Machining Cost Research Articles

Influence of singular and dual MQL nozzles on sustainable milling of Al6061-T651 in different machining environments

Al6061-T651 alloy is widely used in the aerospace industry due to its superior properties and is a very difficult-to-cut material with high quality because of tool wear and thermal softening. This situation causes low surface quality, high cutting force, and limited tool life, as well as an increase in total consumed energy, total carbon dioxide emission, and total machining cost. Therefore, it is necessary to sustainably increase this aerospace alloy's machining performance. In the presented study, for the first time, milling performance and sustainability assessment of Al6061-T651 alloy under dry, minimum quantity lubrication (MQL), nano molybdenum disulfide (MoS2) reinforced nanofluid-assisted MQL (N-MQL) cutting conditions were investigated using the single nozzle and dual nozzles, two different MQL flow rates, and three different cutting speeds. In this context, the results of surface roughness, cutting force, feed force, cutting tool flank wear, total consumed energy, total carbon emission, and total machining cost were obtained. As the cutting speed increased, the cutting force, feed force, total consumed energy, total machining cost, and total carbon emission decreased by 12.9 %, 18 %, 7.8 %, 6 %, and 0.1 %, while the surface roughness increased by 34.9 %. In the MQL and MoS2 N-MQL cutting conditions at the same flow rate, using dual nozzles instead of a single nozzle improved machining response and sustainability indicators. In addition, increasing the flow rate improved machining performance and sustainability indicators. Using the double nozzle MoS2 N-MQL method reduced the surface roughness, cutting force, feed force, flank wear, total consumed energy, total carbon emission, and overall machining cost by 74.2 %, 63.9 %, 68.4 %, 66 %, 27.9 %, 0.4 %, and 6.39 %, respectively.

Investigation of ultrasonic vibration assisted orthogonal turning under dry and minimum quantity lubrication conditions and performing sustainability analyses

Recently, increasing pressure on industries to innovate and adopt sustainable techniques has sparked investigations into machining strategies that have minimal carbon emissions, minimal energy consumption by machine tools, and low machining costs. The primary aim of this paper is to identify the machining conditions that can fulfill the criteria of clean production and sustainable chip forming for low-alloy steel which is extensively used in the many industrial field. To achieve this goal, experiments were done under both dry and Minimum Quantity Lubrication (MQL) conditions, aided by ultrasonic vibration assisted hybrid machining conditions at various cutting speeds (100 and 140 m/min) and uncut chip thicknesses (0.1, 0.14, and 0.18 mm). The study was conducted in two stages. In the first part, machining experiments were performed afterward, cutting forces, cutting temperature, chip morphology, surface roughness, and texture were examined. The second part of this study evaluates the sustainability of various machining parameters and cutting environments. The analysis of economic feasibility or rather the total machining costs were estimated by evaluating the costs incurred due to investment of machine tools, lubricant/coolant delivery systems and ultrasonic vibration system costs, waste processing and management costs, cutting tool and labor costs, cost of cutting fluid and energy consumed. On the other hand, environmental viability was evaluated by estimating the carbon emissions (CE) due to the power consumed, material utilization (i.e., the cutting tool and cutting fluid), and the waste processing and management (i.e., disposal of cutting tool, cutting fluid and chip recycling). Results indicate that both dry and MQL ultrasonic-assisted machining processes resulted in improved chip breakability, and produced short-comma chips. Additionally, it reduced cutting force by 28% and the cutting temperature was lowered by 30 %. Besides, Minimum Quantity Lubrication + Ultrasonic Vibration Assisted Machining (MQL + UVAM) hybrid method prevented surface defects and reduced average surface roughness by 24%–35% compared to dry conditions. While utilizing the MQL + UVAM method, the overall machining cost decreased in the range of about 20–30 % as compared to dry and dry + UVAM conditions. From an environmental standpoint, the MQL + UVAM hybrid machining process was found to be a significant contribution to the overall CE due to use of cutting fluids and increased machine tool utilization. This paper contributed to the understanding of sustainability and cleaner production to mitigate CO2 emissions by using the MQL + UVAM hybrid method.

