Dr Aydin Azizi

This paper researches the importance of ultrasound methodology for swiftly detecting faults in electric motors and rotating machines. The primary focus of this research is on the intricate signal processing of ultrasound signals from both faulty and fault-free electro-motors. The principal goal is to conduct a comprehensive statistical investigation into signal factors, examining the effects of defect progression on the factors associated with continuously operating faulty electro-motors. In addition to the statistical analysis, this study explores the envelope-frequency spectrum of the signal under both healthy and defective conditions, employing the envelope method alongside Hilbert transformation. The objective is to thoroughly scrutinize the dynamic changes in ultrasound waveform and envelope spectrum of defective states, considering diverse degrees of defect severity over an extended time span. Moreover, the paper meticulously tracks the trajectory of factor changes over a 40-day operational period of a defective electro-motor. Additionally, the study delves into the sensitivity of the ultrasound method to impulse-wise shocks, which are recurrently observed in ultrasound signals, leading to deviations in certain signal factors from their established healthy thresholds. In response to this challenge, this paper conducts a particular analysis of signal factor sensitivity to impulse-wise noises, identifying robust factors that serve as reliable tools for firm condition monitoring. These identified factors are then presented as invaluable contributors to ensuring the precision and reliability of condition monitoring, especially in the presence of disruptive impulse-wise noises.

The exponential growth of electric and hybrid vehicles, now numbering close to 6 million on the roads, has highlighted the urgent need to address the environmental impact of their lithium-ion batteries as they approach their end-of-life stages. Repurposing these batteries as second-life batteries (SLBs) for less demanding non-automotive applications is a promising avenue for extending their usefulness and reducing environmental harm. However, the shorter lifespan of SLBs brings them perilously close to their ageing knee, a critical point where further use risks thermal runaway and safety hazards. To mitigate these risks, effective battery management systems must accurately predict the state of health of these batteries. In response to this challenge, this study employs time-series artificial intelligence (AI) models to forecast battery degradation parameters using historical data from their first life cycle. Through rigorous analysis of a lithium-ion NMC cylindrical cell, the study tracks the trends in capacity and internal resistance fade across both the initial and second life stages. Leveraging the insights gained from first-life data, predictive models such as the Holt–Winters method and the nonlinear autoregressive (NAR) neural network are trained to anticipate capacity and internal resistance values during the second life period. These models demonstrate high levels of accuracy, with a maximum error rate of only 2%. Notably, the NAR neural network-based algorithm stands out for its exceptional ability to predict local noise within internal resistance values. These findings hold significant implications for the development of specifically designed battery management systems tailored for second-life batteries.

In this paper, the problem of formation control with regard to leader–follower mobile robots in the presence of disturbances and model uncertainties, without needing to know the velocity of the leader robot, is presented. For this purpose, at first, a first-order kinematic model of leader–follower and leader–leader formations is obtained, and considering the absolute velocity of the leader robots as an uncertainty, a robust adaptive controller is designed to keep the desired formation. In this case, the upper bound of uncertainty is unknown and is obtained via stable adaptive laws. Afterwards, in order to deal with the accelerated robots and obstacles, second-order leader–follower and leader–leader formation models are obtained from the previous models. A robust adaptive controller is then designed to stabilize the entire system in the presence of disturbances and modeling uncertainties, without needing to know the parameters or matrices of the formation models. In addition, by considering one of the leaders in the leader–leader model as a virtual obstacle, the challenge of avoiding moving obstacles is also addressed in the presence of uncertainties. The simulation results show the effect of the presented controllers in effectively keeping the desired leader–follower formations.

This study delves into the implementation of Fast Finite Time Fractional-Order Adaptive Sliding Mode Control (FFOASMC) for knee joint orthosis (KJO) in the presence of undisclosed dynamics. To achieve this, a novel approach introduces a Fractional-Order Sliding Surface (FOSS). In the context of limited knowledge regarding the dynamics of knee joint arthrosis, Fractional-Order Fast Adaptive Sliding Mode Control (FOFASMC) is devised. Its purpose is to ensure both finite-time stability and prompt convergence of the KJO’s state to the desired trajectory. This controller employs adaptive rules to estimate the enigmatic dynamic parameters of KJO. Through the application of the Lyapunov theorem, the attained finite-time stability of the closed loop is demonstrated. Simulation results effectively showcase the viability of these approaches and offer a comparative analysis against conventional integer-order sliding mode controllers.

In this paper, the problem of formation control with regard to leader–follower mobile robots in the presence of disturbances and model uncertainties, without needing to know the velocity of the leader robot, is presented. For this purpose, at first, a first-order kinematic model of leader–follower and leader–leader formations is obtained, and considering the absolute velocity of the leader robots as an uncertainty, a robust adaptive controller is designed to keep the desired formation. In this case, the upper bound of uncertainty is unknown and is obtained via stable adaptive laws. Afterwards, in order to deal with the accelerated robots and obstacles, second-order leader–follower and leader–leader formation models are obtained from the previous models. A robust adaptive controller is then designed to stabilize the entire system in the presence of disturbances and modeling uncertainties, without needing to know the parameters or matrices of the formation models. In addition, by considering one of the leaders in the leader–leader model as a virtual obstacle, the challenge of avoiding moving obstacles is also addressed in the presence of uncertainties. The simulation results show the effect of the presented controllers in effectively keeping the desired leader–follower formations.

