جستجوی مقالات مرتبط با کلیدواژه "regression model" در نشریات گروه "مکانیک"

Accurate forecasting of a country's energy consumption trend is crucial in addressing the imbalance between supply and demand. Given the significant contribution of electricity to Iran's energy consumption portfolio, this research aims to model and forecast the trend of electricity consumption in Iran. Factors influencing the trend of electricity consumption were identified based on previous studies, and relevant data were collected for the period 1978-2021develop forecast models. Various models and methods were employed to predict the trend of electricity consumption, including simple indicators, energy consumption intensity, trend line analysis, regression modeling, and neural networks. The regression model estimation results indicate that the trend of electricity consumption in Iran is significantly influenced by per capita income and consumption in the previous period. From a statistical perspective, other variables such as energy price, air temperature, and rainfall did not have a significant impact on the trend. The results show that electricity consumption in Iran has increased by approximately 22.14% over the period 1978-2021, with an average annual growth rate of 7.49%. According to the forecast, electricity consumption is expected to reach 455,603 thousand megawatts by 2026. In contrast, the regression model forecast for this year is 368,959 thousand megawatts. A comparison of the prediction results reveals that the accuracy of different models and approaches varies, with the regression method exhibiting a lower measurement error than the other investigated methods in predicting the electricity consumption trend.

This paper investigates the deflection of chromium nanobeams using the design of experiments method. Using the full factorial method, a number of 100 experiments were designed in which the length and the force on the nanobeam were considered input variables at 25 and 4 levels, respectively and the deflection of the nanobeam was considered output. First, all 4 force levels (8, 9.5, 11, and 12.5 nN) were used for the analysis, and closed-form equations were obtained to estimate the deflection of the nanobeam. The results showed that the effect of the length is more than the force, by increasing the length so that the deflection changes with third degree. The third-order regression model predicts the deflection with high accuracy, and its error value is lower than the first and second-order regression models. Afterward, two levels of force (8 and 11 nN) were used for analysis, and new equations were obtained to estimate the deflection of the nanobeam. The new equations were used for interpolation and extrapolation of the nanobeam's deflection in forces of 9.5 and 12.5 nN, respectively. The results demonstrated that the regression model using two force levels accurately predicts the deflections of nanobeams as well. The results of this research demonstrated that it is possible to estimate the nanobeam's deflection without the need to perform more experiments and spend cost, perform complex calculations, and solve differential equations; the nanobeam's deflection can be accurately estimated with algebraic formulas.

In ultrasonic vibration-assisted turning, an ultrasonic vibration is added to the tool, which leads to the periodical disengagement of the tool and the work-piece. In this research, an experimental study of ultrasonic vibration-assisted turning and conventional turning on Ti6Al4V Titanium alloy is conducted. First, by analyzing different parameters, four parameters are selected as the main affecting input parameters (cutting speed, feed rate, depth of cut, and ultrasonic vibration), and the effects of these four parameters are studied on two output parameters, namely tool wear and surface roughness. After the experimental tests, a statistical analysis is performed on the results and a neural network model is developed to predict the tool wear and surface roughness. The results show that the developed neural network model has a good agreement with the experimental results. In all experiments using ultrasonic vibrations, the tool wear and surface roughness were lower in comparison with the conventional turning. The cause of the tool wear and surface roughness reduction in ultrasonic mode are reducing the average forces applied to the tool, the alternative disengagement between the tool and the workpiece and increased dynamic stability of the process.

The shunting effect in resistance spot welding (RSW) occurs due to the side electrical current created through the previous spot welds. In creation of alternating welding points, the quality of subsequent welding points is affected by a side electric current due to electro-thermal changes. This shows that the analysis of the effect of the side current is important for achieving cognition of how different parameters affect this process. Therefore, the dimensions of the welding nuggets, the metallurgical structures and the mechanical strength of the welding points are affected by the changes in the electric current and the temperature distribution caused by this current. The amount of this current depends mostly on factors such as distance, number and size of previous spot welds. In this study, experimental research was performed to investigate the shunting effect on the tensile-shear strength of RSW spot welds in aluminum alloy 2219 sheets. The most important factor in shunting (welding distance) together with welding current and time was investigated by three design of experiment factor (DOE) within the welding range to achieve a statistical model based on tensile-shear strength results. An experimental regression model was derived. Results show that increasing the welding current or distance increases the tensile-shear strength of the joints.

