Combination Of Process Parameters Research Articles

Feasibility analysis and parameter optimization of ultrasonic assisted electrolytic plasma polishing of small modulus gears

Precision polishing of small modulus gears is a difficult task due to the complexity of their geometry. Electrolytic plasma polishing technology as a green and efficient processing method, it can effectively decrease the friction between parts and improve the service life. However, considering the uneven distribution of gas layers on the surface of workpieces in electrolytic plasma polishing, which affects the precision of workpiece shapes after polishing, this study proposed the use of ultrasonic vibrations to generate pressure waves to improve the fluid characteristics. Comparative experiments on small modulus gear electrolytic plasma polishing with and without ultrasonic vibration were conducted. A high-speed camera was utilized to monitor the distribution of gas layers around the workpiece during the polishing process in real-time. The optical measurement methods were used to evaluate the gear precision. The results indicate that ultrasonic assistance is beneficial in improving the uniformity of the gas layers surrounding the workpiece, achieving higher gear precision, and obtaining a lower surface roughness after polishing. Through orthogonal experiments, this study analyzed the effects of various process parameters on the surface roughness of gear profiles and the uniformity of material removal after polishing. These parameters included power voltage, polishing time, workpiece immersion depth, ultrasonic frequency, and workpiece posture angle. An optimal combination of process parameters was identified. The results show that the best surface roughness and gear precision were achieved with a power voltage of 300 V, a polishing time of 4 min, a workpiece immersion depth of about 10 mm, an ultrasonic frequency of 40 kHz, and a workpiece tilt angle of 45°.

Multi-objective optimization in EDM of functionally graded Fe-Al using grey relational analysis

Abstract Functionally Graded Materials (FGMs) are multipurpose materials with a specific goal of managing changes in structural, thermal, or functional qualities. They feature a spatial variation in microstructure and/or composition. The current work prepared three layers of sample with different chemical compositions of Fe-Al FGM (50 Al-50 Fe, 45 Al-55 Fe, and 40 Al-60 Fe) at. % produced by powder metallurgy, then studied the machining behavior of these samples. The literature on the machining behavior of FGM was reviewed, and the influence of electrical discharge machine (EDM) process parameters such as voltage (V), current (I), pulse-on time, and pulse-off time on performance characteristics (material removal rate (MRR), tool wear rate (TWR), and surface roughness (Ra)) was investigated. The optimal machining settings for Fe-Al FGM samples will be ascertained by use of Grey Relational Analysis (GRA), which is based on the Taguchi approach, after an experimental investigation of the L18 orthogonal array design of trials. The GRA findings verify that V 140 volts, Ip10 A, Ton 100 μs, and Toff 75 μs is the optimal combination of process parameters. It has been found that the voltage is more significantly affected than the rest of the input parameters to obtain greater material removal rate (MRR) and lower tool wear rate (EWR) and surface roughness (Ra) through the response table. According to the study, choosing the right process parameters can improve the multi-performance feature.

Experimental investigation on in-situ laser-assisted mechanical ruling of single crystal silicon

Abstract In this paper, response surface methodology (RSM) was employed as a robust and convenient predictive tool to establish the correlation between process parameters of in situ laser-assisted mechanical ruling and the ductile-to-brittle transition of single-crystal silicon. The interaction effects among three factors laser power, ruling speed, and negative rake angle on the ductile-to-brittle transition of single-crystal silicon were investigated. The optimal combination of process parameters was determined to be a laser power of 30W, a ruling speed of 0.25 mm s−1, and a negative rake angle of 58°. Utilizing a self-assembled setup and the optimal process parameters, multiple processing experiments were conducted. The average error between the experimental and predicted values was found to be 2.8%.

Experimental Study on the Pelleting and Coating Performance of Red Clover Seeds

This study aimed to optimize the pelleting and coating process for red clover seeds, addressing the issue of low pelleting success rates. Through theoretical analysis and experimental research, coating pan fill rate, powder supply quantity, and pelleting time were identified as key factors influencing the pelleting success rate. Single-factor experiments were conducted to investigate the effects of these parameters on the quality of red clover seed pelleting and coating. Based on these results, orthogonal trials were carried out, and response surface analysis was employed to reveal the influence patterns and interactions of each factor. The research results indicate that the factors affecting the pelleting success rate, ranked in order of importance, are coating pan fill rate, pelleting time, and powder supply quantity. Through mathematical model optimization, the optimal combination of process parameters was determined to be coating pan fill rate of 35.9%, powder supply quantity of 160.2 g, and pelleting time of 6.9 s. Under these conditions, a pelleting success rate of 94.3% was achieved in validation experiments. This study provides a theoretical foundation and practical guidance for optimizing the pelleting and coating process of red clover seeds, which is significant for improving seed coating quality and promoting red clover cultivation.