This study outlines the necessary and sufficient criteria for swarm stability asymptotically, meaning consensus in a class of fractional-order multi-agent systems (FOMAS) with interval uncertainties for both fractional orders 0

Delay and especially delay in the transmission of agents’ information, is one of the most important causes of disruption to achieving consensus in a multi-agent system. This paper deals with achieving consensus in delayed fractional-order multi-agent systems (FOMAS). The aim in the present note is to find the exact maximum allowable delay in a FOMAS with non-uniform delay, i.e., the case in which the interactions between agents are subject to non-identical communication time-delays. By proving a stability theorem, the results available for non-delayed networked fractional-order systems are extended for the case in which interaction links have nonequal communication time-delays. In this extension by considering a time-delay coordination algorithm, necessary and sufficient conditions on the time delays and interaction graph are presented to guarantee the coordination. In addition, the delay-dependent stability region is also obtained. Finally, the dependency of the maximum allowable delay on two parameters, the agent fractional-order and the largest eigenvalue of the graph Laplacian matrix, is exactly determined. Numerical simulation results are given to confirm the proposed methodologies.

The application of robotic systems is widespread in all fields of life and sport. Tennis ball collection robots have recently become popular because of their potential for saving time and energy and increasing the efficiency of training sessions. In this study, an unmanned and autonomous tennis ball collection robot was designed and produced that used LiDAR for 2D mapping of the environment and a single camera for detecting tennis balls. A novel method was used for the path planning and navigation of the robot. A fuzzy controller was designed for controlling the robot during the collection operation. The developed robot was tested, and it successfully detected 91% of the tennis balls and collected 83% of them.

This paper focuses on investigating a parallel camera stabilizing manipulator with three angular degrees of freedom controlled by three linear actuators. An experimental setup is designed and manufactured to actively isolate the host vehicle's disturbing motions. The kinematic analysis of the manipulator combined with a controller is used to disturbance rejection coming from the base platform. Two inertia measurement units (IMU) are used for real-time feedback from the base and up-per platforms' orientation. A Kalman filter is implemented for handling the noises and drifts of the IMUs data. Inverse kinematics of the manipulator is used for calculating the actuating commands and velocity control of the linear motors. The experimental results of the proposed camera stabilizing system are shown. The results indicate its good capability in following the reference input of the controller. Considering the closed kinematic chain of the system and its stiff parallel architecture, this system can be a good choice for the stabilizing system of ground and aerial vehicles.

The presence of faults in a system leads to a lower value for efficiency, accuracy and speed, and, in some cases, even a complete breakdown. Thus, early fault detection is a major factor in efficiency and productivity of the procedure. In recent decades, many research studies have been conducted on troubleshooting and secondary fault detection. The current work presents an efficient and novel observer design capable of stabilizing the residue and dynamic error for the nonlinear Lipschitz systems with faults as well as a troubleshooting analysis and determining the formation of secondary faults in defective systems. The observer is designed based on linearizing dynamic error considering uncertainty, disturbance, and defects by employing non-linear gain factors instead of using state transformation. The dynamic error and residue stabilization of a non-linear faulty system have been discussed as well as the likelihood of secondary fault generation. The results indicate that the observer is able to determine fault-emergence, fault-disappearance and secondary fault formation well and quite fast.

The service life of Lithium-ion batteries disposed from electric vehicles, with an approximate remaining capacity of 75–80%, can be prolonged with their adoption in less demanding second life applications such as buildings. A photovoltaic energy generation system integrated with a second life battery energy storage device is modelled mathematically to assess the design’s technical characteristics. The reviewed studies in the literature assume, during the modelling process, that the second life battery packs are homogeneous in terms of their initial state of health and do not consider the module-to-module variations associated with the state of health differences. This study, therefore, conducts mathematical modelling of second life battery packs with homogenous and heterogeneous state of health in module level using second-order equivalent circuit model (ECM). The developed second-order ECM is validated against experimental data performed in the lab on 3Ah NCM batteries. The degradation parameters are also investigated using the battery cell’s first life degradation data and exponential triple smoothing (ETS) algorithm. The second-order ECM is integrated with the energy generation system to evaluate and compare the performance of the homogenous and heterogeneous battery packs during the year. Results of this study revealed that in heterogeneous packs, a lower electrical current and higher SOC is observed in modules with lower state of health due to their higher ohmic resistance and lower capacity, compared to the other modules for the specific battery pack configuration used in this study. The methodology presented in this study can be used for mathematical modelling of second life battery packs with heterogenous state of health of cells and modules, the simulation results of which can be employed for obtaining the optimum energy management strategy in battery management systems.

