Material Formulation Research Articles

Unveiling the Potential of Civil Briquette Furnace Slag as a Silico–Aluminon Additive in Alkali-Activated Materials

Civil briquette furnace slag (FS), as a type of industrial solid waste, is not currently being recycled as a resource by the building materials industry. This study focuses on the potential of FS in the formulation of alkali-activated materials (AAMs) compared with calcium carbide slag (CS). This study encompasses three distinct AAM systems: alkali-activated fly ash alone (AAFA), fly ash–slag powder blends (AAFB), and slag powder alone (AABS). Electrical conductivity, fluidity, drying shrinkage, and flexural and compressive strengths were also assessed. Advanced characterization techniques, including SEM-EDS, XRD, FTIR, and TG-DSC, were utilized to examine the morphology, mineralogy, and reaction products. Despite the chemical similarity between FS and CS, FS exhibits limited active chemical components (SiO2, Al2O3, CaO, and MgO) and primarily functions as a physical filler, and thus lacks the chemical binding properties of CS. FS has a positive effect on the long-term compressive strength of the AABS system but not on the AAFA and AAFB systems. The NaOH-activated SP mortar sample with 20% FS reaches a compressive strength of 29.8 MPa at 360 days. The binding strength in AAMs incorporating FS is predominantly attributed to the gel formation within the alkali-activated matrix. This research offers valuable insights into the strategic use and substitution of CS, FS, and other silico–aluminon additives within the context of AAMs development.

Ultralight cellulose nanofiber based foams: the effects of material formulation and nanofiber type on mechanical properties and thermal insulation

Ultralight cellulose nanofiber based foams: the effects of material formulation and nanofiber type on mechanical properties and thermal insulation

Sculpting Mechanical Properties of Hydrogels by Patterning Seamlessly Interlocked Stiff Skeleton

AbstractFunctional soft materials, especially hydrogels have been widely developed to achieve various soft structures and machines. However, synthetic hydrogels commonly show formula‐dependent mechanical properties to fulfill the requirements of mechanical elasticity, stiffness, toughness, and tearing‐resistance for adapting to complex application scenario. Inspired by heterostructures and materials found in nature such as leaves and insect wings, a sequential photopolymerization process combined with site‐selective patterning exposure is reported to prepare programmable hydrogels with locally heterogeneous reinforcement skeletons, i.e., interpenetrating double networks. The heterogeneous interface between soft matrices and stiff skeletons is seamlessly interlocked through strong multiple hydrogen bonds induced by phase transition. By harnessing the size, shape, and distribution of the patterned stiff skeletons, a wide range of mechanical properties of hydrogels including modulus (0.32–5.92 MPa), toughness (0.15–18 kJ m−2), dissipated energy (1–100 kJ m−3), impact resistance, and mechanical anisotropy can be readily sculpted within one material system without needing design and optimization of the complex and elusive material formulation on demand. It is believed that this simple yet powerful method relying on heterogenous patterning would guide the development of functional hydrogel materials with programmable mechanical properties toward potential engineering applications, such as damping and flexible circuits.

Product Development of Coco-Squash Spread

Developing new food products like Coco-Squash Spread is crucial for addressing global food challenges. By diversifying food sources and utilizing locally available ingredients such as coconut milk and squash, this research aligns with sustainable agriculture principles. Moreover, if Coco-Squash Spread can effectively utilize food byproducts or surplus produce, it can contribute to reducing food waste. The Coco-Squash Spread product has the potential to provide essential nutrients, thereby combating malnutrition. The research encompassed product formulation and sensory evaluation. Quantitative descriptive analysis was employed to assess sensory attributes. The rank preference test was used to determine the most preferred sample of Coco-Squash Spread that evaluated with the same respondents. The research methodology integrated developmental, evaluative, and descriptive approaches. Sensory characteristics, including appearance, aroma, taste, and texture, were influenced by the formulation of raw materials and additional ingredients. Overall, the study provides preliminary insights into the potential of Coco-Squash Spread as a new food product, warranting further research to optimize formulation and explore market potential.