Parameter Optimization Method for Centrifugal Feed Disc Discharging Based on Numerical Simulation and Response Surface

In this study, a centrifugal feeding disc device is proposed. To investigate the influence of the process parameters on the discharging efficiency and the lifting of the discharging efficiency, the centrifugal feeding disc device was dynamically simulated based on the discrete element method (DEM), and the simulation results were experimentally verified. Based on the quadratic regression orthogonal test method, a significant lossless regression model of process parameters and discharging efficiency was established, and the response surface of the interaction of process parameters was obtained. The results indicated that the order of influence of the process parameters on the discharging speed of the centrifugal feeding disc was as follows: outer turntable speed > inner turntable speed > inner turntable tilt angle > conical turntable angle. The interaction of the conical turntable angle and the inner turntable tilt angle had the greatest influence on the centrifugal feed disc discharge efficiency. The response surface method (RSM) was used to optimize the process parameters, and the optimal combination of process parameters included an outer turntable speed of 135 r/min, an inner turntable speed of 64 r/min, an inner turntable tilt angle of 7°, and a conical angle of 15°. The discharged efficiency of the optimized centrifugal feeding disc device was increased by 31.9%.

Optimization of hydrogel extrusion printing process parameters based on numerical simulation

The printing quality of biological scaffold is not only affected by the fluidity of bio-ink but also by the printing process parameters, such as the size of the needle, printing height, extrusion speed, and printing speed. Therefore, optimizing the printing process parameters can further improve the molding quality of the biological scaffold. In this study, the printing and deposition process of sodium alginate hydrogel was modeled and analyzed based on the Herschel–Bulkley model by the finite element simulation method. The orthogonal experiment method, control variable method, and response surface method were used to design experiments, and the influences of different printing process parameters on the hydrogel deposition process were investigated. Finally, the optimal combination of printing process parameters was obtained by taking the molding degree and offset of the hydrogel line as optimization objectives. The results show that the strength relationship of the factors affecting the molding degree of the hydrogel line is as follows: printing height > needle diameter > printing speed > extrusion speed, and the strength relationship of the factors affecting the printing offset is as follows: printing height > needle diameter > extrusion speed > printing speed. The optimal combination of printing process parameters is d = 0.34 mm, H = 0.51 mm, v1 = 10 mm/s, and v2 = 7.91 mm/s. Compared with the printing experiment results of the hydrogel line molding degree under the optimal process parameters, the error range is within −11.55%–1.27%, which further demonstrates the reliability of the optimization method of hydrogel extrusion printing process parameters based on numerical simulation and response surface method.

Optimization of Magnetic Field-Assisted Laser Cladding Based on Hierarchical Analysis and Gray Correlation Method

Process parameters directly affect the quality of laser cladding. In this study, magnetic field-assisted laser cladding experiments were carried out on the surface of 300 M ultra-high-strength steel by setting laser energy density, magnetic field strength, and frequency as processing parameters. The optimization of laser cladding process parameters was investigated based on evaluating the quality of the laser cladding layer through hierarchical analysis and gray correlation analysis. Based on orthogonal test data, the correlation coefficients of the process parameters with the single objective function and the correlation degree of the multi-objective function were calculated by using the gray theory. Then the comprehensive objective optimization was carried out according to the gray correlation degree. The optimization problem with multiple process objectives was transformed into a single gray correlation degree optimization method to realize the optimization of process objectives and obtain the optimal combination of process parameters. The validation experiments indicate that the quality of the laser cladding layer can be greatly improved by employing optimal process parameters. The optimized laser cladding layer shows a reduced microstructure size and enhanced wear resistance, indicating the effectiveness of the optimization approach.

A promise of nose to brain delivery of bevacizumab intranasal sol-gel formulation substantiated in rat C6 glioma model.

