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In this study, the design and driving performance evaluation of a driving system for driving on deformable terrain was performed using terramechanics theory and multi-body dynamics simulation. For the design of the driving system, the mechanical interaction of track-terrain was analyzed using a Bekker-based model. Based on the analyzed results, the design of a track suitable for the deformable terrain to be driven and selection of a power source (engine, transmission, etc.) were carried out. A multi-body simulation model of the tracked vehicle reflecting the designed track and the selected power source was developed, and a ground model reflecting the mechanical property of terrain was also developed to analyze the mechanical interaction of the track-terrain through simulation. In addition, each link constituting the track was modeled as a 6 DOF spring/damper system to consider the track's tension force and load distribution under the track, and through this, different ground pressure and soil thrust were applied according to the motion state of each link. Finally, driving performance analyses were performed using the developed tracked vehicle, and as a result, it was confirmed that the driving requirements of the tracked vehicle were satisfied.

This study presents sensitivity-based adaptive model-free adaptive displacement and velocity control algorithms for unknown single-input multi-output (SIMO) systems using recursive least squares (RLS) with exponential forgetting. Nonlinearities in real-world systems make predictions of the dynamic behaviors of systems challenging and have a negative influence on the performance of control systems. To address this problem, the sensitivity-based model-free adaptive control algorithm, which does not require a mathematical model of a system, is proposed in this study. In the algorithm, a feedback control law for a SIMO system was designed with multiple adaptive feedback gains. The virtual function that represents the relationship between multiple feedback gains and control errors was also designed to construct an adaptation rule with a linear combination. The coefficients in the virtual function were estimated in real-time using RLS with multiple exponential forgetting factors. By using the estimated coefficients, the adaptation rule for feedback gains with the gradient descent method was designed, which can be automatically adjusted through applying Lyapunov’s direct method. The performance of the proposed model-free adaptive control algorithm was evaluated in an actual DC motor system under angular displacement and velocity tracking control scenarios. Evaluation results show that the designed control algorithm enables the system to track the desired output successfully without system modeling.

Automated excavation is hard to achieve due to several inherent problems such as resistive force acting against the bucket, non-homogenous dynamics of various excavation media, and nonlinearities of the excavator’s hydraulics system. To deal with this issue, this paper provides an integrative control strategy for successful autonomous excavation that considers the mutually associated factors, i.e., position, contour, and force control. For the position tracking, a non-linear PI controller was designed to track the position of individual actuators of the excavator and thereby control the bucket tip’s position. In addition, the contour control technique was applied to achieve an optimal excavation path to minimize contour errors. Finally, to compensate for the ground resistive force during digging tasks, a force impedance controller was designed along with the time-delayed control that reduces the effect of dynamic uncertainties. Experimental results with a modified mini-wheeled excavator show that the developed integrative tracking control strategy can provide a comprehensive solution to improving the tracking performance for autonomous excavation that can simultaneously deal with the critical components of position, contour, and force control.

Controlled Environment Agriculture (CEA) holds promise as a way to intensify current agricultural production systems while limiting pressures on land, water, and energy resources. However, its use has not yet been widely adopted, in part because the engineering design considerations and associated challenges are not well known. This is even more apparent for aeroponics, where the additional cost and complexities in controlling atomization have yet to establish an advantage in scale over simpler hydroponic systems To shed light on these considerations and challenges, an instrumented aeroponic system was prototyped with the goal of creating a quantitative model of growth for various species of leafy greens. As the first consideration, pressure swirl atomizers were paired with a diaphragm-type pressure tank to supply the necessary pressures needed for effective atomization. Secondly, nutrient solution was mixed on-demand from Reverse Osmosis (RO) water and concentrated nutrient stock then pumped into the pressure tank using a positive displacement pump. A bamboo-based substrate that allowed both germination and extended vegetative growth was supported on a stainless steel mesh and PVC frame acting as a grow tray. Finally, a camera microservice platform was developed to quantify plant growth using a computer vision pixel-based segmentation method.