Valorizing brewery industry waste in nanocellulose cryogel-PEG composites for cold chain packaging

Global concerns about food and energy waste call for new sustainable solutions. Phase changing materials (PCM) are promising thermal storage and management materials which have potential to mitigate food waste, yet they often suffer from leakage and non-shape stability. Here, a green shape-stabilized polyethylene glycol 300 (PEG) nanocellulose-based composite was developed using readily available and cost-effective side streams from the brewery industry. Two different routes were examined for the incorporation of PEG into the cellulose nanofiber (CNF) cryogel including adding different dry mass ratios of PEG in the sol stage, with or without physical crosslinking, or adding the PEG after CNF gelation. FTIR and TGA analysis revealed the improved thermal stability of PEG after incorporation into the CNF cryogel. The PEG/CNF composite produced by adding PEG in the gel stage outperformed the other material formulations by demonstrating the highest enthalpy value (93.2 J/g) and the best mechanical properties (Young's modulus of 140 kPa). The package containing the PEG/CNF composite could maintain the meat temperature below 0 °C around 4 times longer than the control package when the cold system was interrupted. Furthermore, the PEG/CNF composite showed a positive effect on the color and drip loss values of frozen meat.

Recent developments in the mechanical properties and recycling of fiber‐reinforced polymer composites

AbstractFiber‐reinforced polymer composites (FRPCs) have become integral to various industries due to their exceptional strength‐to‐weight ratio, corrosion resistance, and versatility. Recent advancements in the properties and recycling of FRPCs reflect significant progress in performance and sustainability. This paper reviews the latest developments in FRPC technology, highlighting innovations in material formulation, including advancements in fiber types, matrix materials, and hybrid composites that enhance mechanical properties. Furthermore, this review emphasizes the modification of matrices by incorporating graphene, which aims to improve the chemical bonding and mechanical interlocking between fiber and matrix. Additionally, it addresses recent breakthroughs in recycling technologies, focusing on methods such as chemical recycling, mechanical recycling, and developing eco‐friendly matrices. Integrating these advancements aims to improve the lifecycle management of FRPCs, reduce environmental impact, and support the transition towards a circular economy. This review underscores the balance between enhancing composite performance and promoting sustainable practices, paving the way for more environmentally responsible applications of FRPCs.Highlights The different types of fiber‐reinforced polymer composites have been thoroughly reviewed. How does graphene affect the mechanical behavior of fiber composite laminates? Provide a systematic correlation and comparison between fabrication methods, materials, and properties. The recycling methods for fiber‐reinforced polymer composites have been deliberated.

Mechanism and optimization of sintered ultra-lightweight aggregates with sand washing slurry and red mud

Sand washing slurry, red mud, and waste sawdust are major solid wastes produced by the construction, mining, and wood processing industries, respectively. Their accumulation poses significant environmental hazards and results in substantial wastage of resources. This study utilized a novel rapid roasting method to prepare ultra-lightweight ceramsite aggregates (ULC) using sand washing slurry, red mud, and sawdust as raw materials. A new process for preparing ultra-lightweight clay aggregates (ULCA) utilizing waste sand washing sludge is proposed, achieving a utilization rate of the wash sand sludge exceeding 60%. Ultra-lightweight clay aggregates with a loose bulk density of 264.3 kg/m3 were produced. The influence of material formulation on the performance of ultra-lightweight clay aggregates was studied. The phase changes and expansion mechanism of the clay aggregates were revealed from microscopic and chemical perspectives. The synergistic effect of red mud and sawdust significantly enhanced the foaming effect of the ceramsite (i.e., porosity), reducing its loose bulk density while slowing the rate of strength reduction. Additionally, increasing the content of red mud and sawdust promoted ceramsite expansion. When the mass ratio of sand washing slurry, red mud, and sawdust was 29: 12: 1, with Fe2O3 content at 13% and sawdust content at 3%, preheating at 500°C for 5 minutes followed by roasting at 1150°C for 5 minutes produced ultra-lightweight ceramsite with a compressive strength of 1.7 MPa. This study provides a promising method for producing ULC from industrial solid wastes.

Optimizing Thermoplastic Starch Film with Heteroscedastic Gaussian Processes in Bayesian Experimental Design Framework.