Glioblastoma is one of the rapidly spreading cancers, with its potent malignancy often linked to pronounced angiogenesis within tumors. To mitigate this vascularization profile, bevacizumab (Avastin®), a monoclonal antibody, has been utilized for its antiangiogenic activity. However, its effectiveness is hindered by challenges in crossing the blood-brain barrier and the risk of off-target organ toxicity. Delivering drugs directly from the nose to the brain through the olfactory or trigeminal nerves bypassing the blood-brain barrier offers enhanced bioavailability and a more precise targeting strategy. To overcome these challenges, we aimed to develop bevacizumab in situ gel loaded mesoporous silica nanoparticles for intranasal delivery and further examine their pharmacokinetic and pharmacodynamic characteristics. The intranasal gel of mesoporous silica nanoparticles loaded with bevacizumab was optimized and formulated using a factorial and quality by design approach. In the case of bevacizumab mesoporous silica nanoparticles, lower particle size and most negative zeta potential were selected as quality target product profiles which is important for drug loading on the mesoporous silica nanoparticles and also transport of these nanoparticles across the nasal mucosa to the brain. A design space with a multidimensional combination of input variables and process parameters has been demonstrated to assure quality. To optimize the design space and achieve the desired quality standards, the base catalyst and surfactant concentration were chosen as the critical process parameters, while particle size and zeta potential were identified as the critical quality attributes. The novel formulation was assessed for physicochemical parameters such as particle size, zeta potential, entrapment efficiency, appearance, color, consistency, and pH. Additionally, studies on in vitro release, ex vivo permeation, stability, nasal toxicity, organ safety, and bioavailability were conducted. The efficacy study was conducted in an orthotopic murine glioblastoma rat model in which C6 Luc cells were instilled in the striatum of the rat's brain. In vivo, bioluminescence imaging of brain tumors was carried out to observe the tumor regression after treatment with the intranasal and intravenous bevacizumab formulation. Biochemical parameters and histopathology were performed for organ safety studies. The optimized intranasal formulation exhibited an average particle size of 318.8nm and a zeta potential of - 14.7mV for the mesoporous silica nanoparticles. The formulation also demonstrated an entrapment efficiency of 91.34% and a loading capacity of 45.67%. Further pharmacokinetic studies revealed that the optimized intranasal bevacizumab formulation achieved a significantly higher brain concentration Cmax = 147.9ng/ml, indicating improved bioavailability compared to rats administered with intravenous bevacizumab formulation (BEVATAS®), which had a Cmax of 127.2ng/ml. Moreover, this nanoparticle formulation entirely mitigated systemic exposure to bevacizumab. Organ safety evaluation of different biochemical parameters and histopathological analyses revealed that the intranasal bevacizumab-treated group was showing less off-target organ toxicity compared to the group treated with intravenous bevacizumab formulation. Subsequently, the efficacy of this nanosystem was evaluated in an orthotopic glioblastoma rat model, monitoring tumor growth over time through in vivo bioluminescence imaging and assessing anti-angiogenic effects. Twenty-one days post-induction, mesoporous silica nanoparticles loaded with bevacizumab in situ gel exhibited a marked reduction in average bioluminescence radiance (4.39 × 103) from day 7 (1.35 × 107) emphasizing an enhanced anti-angiogenic effect compared to the group treated with intravenous bevacizumab formulation which showed a gradual decrease in average bioluminescence radiance (4.82 × 104) from day 7 (1.42 × 107). These results suggest that the proposed novel formulation of mesoporous silica nanoparticles loaded bevacizumab in situ gel could serve as a promising avenue to enhance glioblastoma treatment efficacy, thereby potentially improving patient quality of life and survival rates significantly. Furthermore, the success of this delivery method could open new avenues for treating other neurological disorders, such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, and stroke. By providing effective brain-targeted therapies, this approach has the potential to revolutionize treatment options and improve outcomes for a broad spectrum of neurological conditions.

