Search

When optimizing a mechanical device, the symmetry principle provides important guidance. Minimum gearbox mass and maximum gearbox efficiency are two single objectives that need to be achieved when designing a gearbox, and they are not compatible. In order to address the multi-objective optimization (MOO) problem with the above single targets involved in building a two-stage helical gearbox with second-stage double gear sets, this work presents a novel application of the multi-criteria decision-making (MCDM) method. This study’s objective is to identify the best primary design elements that will increase the gearbox efficiency while lowering the gearbox mass. To carry this out, three main design parameters were selected: the first stage’s gear ratio and the first and second stages’ coefficients of wheel face width (CWFW). Furthermore, a study focusing on two distinct goals was carried out: the lowest possible gearbox mass and the highest possible gearbox efficiency. Furthermore, the two stages of the MOO problem are phase 1 and phase 2, respectively. Phase 2 solves the single-objective optimization issue to minimize the difference between variable levels and the MOO problem to determine the optimal primary design factors. To solve the MOO problem, the EAMR (Evaluation by an Area-based Method of Ranking) method was also chosen. The following are important features of this study: First, a MCDM method (EAMR technique) was successfully applied to solve a MOO problem for the first time. Secondly, this work explored the power losses during idle motion to calculate the efficiency of a two-stage helical gearbox with second-stage double gear sets. This study’s findings were used to identify the optimal values for three important design variables to design a two-stage helical gearbox with second-stage double gear sets.

This paper provides a novel application of the multi-criteria decision-making (MCDM) method to the multi-objective optimization problem (MOOP) of creating a two-stage helical gearbox (TSHG) with second-stage double gear sets (SDGSs). The aim of the study is to determine the optimum major design components for enhancing the gearbox efficiency while reducing the gearbox volume. In this work, three primary design parameters are chosen to accomplish this: the gear ratio of the first stage and the coefficients of the wheel face width (CWFW) of the first and second stages. Additionally, the study is conducted with two distinct objectives in mind: the lowest gearbox volume and the maximum gearbox efficiency. Moreover, phase 1 and phase 2, respectively, are the two stages of the MOOP. Phase 2 handles the MOOP to identify the ideal primary design factors as well as the single-objective optimization problem to minimize the difference between the variable levels. Additionally, the Multi-Attributive Ideal–Real Comparative Analysis (MAIRCA) approach is selected to deal with the MOOP. The results of the study are utilized to determine the ideal values for three crucial design parameters in order to create a TSHG with SDGSs.

In order to address the Multi-Objective Optimization Problem (MOOP) in building a two-stage helical gearbox, this work presents a novel application of the Multi-Criterion Decision-Making (MCDM) method. The aim of the study is to determine the optimal primary design factors that will increase gearbox efficiency while decreasing gearbox volume. Three main design parameters were chosen for assessment in this work: the first stage’s gear ratio, and the first and second stages’ Coefficients of Wheel Face Width (CWFW). In addition, the MOOP is divided into two phases: phase 1 solves the single-objective optimization problem to reduce the gap between variable levels, and phase 2 solves the MOOP to determine the optimal primary design factors. Furthermore, the Entropy approach was picked to compute the weight criteria, and the MARCOS method was chosen as an MCDM method to handle the multi-objective optimization issue. The following are important characteristics of the study: Firstly, the MCDM method (MARCOS technique) was successfully applied to solve a MOOP for the first time. Secondly, this work has looked into power losses during idle motion to calculate the efficiency of a two-stage helical gearbox. The results of the study were used in the design of a two-stage helical gearbox in order to identify the optimal values for three important design parameters.