The design and performance optimization of fully electric trucks constitute an integral goal of the transport sector to meet climate emergency measures and local air quality requirements. Most studies in the literature have determined the optimum pack size based on economic factors, without accounting for the details of pack behavior when varying the size. In this paper, the effect of battery pack sizing and cargo capacity of a class 8, 41-ton truck on its overall energy performance and technical parameters of its powertrain is investigated. For this purpose, the proposed electric truck is designed and mathematically modelled using AVL CRUISE M software. The second-order equivalent circuit model is developed to predict the battery packs’ parameters. The proposed battery pack model is extracted from experimental analysis on SONY VTC6 lithium-ion batteries performed in the lab. The weight changes due to adding the battery packs to the truck are also estimated and have been taken into account. The mathematical model of the powertrain is simulated in the long-haul driving cycle considering different cargo capacities and battery pack sizes. The results of this study revealed that the battery pack voltage reached its minimum value when the maximum cargo capacity was applied for the 399 kWh battery pack. In addition, increasing the occupied cargo capacity from 10% to 100% resulted in an increase in the regenerative brake energy of up to 9.87 kWh, while changing the battery size imposed minimal impacts on regenerative brake energy recovery as well as energy consumption.

This study investigates the design and sizing of the second life battery energy storage system applied to a residential building with an EV charging station. Lithium-ion batteries have an approximate remaining capacity of 75–80% when disposed from Electric Vehicles (EV). Given the increasing demand of EVs, aligned with global net zero targets, and their associated environmental impacts, the service life of these batteries, could be prolonged with their adoption in less demanding second life applications. In this study, a technical assessment of an electric storage system based on second life batteries from electric vehicles (EVs) is conducted for a residential building in the UK, including an EV charging station. The technical and energy performance of the system is evaluated, considering different scenarios and assuming that the EV charging load demand is added to the off-grid photovoltaic (PV) system equipped with energy storage. Furthermore, the Nissan Leaf second life batteries are used as the energy storage system in this study. The proposed off-grid solar driven energy system is modelled and simulated using MATLAB Simulink. The system is simulated on a mid-winter day with minimum solar irradiance and maximum energy demand, as the worst case scenario. A switch for the PV system has been introduced to control the overcharging of the second life battery pack. The results demonstrate that adding the EV charging load to the off-grid system increased the instability of the system. This, however, could be rectified by connecting additional battery packs (with a capacity of 5.850 kWh for each pack) to the system, assuming that increasing the PV installation area is not possible due to physical limitations on site.

This investigation attempts to study a possible controller in improving the dynamic stability of capacitive microstructures through mitigating the effects of disturbances and uncertainties in their resultant dynamic behavior. Consequently, a nonsingular terminal sliding mode control strategy is suggested in this regard. The main features of this particular control strategy are its high response speed and its non-reliance on powerful controller forces. The stability of the controller was investigated using Lyapunov theory. For this purpose, a suitable Lyapunov function was introduced to prove the stability of a controller, and the singularity conditions and methods to overcome these conditions are presented. The achieved results proved the high capability of the applied technique in stabilizing of the microstructure as well as mitigating the effects of disturbances and uncertainties.

Rock tensile strength (TS) is an essential parameter for designing structures in rock-based projects such as tunnels, dams, and foundations. During the preliminary phase of geotechnical projects, rock TS can be determined through laboratory works, i.e., Brazilian tensile strength (BTS) test. However, this approach is often restricted by laborious and costly procedures. Hence, this study attempts to estimate the BTS values of rock by employing three non-destructive rock index tests. BTS predictive models were developed using 127 granitic rock samples. Since the simple regression analysis did not yield a meaningful result, the development of models that integrate multiple input parameters were considered to improve the prediction accuracy. The effects of non-destructive rock index tests were examined through the use of multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) approaches. Different strategies and scenarios were implemented during modelling of MLR and ANFIS approaches, where the focus was to consider the most important parameters of these techniques. As a result, and according to background and behaviour of the ANFIS (or neuro-fuzzy) model, the predicted values obtained by this intelligent methodology are closer to the actual BTS compared to MLR which works based on linear statistical rules. For instance, in terms of system error and a-20 index, values of (0.84 and 1.20) and (0.96 and 0.80) were obtained for evaluation parts of ANFIS and MLR techniques, which revealed that the ANFIS model outperforms the MLR in forecasting BTS values. In addition, the same results were obtained through ranking systems by the authors. The neuro-fuzzy developed in this study is a strong technique in terms of prediction capacity and it can be used in the other rock-based projects for solving relevant problems.