Optimization Study on Hardness of Cold Rolled Strip Steel Based on a Data-Driven Approach

As a key index to measure product quality and processability, the hardness of cold-rolled strip steel has an important influence on the production efficiency of iron and steel enterprises. However, due to the coupling between the process parameters at each stage of the continuous annealing process, and the setting of traditional process parameters depending on the experience of the operator, the lack of scientific and systematic optimization methods, which easily leads to the problem of high hardness fluctuation and low resource utilization. In this study, the data-driven method is used to conduct a preliminary correlation analysis to further clarify the optimal combination of process parameters for achieving the target hardness. The Pearson correlation coefficient is used to determine that carbon content, quenching furnace temperature, and strip speed significantly affect strip hardness. To further clarify the optimal combination of process parameters to achieve the target hardness, a Genetic Algorithm (GA) was tried in this study. The results show that the optimized process parameter combination can effectively improve the stability and consistency of strip hardness, and provide a scientific basis for the process parameter setting in the cold rolled strip production process, which is helpful for enterprises to improve product quality, reduce cost, and enhance market competitiveness.

Assessment of local mechanical properties of laser powder bed fused aluminium alloy by non-destructive testing based on FIMEC indentation

Laser powder bed fusion process is a versatile metal additive manufacturing process. Although significant progress has been made so far, there is still limited large-scale adoption of this technique by the industry. The main problems are repeatability and lack of proper knowledge. In this work, an innovative and non-destructive testing methodology, based on flat-top cylinder indentation, was used to define the mechanical properties of laser powder bed fused aluminium alloy to highlight any variations induced by the combination of process parameters, for global characterization, and by the building direction, for local characterization. Results show similar or improved global mechanical properties of the laser powder bed fused specimens when compared to traditional die-casted ones. Indentation tests highlight a local dependence of properties along the building direction in favor of the upper part of the samples.

Multi-Objective Optimization of the Forming Process Parameters of Disc Forgings Based on Grey Correlation Analysis and the Response Surface Method

This paper introduces a multi-objective optimization problem (MPO) for the forming process parameters of disc forgings using grey relational analysis (GRA) and the response surface methodology (RSM). Firstly, an experimental design based on the Box–Behnken design (BBD) principle was established, and simulations were performed in Deform to obtain response data. Secondly, GRA was used to transform the MPO into a grey relational degree (GRD) problem, and the entropic weight method was integrated to ascertain the influence weights of each variable on GRD. Then, a quadratic polynomial prediction model based on the RSM was constructed, and its accuracy was ensured through model validation. Finally, the optimal process parameter combination was determined through the particle swarm optimization algorithm, which included a friction coefficient of 0.3, an initial temperature of 1250 °C, and a downward pressing speed of 7.5 mm/s. The results of the experimental investigation indicate that optimized process parameters significantly reduce the forming load, equivalent stress, and damage value, effectively enhancing the overall quality of forged parts.

Control and optimization of defects in die casting of complicated right crankcase cover

The die-casting process of ADC12 aluminum alloy right crankcase cover was simulated based on AnyCasting software, and the influence of pouring temperature, initial mold temperature and injection speed on the filling and solidification process of ADC12 right crankcase cover casting was explored by orthogonal experiment method. The optimal process parameters were obtained through optimization, and the die-casting mold was designed. The program was verified in production. The results show that the porosity and shrinkage of the right crankcase cover are mainly concentrated in the thick wall part. The sequence of process parameters that affect the residual melt modulus is pouring temperature, initial mold temperature, and injection speed; The best combination of process parameters: pouring temperature is ∼680 °C, initial mold temperature is ∼200 °C, injection speed is ∼5 m/s. By analyzing the filling process and temperature field simulation results, the casting structure, mold structure and cooling system were optimized, and the process plan was improved. The production verification was carried out according to the optimized process plan. The casting quality is good and has no obvious defects, which meets the actual production needs.

Optimization of laser cladding powder ratio and process parameters based on MOGWO algorithm

Optimizing the process parameters is crucial to obtaining a high-quality fusion coating. The fitted regression model for each response was then analyzed by creating a central composite test with process parameters and power ratios (laser power, scanning speed, and Ni-clad graphite content) as inputs and the quality of the fused coating (dilution rate, microhardness, and width of the fused coating) as responses. The laser cladding process parameters and power ratios were optimized using the multi-objective Gray Wolf Optimization (MOGWO) algorithm. The best combinations of process parameters and power ratios were laser power of 1596 W, scanning speed of 21.7 mm/s, and Ni-clad graphite percentage of 18.4 %. Experimental validation demonstrates the superiority and accuracy of the MOGWO algorithm. The dilution rate of the optimized fusion-clad coating is increased by 47.4 % and the microhardness is increased by 17.8 %.