This paper provides a novel application of a multi-criterion decision-making (MCDM) method to the multi-objective optimization problem of designing a two-stage helical gearbox. This study’s goal is to identify the ideal primary design elements that increase gearbox efficiency while reducing the gearbox cross-section area. In this work, three primary design parameters were selected for investigation: the gear ratio of the first stage and the coefficients of wheel face width (CWFW) of the first and second stages. The multi-objective optimization problem was further split into two phases: phase 1 solved the single-objective optimization problem of minimizing the gap between the variable levels, and phase 2 solved the multi-objective optimization issue of identifying the ideal key design factors. Moreover, the multi-objective optimization problem was handled by the SAW method as an MCDM approach, and the weight criteria were computed using the entropy approach. This study’s significant characteristics are as follows: First, a multi-objective optimization problem was successfully solved using the MCDM approach (SAW technique) for the first time. Second, the power losses in idle motion were investigated in this work in order to determine the efficiency of a two-stage helical gearbox. From this study’s findings, the ideal values for three major design parameters can be determined for the design of a two-stage helical gearbox.

This paper introduces a novel approach to deal with the multi-objective optimization of a two-stage bevel helical gearbox by applying the Taguchi method and Grey Relation Analysis (GRA). The goal of the study is to find optimal main design factors that minimize the gearbox volume and maximize the gearbox efficiency. To accomplish this, five main design parameters were selected: the coefficients of wheel face width (CWFW) of the bevel and the helical gear sets, the allowable contact stresses (ACS) of the first and the second stages, and the gear ratio of the first stage. Furthermore, two single targets were investigated: minimum gearbox volumes, and maximum gearbox efficiency. Also, the multi-objective optimization problem is solved through two steps: Step 1 for closing the gap between variable levels and Step 2 for determining the optimal main design factors. The study’s findings were used to introduce the optimum values of five major design parameters for designing a two-stage helical gearbox.

This paper presents a novel approach to solve the multi-objective optimization problem designing a two-stage helical gearbox by applying the Taguchi method and the grey relation analysis (GRA). The objective of the study is to identify the optimal main design factors that maximize the gearbox efficiency and minimize the gearbox mass. To achieve that, five main design factors. including the coefficients of wheel face width (CWFW) of the first and the second stages, the allowable contact stresses (ACS) of the first and the second stages, and the gear ratio of the first stage were chosen. Additionally, two single objectives, including the maximum gearbox efficiency and minimum gearbox mass, were analyzed. In addition, the multi-objective optimization problem is solved through two phases: Phase 1 solves the single-objective optimization problem in order to close the gap between variable levels, and phase 2 solves the multi-objective optimization problem to determine the optimal main design factors. From the results of the study, optimum values of five main design parameters for designing a two-stage helical gearbox were first introduced.

The MCDM problem is very important and often encountered in life and in engineering as it is used to determine the best solution among various possible alternatives. In this paper, the results of the MCDM problem in the dressing process for internal grinding are presented. To perform this work, an experiment with six input parameters, including the depth and the time of fine dressing, the depth and the time of coarse dressing, non-feeding dressing, and dressing feed rate, was conducted. The experiment was designed according to the Taguchi method with the use of L16 orthogonal arrays. In addition, TOPSIS, MARCOS, EAMR and MAIRCA methods were selected for the MCDM to obtain the minimum SR and the maximum MRR simultaneously. In addition, the weight determination for criteria was implemented by MEREC and entropy methods. From the results, the best solution to the multi-criteria problem for the dressing process in internal grinding has been proposed.

Multi-criteria decision making (MCDM) is used to determine the best alternative among various options. It is of great importance as it hugely affects the efficiency of activities in life, management, business, and engineering. This paper presents the results of a multi-criteria decision-making study when using powder-mixed electrical discharge machining (PMEDM) of cylindrically shaped parts in 90CrSi tool steel. In this study, powder concentration, pulse duration, pulse off time, pulse current, and host voltage were selected as the input process parameters. Moreover, the Taguchi method was used for the experimental design. To simultaneously ensure minimum surface roughness (RS) and maximum material-removal speed (MRS) and to implement multi-criteria decision making, MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), and MAIRCA (Multi-Attributive Ideal–Real Comparative Analysis) methods were applied. Additionally, the weight calculation for the criteria was calculated using the MEREC (Method based on the Removal Effects of Criteria) method. From the results, the best alternative for the multi-criteria problem with PMEDM cylindrically shaped parts was proposed.