In this paper, a self-tuning rule-based position control algorithm is proposed for DC motors with system parameter estimation using the recursive least squares method. First, a mathematical model of the angular position control of a DC motor was derived. Next, the time-varying parameters including the rotational inertia in the model were estimated using the RLS method along with multiple forgetting factors without prior knowledge of the system. Based on the derived model and the parameter estimation, a sliding mode control algorithm was designed by applying a self-tuning rule that enables the magnitude of the voltage input to be adaptively adjusted for improvement of the energy efficiency. The performance of the designed control algorithm was then experimentally evaluated under several different load conditions. Finally, the evaluation results show that the designed controller achieves a satisfactory capability for a DC motor to deal with both tracking accuracy and energy efficiency without prior knowledge of the system.

This paper presents a sliding mode control (SMC)-based path-tracking algorithm for autonomous vehicles by considering model-free adaptive feedback actions. In autonomous vehicles, safe path tracking requires adaptive and robust control algorithms because driving environment and vehicle conditions vary in real time. In this study, the SMC was adopted as a robust control method to adjust the switching gain, taking into account the sliding surface and unknown uncertainty to make the control error zero. The sliding surface can be designed mathematically, but it is difficult to express the unknown uncertainty mathematically. Information of priori bounded uncertainties is needed to obtain closed-loop stability of the control system, and the unknown uncertainty can vary with changes in internal and external factors. In the literature, ongoing efforts have been made to overcome the limitation of losing control stability due to unknown uncertainty. This study proposes an integrated method of adaptive feedback control (AFC) and SMC that can adjust a bounded uncertainty. Some illustrative and representative examples, such as autonomous driving scenarios, are also provided to show the main properties of the designed integrated controller. The examples show superior control performance, and it is expected that the integrated controller could be widely used for the path-tracking algorithms of autonomous vehicles.

As autonomous tractors become more common crop harvesting applications, the need to optimize the global servicing path becomes crucial for maximizing efficiency and crop yield. In recent years, several methods of path generation have been researched, but very few have studied their applications on complex field shapes. In this study, a method of creating the optimal servicing path for simple and complex field shapes is proposed. The proposed algorithm creates subfields for a target land, optimizes the track direction for several subfields individually, merges subfields that result in overall increased efficiency, and finds the minimum non-operating paths to travel from subfield to subfield while selecting the respective optimal subfield starting locations. Additionally, it is required that this process must be done within 3 seconds to meet performance requirements. Results from 3 separate field shapes show that the field traversal efficiency can range from 68.0% to 94.4%, and the coverage ratio can range from 98.8% to 99.9% for several different conditions. In comparison with previous studies using the same field shape, the proposed methods demonstrate an increase of 5.5% in field traversal efficiency.

During the winter season, snowplowing has a significant effect on road users as it is critical to winter road maintenance and operations. The main goal of this study is to generate optimal routes for snowplowing trucks for efficient road maintenance. In addition to the conventional problem of reducing travel time and distance, this study also incorporates actual operational constraints, such as minimum maintenance standards and driver safety, to improve the overall efficiency of operations. To achieve the objectives, we first implemented the Chinese Postman Problem (CPP) to create Euler circuits from the initial routes and then identified the shortest paths by applying Dijkstra’s algorithm. Then, the Tabu search algorithm was chosen as a metaheuristic algorithm for the optimization process that finds near-optimal solutions by considering operational constraints for snowplow routes. Unsafe turning conditions and minimum maintenance standards were taken into account in the objective function defined for the optimization process. In simulations, the route obtained by our approach was compared to one with the application of CPP only in terms of travel distance, time, turning conditions, and road maintenance priority.

This article presents the sensing and safety algorithms for autonomous excavators operating on construction sites. Safety is a key concern for autonomous construction to reduce collisions and machinery damage. Taking this point into consideration, our study deals with LiDAR data processing that allows for object detection, motion tracking/prediction, and track management, as well as safety evaluation in terms of potential collision risk. In the safety algorithm developed in this study, potential collision risks can be evaluated based on information from excavator working areas, predicted states of detected objects, and calculated safety indices. Experiments were performed using a modified mini hydraulic excavator with Velodyne VLP-16 LiDAR. Experimental validations prove that the developed algorithms are capable of tracking objects, predicting their future states, and assessing the degree of collision risks with respect to distance and time. Hence, the proposed algorithms can be applied to diverse autonomous machines for safety enhancement.