Experimental investigation on in-situ laser-assisted machining of single crystal silicon based on response surface methodology

Abstract In this paper, Response Surface Methodology (RSM) was utilized as a robust and convenient predictive tool to establish the correlation between process parameters in in situ laser-assisted machining and the surface roughness of single-crystal silicon. An optimized design of the diamond tool, a novel temperature field analysis method, and Response Surface Methodology (RSM) were utilized. The contribution rate of each process parameter on surface roughness was laser power > rotation speed > cutting depth > feed rate. The optimal process parameter combination is: rotation speed as 1001 r min−1, feed rate as 4.9 μm/r, cutting depth as 7.55 μm, and laser power as 28.81 W. Experimental validation of these optimal parameters compared surface roughness values obtained experimentally with those predicted. The surface roughness model showed a maximum relative error of 5.2%, with an average error of 4.8% across three experimental sets. These errors are within acceptable limits, indicating an alignment between predicted and experimental results.

Best practices for machine learning strategies aimed at process parameter development in powder bed fusion additive manufacturing

AbstractThe process parameters used for building a part utilizing the powder-bed fusion (PBF) additive manufacturing (AM) system have a direct influence on the quality—and therefore performance—of the final object. These parameters are commonly chosen based on experience or, in many cases, iteratively through experimentation. Discovering the optimal set of parameters via trial and error can be time-consuming and costly, as it often requires examining numerous permutations and combinations of parameters which commonly have complex interactions. However, machine learning (ML) methods can recommend suitable processing windows using models trained on data. They achieve this by efficiently identifying the optimal parameters through analyzing and recognizing patterns in data described by a multi-dimensional parameter space. We reviewed ML-based forward and inverse models that have been proposed to unlock the process–structure–property–performance relationships in both directions and assessed them in relation to data (quality, quantity, and diversity), ML method (mismatches and neglect of history), and model evaluation. To address the common shortcomings inherent in the published works, we propose strategies that embrace best practices. We point out the need for consistency in the reporting of details relevant to ML models and advocate for the development of relevant international standards. Significantly, our recommendations can be adopted for ML applications outside of AM where an optimum combination of process parameters (or other inputs) must be found with only a limited amount of training data.

Comparison of Hardness by Electrolytic and Electroless Deposition of Nickel on Aluminium

Abstract The work deals with the hardness characteristic of substrates coated with nickel through two primary methods: electrolytic deposition and electroless Ni-P deposition. The optimised process parameters for both the process was determined by varying a particular parameter keeping the rest constant in order to obtain minimum crystallite size of Ni. The size of the crystallite were determined using Scherrer’s equation. The optimized combination of process parameter for electrolytic deposition is: 270gm/lt of Nickel Sulfate, 60gm/lt Nickel Chloride, 35gm/lt Boric Acid, current density of 90mA/cm2 bath temperature of 55°C. The optimized combination of process parameter for electroless Ni-P deposition: 34gm/lt Nickel Sulfate, 23 gm/lt Sodium Hypophosphite, 46 gm/lt of Lactic acid, 42 gm/lt of Sodium Citrate, pH 5.0 and bath temperature 85°C. The optimised deposition rate 22μm/hr. Micro-hardness (Vickers) tests were conducted on the coated samples with a 50 gf load. The cause for difference in hardness was due to buildup of residual stress during deposition of Nickel on the substrate(aluminium). The orientation and surface texture of the materials are determined.

