mechanical textile recycling system for efficient material recovery.Advancements in Sustainability Assessment: This category explores innovative technologies for sustainability assessment, covering three papers. The paper by Mabey and co-workers introduces an approach for predicting social, environmental, and economic impacts of products through agent-based modeling and life cycle assessment. The work of Karkaria and colleagues proposes a machine learning-based framework for predicting tire life in the commercial freight industry by focusing on product usage data. The paper by Liao and co-workers investigates the capabilities of AI algorithms for the automated evaluation and rating of product repairability.Interventions for Sustainable Behavior: This category explores interventions designed to drive behavioral change toward improving sustainability performance. The paper by Halabieh and Shu explores the effect of limiting wastewater outflows in reducing water consumption and influencing water-conserving behavior. The paper by Rea and colleagues investigates the relationship between thermostat interaction and temperature selection in warm ambient conditions and shows how innovative intervention offers insights into designing products that foster energy-conscious behaviors.Advancing Disassembly Practices: This category investigates disassembly strategies critical for sustainable product life cycles. The paper by Rodríguez and Favi proposes an eco-design methodology for mechatronic products, focusing on repairability and circular economy principles. The paper by Lee and colleagues explores the prospects and challenges of introducing human-robot collaboration in product disassembly. Recognizing the inefficiency of manual disassembly, the study reviews recent progress in robotic disassembly, emphasizing the potential benefits of combining human skills with robotic precision.In addition to the aforementioned topics, the remaining two papers address techno-economic analysis and sustainability education. The paper by Li and Zhang examines the techno-economic dynamics of co-located wind and hydrogen energy systems within an integrated energy system. The paper by Raoufi and Haapala shifts focus to teaching sustainability concepts to non-experts using an analysis tool designed to facilitate sustainability performance analysis of manufacturing processes and systems.We hope these special sections of JMD and JMSE lay the foundations for ongoing and future research in sustainable design and manufacturing. Furthermore, we hope this compilation of papers has established a pathway for disseminating the attendant research findings to future visionary leaders across an array of disciplines to advance the application of sustainable systems engineering research within our society and industry.Special gratitude goes to Dr. Carolyn Seepersad, Editor-in-Chief of JMD; Dr. Albert Shih, Editor-in-Chief of JMSE; and Dr. Wei Chen, former Editor-in-Chief of JMD, for their invaluable support, leadership, and endorsement of the idea behind this joint special issue on Advances in Design and Manufacturing for Sustainability. We also extend our sincere appreciation to Amy E. Suski and Emily Bosco, editorial assistants of JMD and JMSE, for their significant support and efficient collaboration throughout the paper review and production process.We also express our gratitude to all contributors who responded to the call and enriched the joint special issue with their insightful submissions. A special acknowledgment goes to the reviewers for their generous time and insightful evaluations. In addition, our thanks extend to the technical committees of ASME DED and ASME MED for disseminating information about the special issue within their respective communities.

Porous biomaterials design for bone repair is still largely limited to regular structures (e.g. rod-based lattices), due to their easy parameterization and high controllability. The capability of designing stochastic structure can redefine the boundary of our explorable structure-property space for synthesizing next-generation biomaterials. We hereby propose a convolutional neural network (CNN) approach for efficient generation and design of spinodal structure-an intriguing structure with stochastic yet interconnected, smooth, and constant pore channel conducive to bio-transport. Our CNN-based approach simultaneously possesses the tremendous flexibility of physics-based model in generating various spinodal structures (e.g. periodic, anisotropic, gradient, and arbitrarily large ones) and comparable computational efficiency to mathematical approximation model. We thus successfully design spinodal bone structures with target anisotropic elasticity via high-throughput screening, and directly generate large spinodal orthopedic implants with desired gradient porosity. This work significantly advances stochastic biomaterials development by offering an optimal solution to spinodal structure generation and design.