This study focuses on the development and evaluation of advanced safety algorithms for excavators using virtual reality (VR). An excavator typically operates under a stationary state with its working parts rotating 360° in coordination with nearby workers. During excavation, a fatal accident can occur due to operator carelessness and work site blind spots. Accidents due to collisions with nearby workers have been increasing. Accordingly, we presented safety system algorithms to prevent collisions with surrounding objects and secure the maximum working area in this study. We also evaluated the performance of safety system algorithms using VR. For risk assessment, we calculated the predicted working area through a kinematic analysis of the excavator’s working parts and accordingly conducted target selection of risk factors. We used time-to-collision and warning indices as safety indices for the safety assessment of the selected target and divided the excavator’s working modes into three categories: Safe, warning, and emergency braking modes. Control inputs, such as alarms and braking, were appropriately defined for each working mode. Under warning mode, workers can avoid collisions because a safety system will alert them of dangerous situations through an alarm. Under emergency braking mode, an emergency braking input signal is dispatched with an alarm to automatically prevent collisions. The advanced safety algorithm proposed in the study was developed in MATLAB/Simulink environment. The VR application was developed using a physics engine. On the basis of this application, the performance evaluation of the safety system algorithms was conducted on the frequently occurring sticking scenario.

For transient analysis of structural dynamics with reduced order model (ROM), data recovery procedures that use modal methods such as the classical mode-acceleration (MA) and modal-displacement (MD) methods are an important step in order to increase the convergence and accuracy of the solution. In this work, we propose novel MA and MD methods for highly accurate transient analysis with a reduced order model based on enhanced Craig–Bampton (ECB) formulation, which is an extension of the classical Craig–Bampton (CB) method that is a way to reduce the size of a finite element (FE) model. The performance of the proposed data recovery approach is demonstrated with two numerical examples. We also investigate the relation between the proposed and classical MA and MD methods.

Excavation is one of the primary projects in the construction industry. Introducing various technologies for full automation of the excavation can be a solution to improve sensing and productivity that are the ongoing issues in this area. This paper covers three aspects of effective excavation progress monitoring that include excavation volume estimation, occlusion area detection, and 5D mapping. The excavation volume estimation component enables estimating the bucket volume and ground excavation volume. To achieve mapping of the hidden or occluded ground areas, integration of proprioceptive and exteroceptive sensing data was adopted. Finally, we proposed the idea of 5D mapping that provides the info of the excavated ground in terms of geometric space and material type/properties using a 3D ground map with LiDAR intensity and a ground resistive index. Through experimental validations with a mini excavator, the accuracy of the two different volume estimation methods was compared. Finally, a reconstructed map for occlusion areas and a 5D map were created using the bucket tip&rsquo, s trajectory and multiple sensory data with convolutional neural network techniques, respectively. The created 5D map would allow for the provision of extended ground information beyond a normal 3D ground map, which is indispensable to progress monitoring and control of autonomous excavation.

Although the LiDAR sensor provides high-resolution point cloud data, its performance degrades when exposed to dust environments, which may cause a failure in perception for robotics applications. To address this issue, our study designed an intensity-based filter that can remove dust particles from LiDAR data in two steps. In the first step, it identifies potential points that are likely to be dust by using intensity information. The second step involves analyzing the point density around selected points and removing them if they do not meet the threshold criterion. To test the proposed filter, we collected experimental data sets under the existence of dust and manually labeled them. Using these data, the de-dusting performance of the designed filter was evaluated and compared to several types of conventional filters. The proposed filter outperforms the conventional ones in achieving the best performance with the highest F1 score and removing dust without sacrificing the original surrounding data.

The Excavator is one of the key equipment utilized for earthmoving tasks at construction sites. This paper aims to provide an integrative tracking control strategy comprising of position, contour, and force tracking controls for excavation tasks. A non-linear proportional-integral controller was applied for position control of hydraulic actuators and the contour control strategy was added to create an optimal path of the bucket tip minimizing contour errors. The force control was finally considered to compensate for the ground resistive force. A multiphysics simulation model was developed for an evaluation of the designed controller’s performance through co-simulation. Experimental results obtained from a test platform show that the developed control algorithms provide good tracking performance for soil digging.