In the current study, an optimization process of powder-mixed electrical discharge machining (PMEDM) process when machining cylindrically shaped parts made of hardened 90CrSi steel is reported. In this study, SiC powder was mixed into the Diel MS 7000 dielectric solution. Additionally, graphite was chosen as the electrode material. The multi-objective functions were minimizing the surface roughness (SR) and electrode wear rate (EWR) and maximizing the material removal rate (MRR). The used input parameters of the optimization process included the powder concentration, the pulse-on time, the pulse-off time, the pulse current, and the servo voltage. A combination between the Taguchi method and the grey relation analysis (GRA) method with the support of Minitab R19 software was used to design the experiment and analyze the results. It was found that the optimal set of process parameters that can satisfy the above responses are Cp of 0.5 g/L, Ton of 8 µs, Toff of 8 µs, IP of 5 A, and SV of 4 V.

As a successful solution applied to electrical discharge machining (EDM), powder-mixed electrical discharge machining (PMEDM) has been proposed as an upgrade of the EDM process. The optimization of the process parameters of PMEDM is essential and pressing. In this study, Taguchi methods and analysis of variance (ANOVA) were used to find the main parameters affecting surface roughness in the EDM process with SiC powder-mixed-dielectric of hardened 90CrSi steel. The PMEDM parameters selected were the powder concentration, the pulse-on-time, the pulse-off-time, the pulse current, and the server voltage. It was found that SiC powder exhibits positive effects on reducing surface roughness. The roughness obtained with the optimum powder concentration of 4 g/L was reduced by 30.02% compared to that when processed by conventional EDM. Furthermore, the pulse-off-time was found to be the most influential factor that gave an important effect on surface roughness followed by the powder concentration. The EDM condition including a powder concentration of 4 g/L, a pulse-on-time of 6 µs, a pulse-off-time of 21 µs, a pulse current of 8 A, and a server voltage of 4 V resulted in the best surface roughness.

The blasting technique has been broadly utilized to prepare new surfaces for painting, engraving, etc., in industry. In fact, the minimization of the cleaning cost system is essential. However, the knowledge of this technique in terms of optimization has been poor so far. The aim of this study is to find the optimum replaced boron carbide nozzle diameter for the abrasive blasting system. The basis of the study is to find the replaced nozzle diameter to minimize the cost of the blasting system. Seven main parameters are adopted for examining their influences on the response and the optimum replaced nozzle diameter. The design of experiment (DOE) technique is utilized by using Minitab@19. The results reveal that the initial nozzle diameter has the strongest impact on the optimum replaced nozzle diameter. Furthermore, the proposed regression model has been found to be entirely insistent to the experiments. The utilization of this model can provide an effective way to simplify the calculation of the optimum initial nozzle diameter for boron carbide.

This study is aimed at determining optimum partial gear ratios to minimize the cost of a three-stage helical gearbox. In this work, eleven input parameters were investigated to find their influence on the optimum gear ratios of the second and the third stages ( u 2 and u 3 ). To reach the goal, a simulation experiment was designed and implemented by a cost optimization program. The results revealed that in addition to the input parameters, their interactions also have important effects in which the total ratio gearbox ratio ( u t ) and the cost of shaft ( C s ) have the most impact on u 2 and u 3 responses, respectively. Moreover, the proposed models of the two responses are highly consistent to the experimental results. The proposed regression equations can be applied to solve optimization cost problems.