The suspension system is referred to as the set of springs, shock absorbers, and linkages that connect the car to the wheel system. The main purpose of the suspension system is to provide comfort for the passengers, which is created by reducing the effects of road bumpiness. It is worth noting that reducing the effects of such vibrations also diminishes the noise and undesirable sound as well as the effects of fatigue on mechanical parts of the vehicle. Due to the importance of the abovementioned issues, the objective of this article is to reduce such vibrations on the car by implementing an active control method on the suspension system. For this purpose, a conventional first-order sliding mode controller has been designed for stochastic control of the quarter-car model. It is noteworthy that this controller has a significant ability to overcome the stochastic effects, uncertainty, and deal with nonlinear factors. To design a controller, the governing dynamical equation of the quarter-car system has been presented by considering the nonlinear terms in the springs and shock absorber, as well as taking into account the uncertainty factors in the system and the actuator. The design process of the sliding mode controller has been presented and its stability has been investigated in terms of the Lyapunov stability. In the current research, road surface variations are considered as Gaussian white noise. The dynamical system behavior for controlled and uncontrolled situations has been simulated and the extracted results have been presented. Besides, the effects of existing uncertainty in the suspension system and actuator have been evaluated and controller robustness has been checked. Also, the obtained quantitative and qualitative compressions have been presented. Moreover, the effect of controller parameters on the basin of attraction set and its extensiveness has been assessed. The achieved results have indicated the good performance and significant robustness of the designed controller to stabilize the suspension system and mitigate the effects of road bumpiness in the presence of uncertainty and noise factors.

Parallel-plates based micro-tunable capacitors are known to have low travel ranges, which deteriorate as going even lower in terms of their initials gap sizes. Such conditions have put strict requirements on the operation of such designs and hence hindering their use in numerous practical applications requiring high tunability. This work is proposed to examine the possibility to implement a closed-loop control strategy to increase the maximum capacitance and therefore tunability of micro tunable capacitors. The suggested control strategy is implemented on an electrostatically actuated parallel-plates (one stationary and one movable) based micro-capacitor and had an objective to stabilize the movable electrode when it is close to the fixed one for the sake of maximizing its maximum capacitance and possibly improving its overall tunability. Robustness of the micro-capacitor to the so-called pull-in phenomenon (short-circuit instability) when using the closed loop control scheme is studied. Indeed, an adaptive sliding mode controller is designed to compensate the effects of uncertainty, disturbance and eliminate any possibility for chattering phenomenon. The controller proficiencies in terms of stabilizing the micro-capacitor and its robustness to uncertainty as well as disturbance have been thoroughly examined. Furthermore, the effects of the control parameters on the behavior of micro-capacitor, such as overshoot, settling time, steady state error, robustness to uncertainty, external disturbances and to the chattering phenomenon, have been completely inspected. The obtained results indicated satisfactory proficiency and trustworthiness of the proposed control strategy to achieve high level of tunability and maximum capacitance.

The goal of this research is to design an Artificial Intelligence controller for the active suspension system of vehicles. The Ring Probabilistic Logic Neural Network (RPLNN) architecture has been adopted to design the proposed controller, and the pavement condition has been modelled utilizing Gaussian white noise. The results show that the proposed RPLNN controller has an effective performance to reduce the unwanted stochastic effect of the road profile.

Industrial robots have a great impact on increasing the productivity and reducing the time of the manufacturing process. To serve this purpose, in the past decade, many researchers have concentrated to optimize robotic models utilizing artificial intelligence (AI) techniques. Gimbal joints because of their adjustable mechanical advantages have been investigated as a replacement for traditional revolute joints, especially when they are supposed to have tiny motions. In this research, the genetic algorithm (GA), a well-known evolutionary technique, has been adopted to find optimal parameters of the gimbal joints. Since adopting the GA is a time-consuming process, an artificial neural network (ANN) architecture has been proposed to model the behavior of the GA. The result shows that the proposed ANN model can be used instead of the complex and time-consuming GA in the process of finding the optimal parameters of the gimbal joint.

This study aims to stabilize the unwanted fluctuation of buildings as mechanical structures subjected to earth excitation as the noise. In this study, the ground motion is considered as a Wiener process, in which the governing stochastic differential equations have been presented in the form of Ito equation. To stabilize the vibration of the system, the ATMD system is considered and located on the upmost story of the building. A sliding mode controller has been utilized to control the ATMD system, which is a robust controller in the presence of uncertainty. For this purpose, the design of a sliding mode controller for the general dynamic system with Lipschitz nonlinearity and considering the Ito relations has been accomplished. The mentioned design has been implemented considering the presence of the Weiner process and existence of uncertainty in the structure and actuator. Then, the obtained general control law has been generalized to control the ATMD system. The results show that the designed controller is effective to reduce the effect of the unwanted impused vibrations on the building.

Enhancement of both efficiency and reliability of MEMS structures has always been an interesting and even essential issue for research community. This paper provides a comparative investigation in this field focusing on the role of initially curved electrodes of capacitive micro-switches. Four models have been introduced by appliance of curved microbeams as either upper or lower electrodes of a capacitive MEMS switch, as well as the conventional base model with straight both electrodes. By introducing a mathematical model and appropriate numerical procedure, the contact area between two electrodes, which has direct effect on the reliability has been estimated using Hertz relation for all models. The electromechanical coupling factor which is related to the efficiency of the switch has been calculated considering the stored mechanical and electrical energy of the system. The results have shown that by appliance of an initial curvature to the both electrodes, the estimated contact area can increase up to 279% compared to the conventional switches. Also, a switch with straight moveable electrode and curved substrate exhibits an increase in coupling factor up to 24% compared to the base model, while increasing the pull-in voltage of the switch.