Research on size optimization of 3D printed parts using Grey-Taguchi method

Abstract Fused deposition modeling (FDM) is one of the earliest developed 3D printing processes, which has the advantages of a clean processing environment, low overall manufacturing costs, and a variety of optional materials. However, it still has the disadvantages of low dimensional accuracy and poor surface quality of the molded workpiece. Therefore, how to solve this annoying problem has become an important research topic in the current industry-university-research fields. In this study, a square container with a bottom area of 48 mm×48 mm, a height of 30 mm, and a thin wall of 6 mm was used as the design object, and four controllable factors were adopted: layer thickness (A), nozzle temperature (B), printing speed (C), and forming path (D), combined with Grey- Taguchi’s experiments and calculations are carried out in order to achieve the purpose of optimizing the inner volume (IV) and inner diagonal length (IDL) of the square container (dual-objective quality characteristics). The experimental results show that within the selected printing parameter range, the optimized process parameter combination is layer thickness of 0.25 mm (A3), nozzle temperature of 190 °C (B1), printing speed of 50 mm/s (C3) and the molding direction of the annular surface (D2). Finally, use the GOM Inspect software (ATOS Q 3D scanner) and vernier caliper (VC) to compare the physical errors. According to the verification results, the errors of IV and IDL of the square container obtained by optimizing process parameters are 0.49% and 1.53%, and 3.76% and 6.33% respectively in GOM Inspect software and vernier caliper measurement. The results of this optimization process not only prove that the GOM Inspect software has a considerable degree of accuracy, but also can be used as a means to improve FDM multi-quality printing process optimization when combined with the Grey-Taguchi method.

Evaluation of Fused Deposition Modelling process parameters influence on the porosity of printed parts

Fused Deposition Modelling (FDM) is an additive manufacturing technology that is rapidly gaining popularity due to its ability to produce complex shaped parts in a short time. However, parts produced by means of FDM have porosity that results from the printing process. The mechanical properties of printed parts depend on the FDM process parameters and porosity. This study investigates the effect of FDM process parameters on the porosity of FDM parts. The results of the study show that properly selected FDM process parameters can significantly minimize the percentage of part porosity. The effect of FDM process parameters on porosity was determined by analyzing nine process variables: layer height, extrusion temperature, printing speed, extrusion multiplier, bed temperature, infill pattern, deposited strand width, number of shells, number of solid top and bottom layers. The percentage of samples porosity was measured by means of the hydrostatic weighing method. Using the analysis of variance, statistically significant factors and their combinations affecting the percentage of parts porosity were determined. It was shown that the extrusion multiplier had the greatest influence on the percentage of parts porosity. A significant contribution to the formation of porosity was made by the infill pattern, layer heights, printing speed, number of shells, number of solid top and bottom layers and their interaction. A regression model has been developed that allows predicting the porosity of parts achieved by different combinations of FDM process parameters.

A Scan Strategy Based Compensation of Cumulative Heating Effects in Electron Beam Powder Bed Fusion

AbstractThe fabrication of complex geometries with uniform material properties in electron beam powder bed fusion (PBF-EB) remains a major challenge. Local material properties in PBF-EB are determined by the local thermal conditions and the spatio-temporal melt pool evolution. The local thermal conditions are governed by the cumulative heating effect on the hatch scale, which results from the superposition of temperature fields from adjacent hatch lines. The build-up of the cumulative heating effect at the beginning of a new hatch segment, without prior hatch lines, which results in regions with underdeveloped thermal conditions, is so far only rarely considered in the design of process strategies. This study introduces a numerical optimization scheme with the objective to minimize the extent of regions with underdeveloped thermal conditions at the beginning of line-based hatches, by means of scan strategy modifications. For this purpose, a simplified thermal solution is combined with an optimization approach to determine an optimal process strategy for line-based PBF-EB of a cuboid model geometry through the adaptation of individual hatch line spacing. Based on the approach determined for the model geometry, a generalized process strategy is derived for complex geometries and is numerically validated for different process parameter and geometry combinations.

Leveraging multi-output modelling for CIELAB using colour difference formula towards sustainable textile dyeing

Textile dyeing requires optimizing combinations of ingredients and process parameters to achieve target colour properties. Modelling the complex relationships between these factors and the resulting colour is challenging. In this case, a physics-informed approach for multi-output regression to model CIELAB colour values from dyeing ingredient and process inputs is proposed. Leveraging attention mechanisms and multi-task learning, the model outperforms baseline methods at predicting multiple colour outputs jointly. Specifically, the Transformer model’s attention mechanism captures the complex interactions between dyeing ingredients and process parameters, while the multi-task learning framework exploits the intrinsic correlations among the L*, a*, and b* dimensions of the CIELAB colour space. In addition, the incorporation of physical knowledge through a physics-informed loss function integrates the CMC colour difference formula. This loss function, along with the attention mechanisms, enables the model to learn the nuanced relationships between the dyeing process variables and the final colour output, thereby improving the overall prediction accuracy. This reduces trial-and-error costs and resource waste, contributing to environmental sustainability by minimizing water and energy consumption and chemical emissions.