Parametric Analysis of EN 353 Alloy Using MQL Machining with Response Surface Method

Heat generated in any conventional machining between tool and work- piece can be minimized by the use of cutting fluids since the latter plays a vital role in terms of improving surface quality and its characteristics because of its properties like continuous lubrication, cooling and chips flushing etc. As extensive use of cutting fluids increase the machining costs, the problem can be overcome by employing a sustainable manufacturing technique known as Minimum Quantity Lubrication (MQL) which reduces the flow of cutting fluid. On the other hand researches are focusing on Nano based fluids which are gaining more importance. As nano based cutting fluids are colloidal mixture of nano sized particles and a base fluid, which enhance the heat transfer characteristics suitable for machining applications. The present paper focuses on the application of Hybrid Nano based cutting fluids for machining of alloy steel under different MQL flow conditions. Using response surface methodology (RSM), an optimum solution is obtained for the selected input parameters to the output response-surface roughness. A regression relation is developed between the input and output responses with the help of Response surface methodology (RSM).

Paletli Kara Platformları için Sıvama Yöntemi ile Üretilen Alüminyum Portör Tekerlerinin Dayanımı Üzerine bir Çalışma

Conventional road wheels made by steel material for defense vehicles are generally easy and cheap in production, but it brings extra, undesirable weight to the platforms. It is found that the use of aluminum for road wheels provides weight gain on tracked land platforms. Generally, common manufacturing techniques for road wheels are casting and forging. Since load spectrum that each tracked vehicle road wheel experiences is considerably high and dynamic, casting may not be a good solution for manufacturing due to lower load absorbing capability of casting products. On the other hand, forging might be a suitable method for dynamically loaded road wheels but, it is also expensive as well due to mold and final machining cost. In this study, aluminum alloy material (2014-T6) produced by flow forming method as an alternative to steel is subjected to numerical and experimental stress and fatigue analysis for being used in road wheels of heavy tracked defense platforms. Flow forming method was utilized for effective, cheap and lighter production. Therefore, main focus was put to see the effect of flow forming method on fatigue behavior of aluminum made road wheels. It was concluded that flow forming production method of aluminum road wheel would be suitable method to provide required low cycle fatigue life with advantageous of cheapness and lightness.

A Large-Diameter Earth–Air Heat Exchanger (EAHX) Built for Standalone Office Room Cooling: Monitoring Results for Hot and Dry Summer Conditions

Earth–air heat exchangers (EAHX) use the soil thermal capacity to dampen the amplitude of outdoor air temperature oscillations. This effect can be used in hot and dry climates for room cooling, and depending on the EAHX design, this cooling can be achieved with very few resources other than those used during EAHX construction. This is an obvious advantage compared to the significant energy consumption and operational costs of refrigeration machines traditionally used in room cooling. Despite the large number of papers on EAHXs available in the scientific literature, very few deal with large-diameter EAHXs (with pipe diameters larger than 0.30 m), and even fewer present monitoring data gathered from a built and functional large-diameter EAHX. The present paper uses monitoring data and provides a detailed quantitative analysis of the performance of a large-diameter EAHX built for standalone cooling of an existing office building. The field monitoring was carried out during a characteristic hot and dry summer period of the south of Portugal. Results show that outdoor air to EAHX exit air temperature gradients reach 9 K and cooling capacities exceed 27 kW. Moreover, the studied EAHX is capable of standalone cooling for outdoor air temperatures up to 33 °C and meets more than 50% of the room design cooling demand for outdoor air temperatures as high as 37 °C. This evidences that large-diameter EAHXs have the potential to achieve significant reductions in CO2 emissions and in energy consumption associated with building cooling in hot and dry climates.

Heuristic Approach For Weighted Two Stage Flow Shop Model In A String Of Disjoint Job Block Under No Idle Constraint

The current paper investigates a two-stage flow shop scheduling model with no idle restriction, in which jobs are scheduled to run in a string of two job blocks that are disjoint in nature, and the jobs processing time is correlated with probabilities. Owing to inherent usefulness as well as relevance in real-world situations, jobs' weight has additionally included. In order to eliminate machine idle time and cutting machine cost of rental, the reason for the conduct of the study is to provide a heuristic algorithm which, once put into practice, processes jobs in optimal way, guarantees in smallest conceivable make span. Multiple computational examples generated in MATLAB 2019a serve as testament to efficacy of the proposed strategy operates. The outcomes are contrasted with the current methods that Johnson and Palmer have demonstrated.