The study deals with the design of control algorithms to enhance the steering performance of all-terrain cranes. For this, its hydraulic steering system was modeled using a multiphysics simulation tool and the self-tuning fuzzy PID controller was developed to control hydraulic steering actuators with nonlinearities. The designed control algorithm was validated through co-simulation using a real-time simulator and showed satisfactory performance.

This study proposes an effective control strategy for autonomous excavation under complex ground conditions, by integrating position, contour, and force control that are mutually associated factors. For the position control strategy, a non-linear PI controller was devised to control the stroke of each hydraulic cylinder, and therefore the bucket tip's position. To compensate for the ground resistive forces in contact space, an impedance controller was designed. Finally, contour compensation was considered to generate an optimal path of the bucket tip for ground leveling tasks. The performance of developed control algorithms was evaluated in the case of ground leveling task through co-simulation in multi-physics domains. Simulation results show that the designed control scheme provides good results in terms of transient response and tracking accuracy by dealing with all the aspects of force, position, and contour compensation.

This paper proposes sensing and safety techniques for autonomous excavators working on construction sites. In autonomous construction equipment, safety is a significant concern to reduce fatal accidents and machinery damage. In taking up this point of view, this paper deals with object tracking, motion prediction, and track management for detected objects that can improve the safety function of autonomous construction equipment. By using the info of predicted motion of detected objects and actual working areas of an excavator, and calculating safety indices, the proposed safety algorithms can evaluate the degree of safety (i.e., potential collision risks).

This paper presents a rotational inertia estimation algorithm for excavators based on recursive least-squares with forgetting and an adaptive updating rule that uses the performance analysis of the Kalman filter. Generally, excavators execute a swing motion with various materials, and the rotational inertia of the excavator is changed greatly due to the excavator’s working posture. The large variation in the rotational inertia of the excavator has an influence on the dynamic behaviors of the excavator, and an estimation of the excavator’s rotational inertia is essential to developing a safety system based on prediction of dynamic behavior. Therefore, a real-time rotational inertia estimation algorithm has been proposed in this study using a swing dynamic model. The proposed estimation algorithm has been designed using only swing velocity, utilizing the recursive least squares method with multiple forgetting for practical application to actual excavators. Two updating rules have been applied to the estimation algorithm in order to enhance the estimation performance. The first proposed rule is the damping coefficient updating rule. The second rule is the forgetting factor updating rule based on real-time analysis of linear Kalman filter estimation performance. The performance evaluation of the estimation algorithm proposed in this paper has been conducted based on the excavator’s typical dumping scenario. The performance evaluation results show that the developed inertia estimation algorithm can estimate actual rotational inertia with the two designed updating rules using only excavator swing velocity.

This paper presents a laser scanner–based predictive safety system for excavators. Blind spots on excavators and operators’ carelessness cause majority of fatal accidents, such as those in which de...

This research deals with assist wheel control to improve the steering efficiency and dynamic stability of the multi-axle crane based on model predictive control. Since multi-axle crane has relatively high inertia and long distance between the axles, it has slow dynamic response, and thus, different steering strategies according to driving speed intervals are required. Specifically, the steering strategy is that the number of wheels fixed mechanically increases to secure dynamic stability as the driving speed increases. However, although this strategy enables to secure stability by slowing down the dynamic response, it also has a weakness to decrease the steering efficiency. If the steering efficiency is decreased, it may result in augmenting an accident rate due to an increase in driver’s fatigue. Therefore, this study suggests a new steering strategy to improve the steering efficiency by simultaneously guaranteeing dynamic stability. The suggested steering control algorithm can enhance both steering efficiency and stability by deriving an assist wheel control input based on model predictive control using a variable weighting factor derived from the driver’s steering input (steering angle on the first axle). Development and performance evaluation of the suggested algorithm were conducted in the MATLAB/Simulink environment, and evaluation results confirmed that the developed algorithm could both enhance the driver’s steering efficiency and secure dynamic stability.