Considering the importance of closed-loop control for control of capacitive microstructures and the high capability of the sliding-mode controllers in control of nonlinear systems, the present study has investigated the closed-loop control of these structures using the sliding-mode controller. For this purpose, the sliding-mode controller was designed for single- and double-input cases, and the applied voltages between the capacitive electrodes was considered as the control inputs. The simulation results for both single- and double-input cases have been extracted and presented, and it was concluded that achieving an accurate control for the single input case is not always feasible given that the sign of the sliding surface changes and the control parameter assume positive definite values. Moreover, it has been shown that an accurate control could be achieved by employing the double-input controller without any limitations. The chattering phenomenon was also thoroughly studied in the initial controller design. To eliminate this phenomenon, a smooth variation has been applied to the switching of the sliding surface. In addition to tracking, the capacitive microstructure has been investigated to reduce the relative distance between the two electrodes (in order to increase the capacitive storage), and the obtained results have been presented.

The properties of small- and medium-sized enterprises (SMEs) make them one of the most important categories of enterprises for the economics of challenging world. SMEs, in most countries, are still enterprises with marketing and financial challenges. In addition, most of these challenges are related to their production and product characteristics. On the other hand, SMEs should fulfil the costumer’s demands. In order to reach these goals, SMEs must reach the highest level of production quality and quantity and successfully sustain them. Consequently, various manufacturing paradigms have been offered by an Industry 4.0 concept, which offers a variety of solutions to increase the productivity and enhance the performance of SMEs. It should be noted that implementation of these manufacturing paradigms for SMEs is quite difficult and sometimes risky for several reasons. Still, amidst all these difficulties and challenges, the benefits and idealism of the Industry 4.0 paradigms prevail. From productivity to market, it is difficult to deny that SMEs are frightened by the challenges they face and fleeing from the potential of overcoming them. This paper is an extended version of the research by Ghafoorpoor Yazdi et al. (2018) and conducts a hybrid methodology to satisfy the SMEs by validating and verifying any optimization idea before implementing the Industry 4.0 concept. To reach the study goals, an intelligent Material Handling System (MHS) with agent-based control architecture has been developed. The developed MHS has been utilized for auto parts distribution. The system performance has been evaluated, and some solutions have been provided to optimize the performance of system. To evaluate the target system’s performance, an analytical time study method has been utilized. The time study has an Overall Equipment Effectiveness (OEE) standard approach to identifying the matters that need to be resolved and optimized to increase system performance. The other part of the methodology is generating a simulation model of the real system by use of ARENA® software to evaluate the system’s performance before implementing the optimization idea and modifying the real system. Furthermore, as the sustainability strategies create many synergistic effects for SMEs, after evaluating the effects of the optimization ideas on OEE percentage, the influence of the OEE changes on manufacturing sustainability has been investigated. The results show that optimizing the OEE in SMEs with sustainability approaches can create competitive advantages, rather than simply focusing on reducing unsustainability.

Predictive models have been widely used in different engineering fields, as well as in petroleum engineering. Due to the development of high-performance computer systems, the accuracy and complexity of predictive models have been increased significantly. One of the common methods for prediction is artificial neural network (ANN). ANN models in combination with optimization algorithms provide a powerful and fast tool for the prediction and optimization of processes which take a large amount of time if they are simulated using common simulation technics. In the present paper, to predict penetration rate during drilling process, several ANN models were developed based on the data obtained from drilling of a gas well located in south of Iran. Regarding the R2 and RMSE values of the developed models, the best model was selected for prediction of penetration rate. In the next step, artificial bee colony algorithm was used for optimization of the parameters which are effective on rate of penetration (ROP). Results showed that the model is accurate enough for being used in the prediction and optimization of ROP in drilling operations.

The primary goal of this research is to apply principles of ergonomics using virtual reality to develop an integrated approach to provide interactive designing and evaluating of production in a manufacturing cell. One of the most common parts of every cell is storage unit, while, studies have been done on it is not sufficiently considered the importance of the design based on human labor ergonomic. Thus, the objective of this study is to improve the design of a common storage unit used in the assembly process for pen production utilizing the RULA method for improving the ergonomic posture. The significance of this research is to investigate the interaction between a labor and a product often involves ergonomic elements. The developed method decreases the production time, reduces the fatigue on worker, mitigates the risk of work-related musculoskeletal disorders, and consequently improves the productivity. The pen production assembly line is chosen as a case study for the design of the storage units is in direct interaction with human ergonomic and performance consequently. DELMIA human activity analysis is used to study the fatigue in a virtual environment. The comparison of the real time and simulation, experimental results show that the modification of storage unit could decrease the fatigue by 42.5%. It is demonstrated that the interactive design of a storage unit based on proposed technique improves the productivity and efficiency of the workstation.