Expert system based fault diagnosis for railway point machines

To meet the increasing demands for availability at reasonable cost, operators and maintainers of railway point machines are constantly looking for innovative techniques for switch condition monitoring and prediction. This includes automated fault root cause diagnosis based on measurement data (such as motor current curves) and other information. However, large, comprehensive sets of labeled data suitable for standard machine learning are not yet available. Existing data-driven approaches focus only on the differentiation of a few major fault categories at the level of the measurement data (i.e., the “fault symptoms”). There is great potential in hybrid models that use expert knowledge in combination with multiple sources of information to automatically identify failure causes at a much more detailed level. This paper discusses a Bayesian network diagnostic model for determining the root causes of faults in point machines, based on expert knowledge and few labeled data examples from the Netherlands. Human-interpretable current curve features and other information sources (e.g., past maintenance actions) are used as evidence. The result of the model is a ranking of the most likely failure causes with associated probabilities in terms of fuzzy multi-label classification, which is directly aimed at providing decision support to maintenance engineers. The validity and limitations of the model are demonstrated by a scenario-based evaluation and a brief analysis using information theoretic measures. We present the information sources used, the detailed development process and the analysis methodology. This article is intended to be a guide to developing similar models for various complex technical assets.

Potential Use of Rice Straw with Sustainable Technologies: Knowledge and Constraints

The present study was carried out in eight districts of Haryana state. The outcomes of the study were explained after completing a field survey in which a total of 480 farmers (60 from each district) were interviewed using a well-structured interview schedule. The results were visualized that in comparison to manufacturing industries and renewable energy production, respondents in the study had a high level of awareness about the possible use of straw in agriculture especially in live-stock feeding and in making of compost and vermi-compost. They were also well aware about the use of straw in paper making and in bio-fuel production. The major constraints reported were ‘straw of paddy delay in wheat sowing’, ‘non-availability of suitable technology’ and ‘high cost of residue management machines’. Management constraints were ranked first among the various types of constraints, followed by financial and then technical constraints. Farmers were also aware about benefits of straw management i.e. straw management helps in controlling carbon emission and improving soil health.

Sustainability assessment and optimization for milling of compacted graphite iron using hybrid nanofluid assisted minimum quantity lubrication method

The climate change packages have strict regulations to reduce fuel consumption, costs, and carbon emissions in vehicles. Automobile manufacturers tend to utilize compacted graphite iron (CGI) in vehicle engine parts due to these regulations and rising engine performance needs. However, the difficulty of CGI machining and the sustainability problem during machining are barriers to its broad application in the automobile sector. In this study, nano molybdenum disulfide (nMoS2) reinforced nanofluid-assisted MQL (nMoS2-NMQL), multi-walled carbon nanotube (MWCNT) reinforced nanofluid-assisted MQL (MWCNT-NMQL) and nMoS2/MWCNT reinforced hybrid nanofluid-assisted MQL (HNMQL) cutting conditions were used for the first time in the milling of CGI material and machining performance was investigated in terms of cutting forces, cutting temperature and surface roughness. In addition, for the first time in the milling of CGI material, sustainability assessment and optimization in terms of carbon emissions and total machining cost have been carried out using non-dominant sequencing genetic algorithm II (NSGA-II) and technique for order of preference by similarity to ideal solution (TOPSIS). Using the MWCNT-NMQL cutting condition reduced the cutting temperature, resultant force, surface roughness, carbon emissions amount, and total machining cost by 38.2%, 54.5%, 57.6%, 7.6%, and 24%, respectively. The sustainability optimization conclusion was that MWCNT-NMQL cutting condition, cutting speed values between 90 and 93 m/min, and feed values between 0.11 and 0.117 mm/rev should be used in order to obtain the values closest to the optimum result for an improved and sustainable CGI milling process.

Design of sprinklers from a resource-saving perspective

Sprinklers created in the Soviet period and still widely used in the fields today, in many respects no longer meet modern requirements, do not guarantee high-quality watering and reliable operation. It is necessary to develop new design principles, concepts, new techniques and approaches, to clarify the required parameters and characteristics. It is important not only to modernize and restore the irrigation equipment in operation, but also to produce new reliable, high-quality machines. One of the current trends in the design of sprinklers is the principle of resource conservation. The article defines the directions of improvement of structural and technical parameters, presents the design concept depending on the operating conditions. The ways of reducing the metal consumption, and consequently the cost of machines, are considered. Obvious ways to reduce the metal consumption of machines include increasing the span between the trolleys and reducing the weight of the truss water belt by improving the design. Optimization of the truss water belt allowed to increase the span length by 22.1-33.8% with a slight increase in weight by 5.8-15.1%.