Based on a cost analysis, a method of identifying and predicting optimum replaced grinding wheel diameter (De.op) in a surface grinding operation for 9CrSi steel material was developed in this study. The De.op value was determined by minimizing the cost function. An experimental design was set up, and a computational program was developed to perform the experiment in order to calculate the De.op value. Furthermore, the impact of the grinding process parameters such as the initial grinding wheel diameter, the grinding wheel width, the total dressing depth, the Rockwell hardness of the workpiece, the radial grinding wheel wear per dress, and the wheel life on the De.op value were investigated. Moreover, the impacts of the cost components such as the machine tool hourly rate and the grinding wheel cost on the De.op value were given. Based on that, a mathematical model was proposed to determine the De.op value. The predicted De.op value was also verified by an experiment. The obtained result shows that the difference between the experimental De.op value and the predicted De.op value is within 1.7%, indicating that the mathematical model proposed in the study is reliable.

This paper shows an optimization study on calculating the optimum replaced wheel diameter in internal grinding of stainless steel. In this work, the effects of the input factors, including the initial diameter, the grinding wheel width, the ratio between the length and the diameter of the work-pieces, the dressing depth of cut, the wheel life and the radial grinding wheel wear per dress on the optimum replaced grinding wheel diameter were considered. Also, the effects of cost components, including the cost of the grinding machine and the wheel cost were examined. Moreover, to estimate the influences of these parameters on the optimum replaced diameter, a simulation experiment was given and conducted by programming. From the results of the study, a regression equation was proposed to calculate the optimum replaced diameter.

Cutting regime parameters play an important role in determining the efficiency of the grinding process and the quality of the ground parts. In this study, the influences of the cutting parameters, including the cutting depth (ae), the feed rate (Fe) and the wheel speed (RPM) on the grinding time when grinding tablet shape punches by a cubic boron nitride (CBN) wheel on a CNC (Computerized Numerical Control) milling machine are investigated. The Taguchi technique based on orthogonal array and analysis of variance (ANOVA) was then applied to design the number of experiments and evaluate the influence of cutting depth, feed rate and wheel speed on the grinding time. The results show that among the three cutting parameters, the most influential parameter on the grinding time is the cutting depth. The second influential parameter on the grinding time is the feed rate. The least influential parameter on grinding time is the wheel speed. In addition, the optimal condition of cutting parameters obtained for grinding tablet shape punches by cubic boron nitride wheels on a CNC milling machine are a cutting depth of 0.03 mm, wheel speed of 5000 rpm and feed rate of 3500 mm/min. This optimum cutting parameters ensure the least grinding time.

This paper presents a new approach to developing the torque model in deep hole drilling, both for conventional and ultrasonic assisted drilling processes. The model was proposed as a sum of three components: the cutting, the chip evacuation and the stick-slip torques. Parameters of the new model were carried out by applying the regression analysis technique, with the correlation values higher than 0.999. The data were collected from 36 experimental dry drilling tests, both in conventional and ultrasonic assisted cutting conditions, with the depth-to-diameter of the drilled holes of 7.5. The major advantage of the new model compared to previous models is that the new model of chip-evacuation torque has only one coefficient, thus making it easier to evaluate and compare different deep-drilling processes. The effectiveness of ultrasonic assistance in deep hole drilling was also highlighted using the proposed model. The new model is promising to predict critical depth and torque in deep hole drilling.

In order to design a high-efficiency two-stage gearbox to reduce power loss and conserve energy, a Multi-Criterion Decision-Making (MCDM) method is selected for solving the Multi-Objective Optimization Problem (MOOP) in this research. The study's objective is to determine the best primary design factors that will increase gearbox efficiency and decrease gearbox mass. To that end, the first stage's gear ratio and the first and second stages' Coefficients of Wheel Face Width (CWFW) were chosen as the three main design elements. Furthermore, two distinct goals were analyzed: the lowest gearbox mass and the highest gearbox efficiency. Additionally, the MOOP is carried out in two steps: phase 1 solves the Single-Objective Optimization Problem (SOOP) to close the gap between variable levels, and phase 2 solves the MOOP to determine the optimal primary design factors. Furthermore, the TOPSIS approach was selected to address the MOOP. For the first time, an MCDM technique is used to solve the MOOP of a two-stage helical gearbox considering the power losses during idle motion. When designing the gearbox, the optimal values for three crucial design parameters were ascertained according to the study's results.