Flyrock is an adverse effect produced by blasting in open-pit mines and tunnelling projects. So, it seems that the precise estimation of flyrock is essential in minimizing environmental effects induced by blasting. In this study, an attempt has been made to evaluate/predict flyrock induced by blasting through applying three hybrid intelligent systems, namely imperialist competitive algorithm (ICA)–artificial neural network (ANN), genetic algorithm (GA)–ANN and particle swarm optimization (PSO)–ANN. In fact, ICA, PSO and GA were used to adjust weights and biases of ANN model. To achieve the aim of this study, a database composed of 262 datasets with six model inputs including burden to spacing ratio, blast-hole diameter, powder factor, stemming length, the maximum charge per delay, and blast-hole depth and one output (flyrock distance) was established. Several parametric investigations were conducted to determine the most effective factors of GA, ICA and PSO algorithms. Then, at the end of modelling process of each hybrid model, eight models were constructed and their results were checked considering two performance indices, i.e., root mean square error (RMSE) and coefficient of determination (R2). The obtained results showed that although all predictive models are able to approximate flyrock, PSO–ANN predictive model can perform better compared to others. Based on R2, values of (0.943, 0.958 and 0.930) and (0.958, 0.959 and 0.932) were found for training and testing of ICA–ANN, PSO–ANN and GA–ANN predictive models, respectively. In addition, RMSE values of (0.052, 0.045 and 0.057) and (0.045, 0.044 and 0.058) were achieved for training and testing of ICA–ANN, PSO–ANN and GA–ANN predictive models, respectively. These results show higher efficiency of the PSO–ANN model in predicting flyrock distance resulting from blasting. Moreover, sensitivity analysis shows that hole diameter is more effective than others.

The goal of this paper is to design an effective Proportional Integral Derivative (PID) controller, which will control the active suspension system of a car, in order to eliminate the imposed vibration to the car from pavement. In this research, Gaussian white noise has been adopted to model the pavement condition, and MATLAB/Simulink software has been used to design a PID controller, as well as to model the effect of the white noise on active suspension system. The results show that the designed controller is effective in eliminating the effect of road conditions. This has a significant effect on reducing the fuel consumption and contributes to environment sustainability.

Nowadays, small and medium sized enterprises (SMEs) are becoming increasingly competitive. In order to fulfill the rapidly changing market and diversified demands of customers, the SMEs need to achieve and maintain high productivity and quality, with fast response, sufficient flexibility, and short lead times. Therefore, Industry 4.0 offers various manufacturing paradigms that might be a solution in order to increase the productivity of SMEs such as intelligent and flexible manufacturing. Furthermore, in the last decade, the emphasis on adopting eco-friendly practices, implementing sustainability measures, and protecting the environment has continued to grow, to gain traction across SMEs. In fact, because of this need, many SMEs are now adopting sustainable manufacturing practices in response to this increased focus on sustainability and environmental stewardship. The main purpose of this paper is to design and study the implementation of a sustainable, intelligent material handling system for material distribution with utilizing an agent-based algorithm as control architecture. A time study-based methodology has been implemented to evaluate the overall equipment effectiveness (OEE) to identify the matters that need to be resolved and optimized to increase the OEE percentage with consideration of the sustainability of the system. An exhaustive analytical trend applied to the generated time study data. Accordingly, further hardware, software, and layout design limitation and problems detected, and the proper solutions were anticipated. The observed time study results were presented, a fundamental set of analytical observation and information with associated histograms was reviewed. In addition, the study aims to recognize and analyze effective factors on the sustainability of improved processes, using a simple model. To do this, using experts’ viewpoints, affective factors on the sustainability of process improvement activities are determined.

The influence of global economy has led to changes in the conventional approaches for manufacturing companies. In this case, manufacturing companies has taken under consideration several essential characteristics such as real-time reaction to changes, quick and quality response in satisfying customer requests, in both hardware equipment and software modules by which the production processes are improved for next generation manufacturing systems. Nowadays, material handling system for manufacturing purposes is the key component for any modern manufacturing processes. Additionally, due to high variety of products and shorter response times in today’s manufacturing industry, the demand for smart material handling system has increased. Therefore, to recover these demands, custom-built systems have to be implemented which requires individually created control software which deals with flow control, product routings, layout and products distribution. Internet of Things (IoT) is a concept that is set to enhance manufacturing by improving output quality and workflow efficiency. Nowadays manufacturing industries are utilizing internet of things concept as a network that connects multiple sensors and devices through the internet. There are thousands of these sensors within a manufacturing environment, from the temperature gauges to the individual components on the material handling system, connecting data from these devices can improve business efficiency, innovation as well as strengthening security. However; if the data is not connected to a single common platform talking the same language, these potential gains are lost. The aim of this research is to develop an agent based feedback control system and implement it utilizing IoT to control the material handling system in manufacturing industry.