Expert opinion based optimziation of CNC machining parameters for Al-CNT composites using fuzzy TOPSIS method

Machinability of Al-CNT composites at optimum conditions of machining parameters saves a lot of time, improves quality of machined components at minimum possible cost of machining. In conventional methods, controlled experimentation is performed. In order to obtain best machining parameters, optimization methods are applied but they are inadequate in many situations. Hence, an attempt was made in this work to collect subjective data for response parameters such as, quality, cost, and time, from selected experts in this field for various combinations of input process parameters of CNC milling machine for the case of Al-CNT composites with high-speed steel as tool material. 27 sets of experiments were devised for various combinations of machining parameters systematically by taking cutting speed (in rpm), feed, step over ratio, and depth of cut each at 3 levels. Two experts were invited for collection of data for the response parameters and three experts were involved for giving weightages for the response parameters. Fuzzy trapezoidal membership functions were made use of, for representing fuzzy set and fuzzy Technique and TOPSIS method was applied for finding the best solution. Results revealed that best solution was obtained for a spindle speed of 3000 rpm, feed of 150 mm rev−1, step-over ratio of 0.5, and depth of cut of 0.2 mm.

Environmental and Economic Sustainability Impacts of Metal Additive Manufacturing: A Study in the Industrial Machinery and Aeronautical Sectors

Metal additive manufacturing has emerged as a promising alternative to conventional manufacturing in various industries, offering advantages like complex geometries and reduced material waste. However, there is a research gap in the form of comparative and industry-specific investigations and case studies to comprehensively assess the environmental and economic impact of metal additive manufacturing.This study focuses on two significant sectors, industrial machinery and aeronautics, to understand how metal additive manufacturing influences environmental and economic sustainability compared to conventional manufacturing. Therefore, taking a life cycle approach, the environmental and economic impacts of this technology are evaluated and compared to conventional manufacturing through representative case studies in the two sectors using life cycle assessment and life cycle cost methodologies.The economic evaluation reveals that metal additive manufacturing reduces the total cost for aeronautical components by 33.2 %, even considering several post-processing operations. While for industrial machinery parts, this cost increases by 79.3 % due to high machine and material cost. However, this technology reduces the potential environmental impact by more than 60 % for both sectors, mainly due to reduced material consumption. The use phase for aeronautical components further contributes to environmental and economic benefits, with significant fuel and CO2 emissions savings. The results emphasize the sector-dependent nature of metal additive manufacturing's environmental and economic impact and underscore the importance of a life cycle perspective for informed decision-making.Some critical points for sustainable adoption of metal additive manufacturing in each sector are equipment investment, the post-processing applied, the material selected or, for the aeronautical industry, the impact of the use phase. Nevertheless, future developments are needed to address powder reuse, optimization, certification, and the social impact of metal additive manufacturing.

Hybrid adaptive control of CNC drilling for enhancement of tool life and surface quality

Intelligent machining requires the online adaptation of the machining parameters to improve tool life and product quality and to reduce machining costs. This article presents a novel hybrid adaptive control (HAC) system for a drilling process. The HAC system is a combination of two adaptive controls: geometric adaptive control (GAC) and adaptive control by optimisation (ACO). It keeps the roughness of the holes within tolerance without compromising tool life. A response surface model (RSM) is used for modelling the drill wear and surface roughness with speed, feed, acceleration and force signals as inputs. The model predicts the wear and roughness with prediction accuracies of 97.1% and 93.6%, respectively. The roughness control is achieved through a Massachusetts Institute of Technology rule and tool wear is minimised by genetic algorithm optimisation. The adaptive algorithms are simulated and validated for the machining conditions given by the adaptive algorithms. The results show an improved tool life of 7% and surface roughness of 11%.