The development of thermoplastic starch (TPS) films is crucial for fabricating sustainable and compostable plastics with desirable mechanical properties. However, traditional design of experiments (DOE) methods used in TPS development are often inefficient. They require extensive time and resources while frequently failing to identify optimal material formulations. As an alternative, adaptive experimental design methods based on Bayesian optimization (BO) principles have been recently proposed to streamline material development by iteratively refining experiments based on prior results. However, most implementations are not suited to manage the heteroscedastic noise inherently present in physical experiments. This work introduces a heteroscedastic Gaussian process (HGP) model within the BO framework to account for varying levels of uncertainty in the data, improve the accuracy of the predictions, and increase the overall experimental efficiency. The aim is to find the optimal TPS film composition that maximizes its elongation at break and tensile strength. To demonstrate the effectiveness of this approach, TPS films were prepared by mixing potato starch, distilled water, glycerol as a plasticizer, and acetic acid as a catalyst. After gelation, the mixture was degassed via centrifugation and molded into films, which were dried at room temperature. Tensile tests were conducted according to ASTM D638 standards. After five iterations and 30 experiments, the films containing 4.5 wt% plasticizer and 2.0 wt% starch exhibited the highest elongation at break (M = 96.7%, SD = 5.6%), while the films with 0.5 wt% plasticizer and 7.0 wt% starch demonstrated the highest tensile strength (M = 2.77 MPa, SD = 1.54 MPa). These results demonstrate the potential of the HGP model within a BO framework to improve material development efficiency and performance in TPS film and other potential material formulations.

Analysis Based on Three Aspects of Sunscreen

With the increasing awareness of health, people’s awareness of UV protection is also gradually improving. Sunscreen with UV protection has become an indispensable part of people’s daily lives. The market size of China’s sunscreen market will reach 74.2 billion yuan in 2023, and it is expected to grow to 95.8 billion yuan by 2026. The purpose of this paper is to analyze the safety, effectiveness, and environmental friendliness of sunscreen from three perspectives, and discuss the reliability of SPF human testing, improving the stability of sunscreen use, and the cost of new environmentally friendly materials. Through analysis, it is found that there are still shortcomings in the current SPF testing methods, production costs of environmentally friendly materials, and promotion of correct application methods for sunscreen. Subsequently, appropriate adjustments were made to the testing methods and material formulations of sunscreen, as well as recommendations for the selection and correct application of sunscreen. However, the long-term safety of nanomaterials still needs to be further explored. Due to the rapid development of research technology for sunscreen products and the diversification of market products, it is necessary to review the safety, effectiveness, and environmental friendliness of current sunscreen products.

UV-Curable Polymer Nanocomposites: Material Selection, Formulations, and Recent Advances

This study addresses the development of UV-curable polymer nanocomposites (PNCs), mainly based on acrylate, emphasizing material selection and formulation strategies that achieve efficient dispersion of the nanofillers (NFs). We begin by exploring various types of UV-curing coatings and delve deeper into their key components: monomers, oligomers, photoinitiators, fillers, and additives. Different types of components and examples are presented. Furthermore, this study delves into the critical importance of modifying NFs to tune the physical properties of the composite. It provides an overview of commonly used NFs and underscores the importance of surface modification (chemical and physical) as a pivotal technique for producing high-performance UV-curable PNCs. Additionally, various additives such as adhesion promoters, anti-foaming agents, and wetting and dispersing agents are discussed, emphasizing their functions within the formulation process. Different dispersion and blending methods are also discussed. The paper concludes by summarizing and presenting recent advancements in the formulation of UV-curable PNCs. This overview offers valuable insights to researchers and engineers working on the development of advanced materials.

Understanding of Wetting Mechanism Toward the Sticky Powder and Machine Learning in Predicting Granule Size Distribution Under High Shear Wet Granulation.

The granulation of traditional Chinese medicine (TCM) has attracted widespread attention, there is limited research on the high shear wet granulation (HSWG) and wetting mechanisms of sticky TCM powders, which profoundly impact the granule size distribution (GSD). Here we investigate the wetting mechanism of binders and the influence of various parameters on the GSD of HSWG and establish a GSD prediction model. Permeability and contact angle experiments combined with molecular dynamics (MD) simulations were used to explore the wetting mechanism of hydroalcoholic solutions with TCM powder. Machine learning (ML) was employed to build a GSD prediction model, feature importance explained the influence of features on the predictive performance of the model, and correlation analysis was used to assess the influence of various parameters on GSD. The results show that water increases powder viscosity, forming high-viscosity aggregates, while ethanol primarily acted as a wetting agent. The contact angle of water on the powder bed was the largest and decreased with an increase in ethanol concentration. Extreme Gradient Boosting (XGBoost) outperformed other models in overall prediction accuracy in GSD prediction, the binder had the greatest impact on the predictions and GSD, adjusting the amount and concentration of adhesive can control the adhesion and growth of granules while the impeller speed had the least influence on granulation. The study elucidates the wetting mechanism and provides a GSD prediction model, along with the impact of material properties, formulation, and process parameters obtained, aiding the intelligent manufacturing and formulation development of TMC.