The influence of global economy has led to changes in the conventional approaches for manufacturing companies. In this case, manufacturing companies has taken under consideration several essential characteristics such as real-time reaction to changes, quick and quality response in satisfying customer requests, in both hardware equipment and software modules by which the production processes are improved for next generation manufacturing system. Nowadays, material handling system for manufacturing purposes is the key component for any modern manufacturing processes. Additionally, due to high variety of products and shorter response times in today’s manufacturing industry, the demand for smart material handling system has increased. Therefore, to recover these demands, custom-built systems should be implemented which requires individually created control software which deals with flow control, product routings, and layout and products distribution. Therefore, this paper aims to design and fabricate smart material handling system. This system includes conveyors, robot and sensors. Conveyors are designed in such a way that it can facilitate easy and has a safe loading and unloading which is done with a robotic arm. To make the system smart, it is integrated with sensors in order to be able to distinguish and distribute objects.

Minimally Invasive Surgery (MIS) is a modern surgicaltechnique. In MIS the surgeon should have more experienced compared to open surgery to perform a surgery by long instruments separation from the operation room. So, the surgeon has not any sense of grasping the organ as normally has in regular surgery in which the surgeon has a complete sense of touch. Thus, a less expert surgeon who wishes to perform MIS should be trained and perform open surgery for a couple of years before he/she can involve in a surgery operation. These limitations motivate researchers in biomedical engineering to explore the new smart system and design new surgical grasper. In this project we design and model a smart grasper which can sense and recognition the grasped material respect to the generated voltage, also finding the magnitude of the allowable applied force by the surgeon which does not hit the organs is possible.

Recent advances in modern manufacturing industries have created a great need to track and identify objects and parts by obtaining real-time information. One of the main technologies which has been utilized for this need is the Radio Frequency Identification (RFID) system. As a result of adopting this technology to the manufacturing industry environment, RFID Network Planning (RNP) has become a challenge. Mainly RNP deals with calculating the number and position of antennas which should be deployed in the RFID network to achieve full coverage of the tags that need to be read. The ultimate goal of this paper is to present and evaluate a way of modelling and optimizing nonlinear RNP problems utilizing artificial intelligence (AI) techniques. This effort has led the author to propose a novel AI algorithm, which has been named “hybrid AI optimization technique,” to perform optimization of RNP as a hard learning problem. The proposed algorithm is composed of two different optimization algorithms: Redundant Antenna Elimination (RAE) and Ring Probabilistic Logic Neural Networks (RPLNN). The proposed hybrid paradigm has been explored using a flexible manufacturing system (FMS), and results have been compared with Genetic Algorithm (GA) that demonstrates the feasibility of the proposed architecture successfully.

Radio frequency identification is a developing technology that has recently been adopted in industrial applications for identification and tracking operations. The radio frequency identification network planning problem deals with many criteria like number and positions of the deployed antennas in the networks, transmitted power of antennas, and coverage of network. All these criteria must satisfy a set of objectives, such as load balance, economic efficiency, and interference, in order to obtain accurate and reliable network planning. Achieving the best solution for radio frequency identification network planning has been an area of great interest for many scientists. This article introduces the Ring Probabilistic Logic Neuron as a time-efficient and accurate algorithm to deal with the radio frequency identification network planning problem. To achieve the best results, redundant antenna elimination algorithm is used in addition to the proposed optimization techniques. The aim of proposed algorithm is to solve the radio frequency identification network planning problem and to design a cost-effective radio frequency identification network by minimizing the number of embedded radio frequency identification antennas in the network, minimizing collision of antennas, and maximizing coverage area of the objects. The proposed solution is compared with the evolutionary algorithms, namely genetic algorithm and particle swarm optimization. The simulation results show that the Ring Probabilistic Logic Neuron algorithm obtains a far more superior solution for radio frequency identification network planning problem when compared to genetic algorithm and particle swarm optimization.

Friction stir welding (FSW) is an innovative solid state joining technique and has been employed in aerospace, rail, automotive and marine industries for joining aluminum, magnesium, zinc and copper alloys. In this process, parameters play a major role in deciding the weld quality these parameters. Using predictive modelling for mechanical properties of FSW not only reduce experiments but also is created standard model for predict outcomes. Therefore, this paper is undertaken to develop a model to predict the microstructure and mechanical properties of FSW. The proposed model is based on Ring Probabilistic logic Neural Network (RPLNN) and optimize it utilizing Genetic Algorithms (GA). The simulation results show that performance of the RPLNN algorithm with utilizing Genetic Algorithm optimizing technique compared to real data is reliable to deal with function approximation problems, and it is capable of achieving a solution in few convergence time steps with powerful and reliable results.

Centrifugal compressors are critical rotating equipments in industrial processes, especially for natural gas applications. Compressible gas exhibit complex flow behavior that requires a thorough understanding to ensure extended, safe and stabilized compressor performance. Without, Centrifugal compressors can undergo surge which restricts compressor operational range and jeopardize the safety of its components. Dynamic simulation then becomes necessary to evaluate performance and to implement integrated control schemes. This paper will investigate how centrifugal compressor responds to changes at inlet conditions i.e. increase in pressure, temperature and molecular weight. The effect of these changes on compressor performance will be simulated using Unisim Design dynamic compressor model. Results show that compressor operation is easily influenced by changes in inlet condition and that integrated control schemes are effective in safeguarding compressor from surge.