Constraints Perceived by the Farmers Regarding Opportunity and Challenges of Climate Smart Agriculture in Central Plain Zone of Uttar Pradesh, India

This research article discussed about the constraints faced by farmers in the adoption of climate smart agriculture in the Central Plain Zone of Uttar Pradesh. Climate Smart Agriculture (CSA) is basically a management strategy for agriculture in the face of climate change. Farmers face obstacles to adopting diverse Climate Smart Agriculture practices. Farmers face many barriers when it comes to adopting new or unfamiliar practices, such as Climate Smart Agriculture. These include lack of awareness, high cost of machines and input, and insufficient knowledge and guidance. Garrett's ranking technique is used to determine the most important factor influencing the response. The major constraints faced by the farmers in their knowledge and adoption of Climate Smart Agriculture were ‘lack of awareness', 'uncertain returns', 'limited extension activities’ common problems in the study area having the Garrett rank of first (Average Value 114.95), second (Average Value 113.26) and third (Average Value 101.22) respectively, and so forth the other constraints too. The study sought to identify the barriers to Climate Smart Agriculture adoption as well as potential solutions.

Process Quality Control Method for Three-Cylinder Engine Balance Shaft System Oriented to Manufacturing Supply Chain

Automobile manufacturers often outsource key components and parts to suppliers, hence ensuring that process quality has been a hot topic in current research. The quality control demands at different stages of the manufacturing supply chain cycle have been ignored in traditional process quality control methods. To solve this issue, this paper takes the three-cylinder engine balance shaft system as the research object, and builds a process quality control model for the three-cylinder engine balance shaft system oriented to the manufacturing supply chain. The metagraph theory is introduced, and parts suppliers in the manufacturing supply chain, product process quality attributes, supply chain revenue function, and other key elements are defined. By constructing the revenue function of the three-cylinder engine balance shaft system process quality control model, the feasibility of the proposed method is verified by taking the process quality control of one certain three-cylinder engine balance shaft system as a verification example. The results show that, with other conditions unchanged, the application of this method can increase the overall profit of the manufacturing supply chain by 100% at the stage of preparation before product machining, and more than 8% at the stage of product performance evaluation. The proposed method of process quality control based on the metagraph theory provides a theoretical basis for building an information sharing platform of product process quality, and effectively reduces the machining cost for product improvement and upgrading in the manufacturing supply chain.

Opportunistic maintenance strategy optimization considering imperfect maintenance under hybrid unit-level maintenance strategy

Opportunistic maintenance is a widely adopted strategy for maintaining multi-unit systems, which has garnered significant attention from researchers. However, the current opportunistic maintenance still suffers from certain limitations, such as single preventive maintenance strategy at unit-level and lack of flexibility in optimization model. In this paper, an optimization model for scheduled maintenance (SM) and condition-based maintenance (CBM) at the unit-level is presented, and the associated maintenance costs are analyzed. Subsequently, a preventive opportunistic maintenance strategy optimization model under hybrid unit-level maintenance strategies is established by considering imperfect preventive maintenance. An optimization algorithm based on Monte Carlo simulation is designed to solve the model. Finally, the opportunistic maintenance strategy of CNC gear grinding machine is developed. Comparing the opportunistic maintenance strategy given in this paper with other maintenance strategies, the results show that the maintenance strategy presented in this paper can significantly reduce the maintenance cost of the CNC grinding machine. The opportunistic maintenance strategy optimization method proposed in this paper can provide guidance for relevant enterprises to formulate maintenance strategies.

Multi-objective optimization of surface morphology using fractal and multi-fractal analysis for dry milling of AISI 4340

Machining of difficult-to-cut metals is typically associated with inconsistent machining quality, high machining costs, and significant environmental pollution. This work proposes a novel method that utilizes fractal and multi-fractal features of the machined surface morphology to evaluate surface quality in high-speed dry milling of 4340 steel. The results demonstrate that the fractal dimension can be used as an evaluation index for machined surface defects. It accurately characterizes the contour information of machined surface defects, including voids, tears, and material side flow. A smaller fractal dimension indicates fewer surface defects and represents a higher-quality machined surface. However, the surface defects cannot be described using the traditional evaluation method based solely on surface roughness (Ra). Moreover, multi-objective optimization of high-speed milling of 4340 steel is achieved by using the combined indices Ra and fractal features as responses. The Ra generated using the optimized parameters can reach 97 nm, which represents a reduction of 4.9 % compared to the non-optimization state. And typical defects on machined surfaces virtually disappear. Meanwhile, the machining cost has been reduced by 2.52 % and the machining efficiency has increased by 31.45 %. The results demonstrate that the proposed surface quality evaluation method is possible for practical engineering applications to achieve high-quality, high-efficiency and low-cost high-speed dry milling of high-strength steels.