This paper describes stability assist wheel control based on RLS estimation with forgetting in order to improve the dynamic stability of a multi-axle all-terrain crane. Existing multi-axle all-terrain cranes have greater mass and a longer distance between axles than ordinary vehicles, so their rotational inertia is very large. Large rotational inertia implies a slower dynamic response for yaw motion, and it is difficult to achieve the desired yaw motion within an expected amount of time. Therefore, ensuring yaw and lateral dynamic stability is an important theme in research regarding multi-axle all-terrain cranes. In this study, to ensure the crane’s dynamic stability, a simplified linear crane model of a multi-axle all-terrain crane was developed, in which an assist wheel was chosen according to speed, for control. To improve the driving stability, the chosen assistant wheel’s optimal steering angle was calculated through LQR, and to calculate the optimal feedback gain and steering angle, the rotational inertia and lateral velocity were estimated using recursive least square algorithms with forgetting. MATLAB/Simulink based simulations were used to evaluate the performance of the assist wheel controller for improving the crane’s dynamic stability, and the simulation results showed that the proposed stability assist wheel control method improved yaw and lateral dynamic stability over existing steering systems.

In a conventional steering system for a multi-axle crane, the steering angle of each axle is determined according to Ackermann’s steering principle, which minimizes the slip angles of the tires. The role of optimal steering control in improving a driver’s steering efficiency is hardly considered in Ackermann’s principle. To address this problem, this paper proposes a control strategy for determining the optimal steering angles for a multi-axle crane and thereby improving a driver’s steering efficiency by applying the model predictive control (MPC) algorithm and defining a driver’s intentions. A simplified crane model for the steering system was developed using a bicycle model, and a comparative study was carried out via simulation to analyze steering performance for the conventional (Ackermann) and proposed steering control systems for the cases of all-wheel steering and road steering modes. The simulation results show that both the minimum turning radius and the driver’s steering effort are decreased more by the proposed steering control system than by conventional system and that the proposed control strategy therefore yields better steering performance.

All-terrain cranes with multi-axles have large inertia and long distances between the axles that lead to a slower dynamic response than normal vehicles. This has a significant effect on the dynamic behavior and steering performance of the crane. Therefore, the purpose of this study is to develop an optimal steering control algorithm with a reduced driver steering effort for an all-terrain crane and to evaluate the performance of the algorithm. For this, a model predictive control technique was applied to an all-terrain crane, and a steering control algorithm for the crane was proposed that could reduce the driver’s steering effort. The steering performances of the existing steering system and the steering system applied with the newly developed algorithm were compared using MATLAB/Simulink and ADAMS with a human driver model for reasonable performance evaluation. The simulation was performed with both a double lane change scenario and a curved-path scenario that are expected to happen in road-steering mode.

This paper presents an online tuning rule-based adaptive control algorithm to control DC motor speed. For this, a method of recursive least squares with forgetting was proposed to approximate the relation between single input (voltage) and single output (angular speed) of the DC motor system as a first order differential equation. Using this approximated first order system and Lyapunov method-based disturbance observer with an online turning rule, a voltage input of the DC motor was generated to track a desired value of rotational speed of the motor. A performance evaluation of the proposed algorithm was conducted in the MATLAB/Simulink environment. The results show that the designed algorithm enables to track a reference speed successfully using only a single input and a single output of the DC motor system.

This study aims to establish a test method to obtain the dynamic characteristics of hydraulic-pneumatic semi-active suspensions used in tractor cabins. Because dynamic characteristics are utilized in simulation models for developing suspension control logic and must be secured to improve control performance, an accurate test method must be established. The dynamic characteristics of the suspension, i.e., the spring constant and damping coefficient, were obtained by changing the current and velocity conditions. An exciter was used as a test device to control the displacement and velocity of the hydraulic cylinder. In order to derive the spring constant of the suspension, a low-speed reciprocating motion test was performed to obtain the force-displacement diagram and to derive the damping coefficient; 48 tests were performed under 6 velocity conditions and 8 current conditions to obtain a force-velocity diagram for each result. The spring constant of the suspension was confirmed using the slope of the trend line in the force-displacement diagram obtained through the low-speed reciprocating motion test of the suspension. In addition, the damping coefficient was calculated using the force-velocity diagram obtained through the reciprocating motion test of the suspension under various current and velocity conditions.