In order to design a high-efficiency two-stage gearbox to reduce power loss and conserve energy, a MultiCriterion Decision-Making (MCDM) method is selected for solving the Multi-Objective Optimization Problem (MOOP) in this research. The study's objective is to determine the best primary design factors that will increase gearbox efficiency and decrease gearbox mass. To that end, the first stage's gear ratio and the first and second stages' Coefficients of Wheel Face Width (CWFW) were chosen as the three main design elements. Furthermore, two distinct goals were analyzed: the lowest gearbox mass and the highest gearbox efficiency. Additionally, the MOOP is carried out in two steps: phase 1 solves the Single-Objective Optimization Problem (SOOP) to close the gap between variable levels, and phase 2 solves the MOOP to determine the optimal primary design factors. Furthermore, the TOPSIS approach was selected to address the MOOP. For the first time, an MCDM technique is used to solve the MOOP of a two-stage helical gearbox considering the power losses during idle motion. When designing the gearbox, the optimal values for three crucial design parameters were ascertained according to the study's results. [ABSTRACT FROM AUTHOR]

When optimizing a mechanical device, the symmetry principle provides important guidance. Minimum gearbox mass and maximum gearbox efficiency are two single objectives that need to be achieved when designing a gearbox, and they are not compatible. In order to address the multi-objective optimization (MOO) problem with the above single targets involved in building a two-stage helical gearbox with second-stage double gear sets, this work presents a novel application of the multi-criteria decision-making (MCDM) method. This study's objective is to identify the best primary design elements that will increase the gearbox efficiency while lowering the gearbox mass. To carry this out, three main design parameters were selected: the first stage's gear ratio and the first and second stages' coefficients of wheel face width (CWFW). Furthermore, a study focusing on two distinct goals was carried out: the lowest possible gearbox mass and the highest possible gearbox efficiency. Furthermore, the two stages of the MOO problem are phase 1 and phase 2, respectively. Phase 2 solves the single-objective optimization issue to minimize the difference between variable levels and the MOO problem to determine the optimal primary design factors. To solve the MOO problem, the EAMR (Evaluation by an Area-based Method of Ranking) method was also chosen. The following are important features of this study: First, a MCDM method (EAMR technique) was successfully applied to solve a MOO problem for the first time. Secondly, this work explored the power losses during idle motion to calculate the efficiency of a two-stage helical gearbox with second-stage double gear sets. This study's findings were used to identify the optimal values for three important design variables to design a two-stage helical gearbox with second-stage double gear sets. [ABSTRACT FROM AUTHOR]

This paper provides a novel application of the multi-criteria decision-making (MCDM) method to the multi-objective optimization problem (MOOP) of creating a two-stage helical gearbox (TSHG) with second-stage double gear sets (SDGSs). The aim of the study is to determine the optimum major design components for enhancing the gearbox efficiency while reducing the gearbox volume. In this work, three primary design parameters are chosen to accomplish this: the gear ratio of the first stage and the coefficients of the wheel face width (CWFW) of the first and second stages. Additionally, the study is conducted with two distinct objectives in mind: the lowest gearbox volume and the maximum gearbox efficiency. Moreover, phase 1 and phase 2, respectively, are the two stages of the MOOP. Phase 2 handles the MOOP to identify the ideal primary design factors as well as the single-objective optimization problem to minimize the difference between the variable levels. Additionally, the Multi-Attributive Ideal–Real Comparative Analysis (MAIRCA) approach is selected to deal with the MOOP. The results of the study are utilized to determine the ideal values for three crucial design parameters in order to create a TSHG with SDGSs. [ABSTRACT FROM AUTHOR]