Investigation of Heat Annealing and Parametric Optimization for Drilling of Monel-400 Alloy

A nickel-based copper alloy known as Monel-400 is extensively applied in many industries including aerospace, marine engineering, and nuclear power generation, owing to its exceptional characteristics such as extreme tensile strength and toughness, excellent corrosion resistance, and the ability to retain shape even at extremely high temperatures. Traditional methods of drilling Monel-400 alloy are difficult due to quick tool wear and poor surface polishing, resulting in expensive machining costs. In this study, a technique called heat annealing was implemented to externally heat-treat the Monel-400 alloy material before the drilling process. Cutting force, surface roughness, and tool wear were used as the responses to investigate the effect of heat annealing and the drilling parameters on the machinability of Monel-400. The results revealed that the cutting force (Fz) and surface roughness (Ra and Rt) could be reduced by 33%, 31%, and 25%, respectively, after annealing at 700 °C compared to the results of the drilled Monel-400 at room temperature. It can be observed that the maximum improvement can reach 42% of Fz, 35% of Ra, and 59% of Rt while annealing Monel-400 at 1000 °C. A significant reduction was observed in the tool wear for machining the annealed material, which minimized the tooling and overall machining cost. Regarding the effects of the drilling process on the considered responses, the results revealed that the spindle speed has a greater effect on the cutting force, whereas the feed rate has the most significant effect on Ra. The significance of the drilling input parameters on the outputs is determined by analysis of the main effect plots and surface plots. Subsequently, the multi-objective genetic algorithm (MOGA) is used to identify the optimal parametric conditions for minimizing the cutting force and surface roughness of the drilled holes. The optimized values achieved via multi-objective optimization are the cutting force, Fz = 388–466 N, and the surface roughness, Ra = 0.17–0.19 μm and Rt = 3–3.5 μm, respectively.

Comparative performance investigation and sustainability assessment in electrical discharge machining of SS316 using different dielectrics

In electrical discharge machining, the material removal phenomenon is heavily influenced by the breakdown strength of the dielectric fluid and the inter-electrode gap monitoring. This innovative research addresses a comparative performance evaluation and sustainability assessment between biodegradable palm oil and conventional EDM oil during machining of stainless steel (SS-316) using copper electrode. The cutting performance is investigated by comprehensive result analysis of various machinability aspects including material removal rate, surface integrity, tool wear rate, radial overcut of machined components. According to the findings, machining with palm oil dielectric outperformed the usual EDM oil with regards to improved material removal, surface morphology and finish, increased machined surface hardness with minimum surface crack density and hole overcut. Pyrolysis of conventional EDM dielectric oil leads to deposition of thin film carbon on bottom surface of the copper electrode which prevents the quick tool wear due to its higher breakdown voltage. The total machining cost expenditure per component under palm oil (INR383) is reduced by 11% than expenditure in traditional EDM oil (INR426). Compared to EDM oil, machining under biodegradable palm oil as dielectric fluid is better socio-economical viable and environmental conscious to enhance sustainability.

Prediction of QCE using ANN and ANFIS for milling Alloy 2017A

Population growth and economic development, particularly in developing market nations, are driving up global energy consumption at an alarming rate. Despite increased wealth, growing demand presents new obstacles. Computer Numerical Control (CNC) machine tools are widely used in most metal machining processes due to their efficiency and repeatability in achieving high-precision machining. It has been shown that figuring out the best cutting parameters can improve the results of machining, leading to high efficiency and low costs. This study identifies and examines thoroughly the scientific contributions of the influence of strategies, machining sequences, and cutting parameters on surface quality, machining cost, and energy consumption (QCE) using artificial intelligence (ANN and ANFIS). The results show that the 3.10−3 architecture with the Bayesian Regularization (BR) algorithm is the optimal neural architecture that yields an overall mean square error (MSE) of 2.74 10−3. The correlation coefficients ( R2) for Etot, Ctot, and Ra are 0.9992, 1, and 0.9117 respectively. Similarly, for the adaptive neuro-fuzzy inference system (ANFIS), the optimal structure which gives a better error and better correlation is the {2, 2, 2} structure, and this for the three output variables (Etot, Ctot, and Ra). The correlation coefficient ( R2) for the variables Etot, Ctot, and Ra are respectively 0.95, 0.965, and 0.968. The results show that the use of the Bayesian Regularization algorithm with a multi-criteria output response can give good results when compared with the adaptive neuro-fuzzy inference system.