In this paper, the problem of intercepting very high-speed non-maneuvering ballistic targets is addressed and a novel guidance law to enhance the probability of ‘hit to kill’ is designed. The most important challenges which arise in the interception of high-speed ballistic targets include high closing velocity resulting in the lack of time for the final reaction, low radar cross section and high noise power resulting in the lack of precise detection of target track until the last moments and considerable time constant in the order of final interception phase time. By ignoring some of these important challenges, existing guidance laws show great degradation in practice. In this paper we propose a twofold technique to overcome the challenges: a staggered missile team to share the target detection information and a feedforward static guidance input. The amplitude of static guidance is deduced through a regression model using a simulator software. Simulation of the proposed guidance law on a high precision model shows the efficiency of the method.

Nanoclay is added to rubber formulation to enhance mechanical properties of rubber. In this study, the non-destructive inspection method of ultrasonic waves was used to investigate the nanoclay reinforced rubber formulation. In this method, the time between the transmission and the reflection of ultrasonic waves is measured. Longitudinal propagation velocity is obtained by the thickness of sample divided on time. In this study, an image processing method was used to measure the sample thickness due to the flexibility of rubber materials. By changing raw material composition of the rubber, the physical and mechanical properties of nanoclay reinforced rubber are altering and as a result, the velocity of ultrasonic propagation is changing. To investigate the nanoclay reinforced rubber formulation at first samples with different formulations were prepared and for each of the samples, the propagation velocity of the longitudinal sound waves was measured. Another sample with a new formulation was developed and longitudinal wave velocity was measured for validation of proposed method. The results show that the proposed method can accurately predict longitudinal wave propagation velocity in reinforced rubber formulation. Thus, this method can be used for validation of compound formulation in rubber production lines.

Non-destructive tests can identify and investigate the defects and properties of the test piece without changing the physical and mechanical properties of the sample. In this study, the non-destructive inspection method of ultrasonic waves was used to investigate the rubber formulation. In this method, the time between the transmission and the reflection of ultrasonic waves is measured and using this time propagation velocity is calculated. As components percent of the rubber are changed the physical and mechanical properties of rubber are altered and as a result, the velocity of ultrasonic propagation is changed. To investigate the rubber formulation at first 12 samples with different formulations were prepared and for each of the samples the propagation velocity of the longitudinal sound waves was measured. In order to establish the mathematical model between used elements percent in the rubber formulation and longitudinal wave velocity the multiple linear regression was used. To evaluate the accuracy another sample with a new formulation was developed and longitudinal wave velocity was measured. The comparison between the results of the test and those of the regression model showed a low error in the predicted result by the proposed model; therefor, the ultrasonic waves can be used to investigate the rubber formulation and the rubber production lines can use this non-destructive test to control tire quality online.

Superalloys are extensively used in various industries like aerospace, chemical and petrochemical industries due to their properties such as high strength at elevated temperature and good corrosion resistance. On the other hand, owing to these properties, superalloys are classified as difficult to cut materials. In the present work, the effect of cutting parameters on tool life in turning of N-155 iron-nickel-base superalloy is investigated. Cutting speed and feed rate each at five level were selected as cutting variables. Relationship between cutting parameters and output variable i.e. tool life was modeled by using response surface methodology (RSM). The results showed that there was a good agreement between the experimental results and the predicted values using the developed mathematical model. Additionally, analysis of variance was implemented to evaluate the adequacy of the regression model and respective variables. ANOVA results indicated that the cutting speed had more effect on tool life than feed rate. Moreover, wear mechanisms and failure modes of the cutting edges were analyzed by using the images of scanning electron microscope (SEM) at different cutting speeds and feed rates. It was observed that abrasion and adhesion were the most dominant wear mechanisms in this study. Finally, desirability function was used so as to predict optimum cutting parameters for achieving maximum tool life.