Smart and Sustainable 3D-Printed Nanocellulose-Based Sensors for Food Freshness Monitoring.

Annually, about one-third of the food produced around the world is wasted due to spoilage. Food contamination and spoilage, along with the use and disposal of nondegradable packaging materials, impact human health and have huge economic and sustainability implications. Achieving sustainability within the food system requires innovative solutions to reduce the environmental footprint. Herein, we describe the formulation, scalable manufacturing, and characterization of three-dimensional (3D)-printed sensors prepared from a mixture of edible biopolymer hydrogels, 8% alginate, and 10% gelatin and nanocellulose (CNC) as a reinforcement filler. We demonstrate that incorporating CNC improves the overall mechanical performance of the printed film and enables the stabilization of pH-responsive dyes for monitoring the release of total volatile basic nitrogen (TVB-N), an indicator of food freshness. Mechanical performance enhancement includes increases of 43% in load-depth indentation, 28.2% in hardness, and 17.4% in elastic modulus. This enhancement facilitates its use as a smart label technology, enabling the visual assessment of spoilage when placed inside packaging over a period of 3 days at room temperature. The 3D-printed film exhibits excellent durability, flexibility, shape memory, and robustness, along with pH responsiveness, showing distinctive color changes over the pH range of 2 to 13. These performances are demonstrated in packaged meat and fish, enabling monitoring over several days and illustrating potential as a real-time freshness indicator. The material formulations developed in this work are biodegradable, eco-friendly, and inexpensive, making them suitable candidates for smart and sustainable food packaging.

Effect of Material Extrusion Process Parameters to Enhance Water Vapour Adsorption Capacity of PLA/Wood Composite Printed Parts

This study aims to optimise the water vapour adsorption capacity of polylactic acid (PLA) and wood composite materials for application in dehumidification systems through material extrusion additive manufacturing. By analysing key process parameters, including nozzle diameter, layer height, and temperature, the research evaluates their impact on the porosity and adsorption performance of the composite. Additionally, the influence of different infill densities on moisture absorption is investigated. The results show that increasing wood content significantly enhances water vapour adsorption, with nozzle diameter and layer height identified as the most critical factors. These findings confirm that composite materials, especially those with higher wood content and optimised printing parameters, offer promising solutions for improving dehumidification efficiency. Potential applications include heating, ventilation, and air conditioning systems or environmental control. This work introduces an innovative approach to using composite materials in desiccant-based dehumidification and provides a solid foundation for future research. Further studies could focus on optimising material formulations and scaling this approach for broader industrial applications.

Sustainable Composites from Waste Polypropylene Added with Thermoset Composite Waste or Recovered Carbon Fibres

In order to limit the ever-increasing consumption of new resources for material formulations, regulations and legislation require us to move from a linear to a circular economy and to find efficient ways to recycle, reuse and recover materials. Taking into account the principles of material circularity and waste reuse, this research study aims to produce thermoplastic composites using two types of industrial waste from neighbouring companies, namely waste polypropylene (wPP) from household production and carbon-fibre-reinforced epoxy composite scrap from a pultrusion company. The industrial scrap of the carbon-fibre-reinforced epoxy composites was either machined/ground to powder (pCFRC) and used directly as a reinforcement agent or subjected to a chemical digestion process to recover the carbon fibres (rCFs). Both pCFRC and rCF, at different weight ratios, were melt-blended with wPP. Prior to melt blending, both pCFRC and rCF were analysed for morphology by scanning electron microscopy (SEM). The pCFRC powder contains epoxy resin fragments with spherical to ellipsoidal shape and carbon fibre fragments. The rCFs are clean from the matrix, but they are slightly thicker and corrugated after the matrix digestion. Further, the morphologies of wPP/pCFRC and wPP/rCF were also investigated by SEM, while the thermal behaviour, i.e., transitions and changes in crystallinity, and thermal resistance were evaluated by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA), respectively. The strength of the interaction between the filler (i.e., pCFRC or rCF) and the wPP matrix and the processability of these composites were assessed by rheological studies. Finally, the mechanical properties of the systems were characterised by tensile tests, and as found, both pCFRC and rCF exert reinforcement effects, although better results were obtained using rCF. The wPP/pCFRC results are more heterogeneous than those of the wPP/rCF due to the presence of epoxy and carbon fibre fragments, and this heterogeneity could be considered responsible for the mechanical behaviour. Further, the presence of both pCFRC and rCF leads to a restriction of polymer chain mobility, which leads to an overall reduction in ductility. All the results obtained suggest that both pCFRC and rCF are good candidates as reinforcing fillers for wPP and that these complex systems could potentially be processed by injection or compression moulding.