In this study, 10-mm-thick pure copper plates were friction stir welded at a constant rotational speed of 700 rpm and different traverse speeds of 50, 100, 150, and 200 mm/min using a square pin profile tool. The thermal cycles and peak temperatures were recorded using accurate thermocouples. In addition, the microstructural features of the joints were examined by optical microscopy. Furthermore, for analyzing the mechanical performance of the joints, hardness and tensile tests were conducted. In addition, the fractography of the joints was done using a scanning electron microscope. The results showed that higher traverse speeds caused lower heat input and peak temperature and hence finer grains. Furthermore, higher traverse speeds lead to formation of the defects in the joints. With increasing the traverse speed, the ultimate tensile strength of the joints increased to a maximum value and then decreased. Likewise, with increasing the traverse speed, the hardness and elongation of the joints increased and decreased, respectively. Additionally, the joints welded at lower traverse speeds revealed more ductile fracture mode.

The biomechanics of the glenohumeral joint depend on the interaction of both static and dynamic stabilizing structures. The combined effect of these stabilizers is to support the multiple degrees of motion within the glenohumeral joint. Total shoulder arthroplasty requires release of contracted tissues, repair of rotator cuff defects, reconstruction of normal skeletal anatomy with proper sizing, and positioning of components. Arthroplasty of the shoulder is unlike arthroplasty of the hinge joints when the collateral ligaments afford a high degree of stability and is even distinct from the hip when bony conformity is large and range of motion is less. The goal of this paper is biomechanical analyses of normal, injured and implanted shoulder joint.

Industrial structures deteriorate generally in an uncontrollable rate. To assess the short-term impact due to hazards and the long-term deterioration process due to physical aging and routine operation, structural health monitoring (SHM) is proposed. In this paper as a model of vessel a simply supported beam under constant distributed force is investigated. The objective is to estimate the severity of damage in a known location with sensing devices. As no actuation is consider the problem is solved statically. Finite element method by using MATLAB software to calculate the global stiffness matrix of the smart beam has been applied. It is expected the results show that higher severity of damage causes higher deflection and higher sensor of voltage.

The Genetic Algorithm (GA) is a stochastic global search method that mimics the metaphor of natural biological evolution. GA operates on a population of potential solutions applying the principle of survival of the fittest to produce (hopefully) better and better approximations to a solution. Genetic algorithms are particularly suitable for solving complex optimization problems and for applications that require adaptive problem solving strategies. Here, in this paper genetic algorithm is introduced as an optimization technique.

Neural networks have been applied very successfully in the identification and control of dynamic systems. The universal approximation capabilities of the multilayer perceptron have made it a popular choice for modeling nonlinear systems and for implementing general-purpose nonlinear controllers. In this paper we try to model and control the mass-spring-damper mechanism as a 1 DOF system using neural networks. The control architecture used in this paper is Model reference controller (MRC) as one of the popular neural network control architectures.

This paper presents a modified sensor-based online method for mobile robot navigation generating paths in dynamic environments. The core of the navigation algorithm is based on the velocity obstacle avoidance method and the guidance-based tracking algorithm. A fuzzy decision maker is designed to combine the two mentioned algorithms intelligently. Hence the robot will be able to decide intelligently in various situations when facing the moving obstacles and moving target. A noble noise cancellation algorithm using Neural Network is designed to navigate the robot in an uncertain dynamic environment safely. The results show that the robot can track a moving target while maneuvering safely in dynamic environment and avoids stationary and moving obstacles.

According to the world health organization, major depressive disorder (MDD) is considered as the fourth main cause of death and premature weakness in the whole world. Abnormality in the hormones and neurotransmitters level is the one of the main factors which may result in this disorder. In this article melatonin is chosen among these hormones, which is the most implicated to control sleep and depression. Because the measurement of melatonin is crucial important, the fuzzy logic approach as the mathematical method is utilized to making melatonin behavior model. In this paper, two effective factors on melatonin are modeled by fuzzy logic. This model is only a part of our project which is performed for modeling of the major depression.

A method for optimization in trajectory planning of 3RUU robot manipulators is presented in this paper. At first, to get the optimal trajectory, position analyses has been done on the 3RUU robot, then an objective function which have two terms is minimized: first term relevant to the total execution time and another one relevant to the integral of the squared jerk (defined as the derivative of the acceleration toward time) along the trajectory and this Guarantees that the obtained trajectory is smooth. This technique let to calculate the kinematic constraints on the motion of the robot, defined as upper limits on the absolute values of velocity, acceleration and jerk. , the total execution time does not require to be set priori. The algorithm has been tested in simulation and in comparison with other important trajectory planning techniques it has been given good results.