Energy-efficient FDM printing of sustainable polymers: Optimization strategies for material and process performance

The integration of sustainable polymers in fused deposition modeling (FDM) 3D printing offers a promising pathway toward reducing the environmental impact of additive manufacturing. However, the energy-intensive nature of FDM processes presents a significant challenge to the overall sustainability of this technology. In this study, we explore the use of bio-based and recycled polymers in FDM printing and develop optimization strategies to reduce energy consumption without compromising material and print performance. Our results demonstrate that by systematically optimizing key printing parameters such as extrusion temperature, print speed, and layer height it is possible to achieve up to 20% energy savings. Additionally, we find that novel material formulations and advanced thermal management techniques enhance the mechanical properties of printed objects by up to 15%, all while minimizing energy use. This research not only advances the field of sustainable 3D printing but also provides a framework for the development of next-generation materials and processes that align with the principles of a circular economy.

Peridynamic computations for thin elastic rods

AbstractPeridynamics is a nonlocal continuum mechanics formulation well suited for simulating dynamic fracture phenomena. Various peridynamic material formulations have been developed in recent years. These models have some differences, particularly regarding the correct handling of elastic deformation. This study investigates the elastic wave propagation characteristics of bond‐based, ordinary state‐based, continuum‐kinematics‐inspired peridynamics and a local continuum consistent correspondence formulation. Specifically, it examines the behavior of longitudinal pressure waves within thin rods. While all formulations demonstrate adequate wave propagation handling, all except the correspondence formulation are sensitive to incomplete horizons. In contrast, the local continuum consistent formulation exhibits outstanding accuracy in modeling wave propagation. Moreover, the fascinating “spaghetti fracture” phenomenon, where a bent thin rod always breaks into three or more pieces, motivates additional investigations. It is shown that reproducing a different number of fragments using peridynamic simulations is possible. Although initial experiments can be replicated, the sensitivity to numerous parameters necessitates further investigations for a more comprehensive understanding and validation.

Unified non-hourglass formulation for total Lagrangian SPH solid dynamics

AbstractThe persistence of hourglass modes poses a significant numerical instability issue in total Lagrangian smoothed particle hydrodynamics (TLSPH) solid dynamics, especially when dealing with substantial deformations, regardless of material properties. However, existing hourglass control methods have shown effectiveness only within limited applications. Thus far, a comprehensive solution capable of addressing hourglass issues across a wide range of material models, including elasticity, plasticity, and anisotropy, remains elusive. In this study, we introduce a unified TLSPH formulation grounded in volumetric-deviatoric stress decomposition, aimed at fundamentally mitigating hourglass modes in general simulations. Different conceptually from previous approaches using stress points or extra viscous or hourglass-control stresses within the momentum equation, our formulation is based on the weighted average of a standard but hourglass-prone formulation and an essentially non-hourglass formulation for elastic materials, employing a single limiter to dynamically adjust the weighting between the two formulations. Crucially, the dimensionless characteristic of the formulation enables seamless handling of complex material models. To validate the effectiveness of our formulation, we conduct simulations across a range of benchmark cases involving elastic, plastic, and anisotropic materials. To illustrate its versatility, we apply the formulation to simulate a complex scenario involving viscous plastic Oobleck material, contacts, and very large deformation. Our work addresses a critical gap in TLSPH simulations by offering a unified approach to mitigate hourglass modes, enhancing the reliability and accuracy of simulations across diverse material models and complex scenarios.

Environmental Impact of Polyurethane-Based Aerogel Production: Influence of Solvents and Solids Content

This study provides a comprehensive analysis of the environmental impacts associated with the synthesis of polyurethane (PUR) aerogels. The synthesis process incorporates various solvents and solids contents into the formulation, with the primary objective of enhancing the physical properties of the aerogels for broad industrial applications. Nine experimental scenarios were explored, grouped into two sets based on the variables studied. A detailed Life Cycle Assessment (LCA) was conducted to evaluate the environmental impacts of all formulated PUR aerogels. The findings indicate that a solvent solution of 100% ethyl acetate (EtOAc) results in lower environmental impacts compared to other tested formulations. Notably, a solvent solution comprising 75% acetonitrile (ACN) and 25% EtOAc exhibited the highest environmental Key Performance Indicator (εKPI) among the tested material formulations, closely followed by the PUR aerogel obtained using acetone as a solvent. Furthermore, this study underscores the necessity of performing an integrated LCA that considers both environmental and functional aspects. While reducing the solids content is environmentally advantageous, it may present challenges in terms of material functionality. This is illustrated by the PUR aerogel synthesized with the lowest solids content of 3.2 wt.%, which demonstrated high deformability, thereby complicating the determination of a reliable Young’s modulus for analysis.

Non-invasive regional parameter identification of degenerated human meniscus

Accurate identification of local changes in the biomechanical properties of the normal and degenerative meniscus is critical to better understand knee joint osteoarthritis onset and progression. Ex-vivo material characterization is typically performed on specimens obtained from different locations, compromising the tissue's structural integrity and thus altering its mechanical behavior. Therefore, the aim of this in-silico study was to establish a non-invasive method to determine the region-specific material properties of the degenerated human meniscus. In a previous experimental magnetic resonance imaging (MRI) study, the spatial displacement of the meniscus and its root attachments in mildly degenerated (n = 12) and severely degenerated (n = 12) cadaveric knee joints was determined under controlled subject-specific axial joint loading. To simulate the experimental response of the lateral and medial menisci, individual finite element models were created utilizing a transverse isotropic hyper-poroelastic constitutive material formulation. The superficial displacements were applied to the individual models to calculate the femoral reaction force in an inverse finite element analysis. During particle swarm optimization, the four most sensitive material parameters were varied to minimize the error between the femoral reaction force and the force applied in the MRI loading experiment. Individual global and regional parameter sets were identified. In addition to in-depth model verification, prediction errors were determined to quantify the reliability of the identified parameter sets. Both compressibility of the solid meniscus matrix (+141 %, p ≤ 0.04) and hydraulic permeability (+53 %, p ≤ 0.04) were significantly increased in the menisci of severely degenerated knees compared to mildly degenerated knees, irrespective of the meniscus region. By contrast, tensile and shear properties were unaffected by progressive knee joint degeneration. Overall, the optimization procedure resulted in reliable and robust parameter sets, as evidenced by mean prediction errors of <1 %. In conclusion, the proposed approach demonstrated high potential for application in clinical practice, where it might provide a non-invasive diagnostic tool for the early detection of osteoarthritic changes within the knee joint.

Maximizing machining efficiency and quality in AA7075/Gr/B4C HMMCs through advanced DS-EDM parameter optimization strategies

Abstract Hybrid Metal Matrix Composites (HMMC) offer improved mechanical properties crucial for various industrial applications. However, machining these composites via traditional methods is arduous due to their inherent hardness and abrasive nature. The optimization of the Die-Sinking Electrical Discharge Machining (DS-EDM) process presents a viable solution to overcome these machining challenges and unlock the full potential of HMMCs. Despite the potential benefits of HMMCs, the lack of optimized machining strategies hampers their widespread adoption in industrial applications. Traditional machining approaches often result in excessive tool wear, poor surface finish, and suboptimal material removal rates (MRR) when applied to HMMCs. Additionally, the complex interplay between DS-EDM process parameters and material properties necessitates a systematic investigation to identify optimal machining conditions. This study proposes a multi-step methodology to systematically optimize the DS-EDM process for machining HMMCs comprising AA7075 alloy, 5% of graphite (Gr), and 5% of boron carbide (B4C) particles, which is termed as (AA7075-GR-B4C). Beginning with the formulation of the composite material through stir casting, the research progresses to an L16 Orthogonal Array (L16-OA) based Design of Experiments (DoE) to investigate the effects of key process parameters on machining performance. Experimental testing is conducted to measure mechanical properties, followed by statistical analysis using Analysis of Variance (ANOVA) with Regression Analysis to identify significant factors influencing machining outcomes. Furthermore, an Artificial Neural Network with Black Widow Optimization (ANN-BWO) approach is employed to optimize machining performance metrics such as MRR, Surface Roughness Rate (SRR), and Tool Wear Rate (TWR). The Relative Error (RE) was estimated between experimental MRR (E-MRR), experimental TWR (E-TWR), experimental SRR (E-SRR), and predicted MRR (P-MRR), predicted TWR (P-TWR), predicted SRR (P-SRR) generated by ANN-BWO shows effectiveness of AA7075-GR-B4C.