This paper presents gear ratio and shift schedule optimization strategies to improve energy efficiency for the dynamic simulation of a wheel loader equipped with dual clutch transmission (DCT) and automated manual transmission (AMT). A conventional wheel loader uses torque converter (T/C) based automotive transmission, and the torque converter causes heavy energy loss during the V-pattern working cycle. To improve fuel economy while maintaining working performance in the V-pattern working cycle, automated manual transmission (AMT) and dual clutch transmission (DCT) have been suggested to substitute the clutch for the torque converter. In addition, the optimization strategies for the gear ratio and shift schedule for AMT and DCT have been proposed for improving fuel economy. Gear ratios have been determined by a nonlinear optimization method based on the standard V-pattern working cycle which is obtained from experimental test data by a skilled driver. Then, the gear-shift schedule for clutch-type transmission has been derived by using a determined gear ratio and optimization strategy. Simulations have been conducted to investigate working performance and energy efficiency by using three developed wheel loader simulation models equipped with T/C, AMT, and DCT, respectively, with the driver model for the V-pattern working cycle. Simulation results show that AMT- and DCT-based wheel loaders are more fuel efficient for the V-pattern working cycle than the T/C-based wheel loader.

This paper describes an investigation of energy efficiency by applying an advanced powertrain system in a conventional wheel loader. A conventional powertrain of a wheel loader consists of an engine, torque converter and transmission. A torque converter in a conventional system generally causes a significant amount of energy loss, as determined by analyzing energy flow based on V-pattern working. To prevent energy loss in a torque converter, Automated manual transmission (AMT) was proposed and modeled in this paper as an advanced powertrain. A wheel loader based on AMT does not need to use a torque converter since the single clutch system is used between the engine and transmission with subsystems of engine controller, clutch actuator and controller. A simplified single clutch system and controller were constructed for V-pattern working of a wheel loader. Additionally, a PI-controller was used as a control algorithm for engine speed control to prevent energy loss while the clutch is not engaged. All simulation models have been constructed in the Matlab/ Simulink environment, and simulation studies were conducted by using a simulation model of a wheel loader with a driver model based on V-pattern working. Simulation results of the AMT-based wheel loader were analyzed by comparison with the results of the torque converter-based wheel loader, and the results show that the AMT-based wheel loader is more energy efficient than the conventional wheel loader.

The drifter is one of the main components that play a significant role in the percussion capability of the rock drill. The authors of the paper identified the operating mechanism of the drifter and developed an analysis model for the purpose of improving the percussion capability of the rock drill. The authors of this paper also validated the reliability of the analysis model through a comparative study between the analysis results and the results of the experimental tests and utilized the validation results to understand the main factors wielding effects on blow frequency and impact energy.

An electro hydraulic poppet valve (EHPV) and a variable orifice poppet are assembled in a single block, which is referred to as a RHINO but is also generally called a pilot-operated flow control valve. In this study, we analyzed the structure and the operating principle for a RHINO applied in a 21-ton electric excavator system. The RHINO was experimentally tested to measure the dynamic responses and the pressure energy loss. In this test, we investigated the variation in the conductance coefficient according to the increase in the supply pressure under a constant current and a variation in the flow rate according to the increase in the current. Then, the geometrical shapes and the spring stiffness of the RHINO were considered to develop an analysis model. The characteristics (current-force and hysteresis) for the solenoid based on the experimental data were reflected in the analysis model that was developed, and the reliability of the analysis model was also verified by comparing the experimental and analytical results. The developed model is thus considered to be reliable for use in a wide range of applications, including optimum design, sensitivity analysis, parameter tuning, etc.

We applied an independent metering valve to excavator hydraulic systems in order to verify its effect on energy reduction in excavators through flow regeneration. The structure and modes of independent metering valve were introduced. Then, an excavator hydraulic system was modeled with independent metering valve configuration. Simulations compared power consumption of pump between excavator hydraulic systems with a conventional main control valve (MCV) and independent metering valve configuration (IMV). Through this comparative study, the IMV’s effectiveness on energy saving in the excavator system was analyzed.

This paper describes an adaptive position control algorithm of a DC motor based on the first order approximation using recursive least squares with forgetting. The main concept of the proposed control algorithm is that the relationship between single-input and single-output of the system can be approximated as a first order system in a particular time region. The approximated first order system can be used for optimal control instead of actual complicated DC motor dynamics. In order to approximate the input and output relationship of the system, the method of recursive least squares with forgetting has been used. The disturbance observer has been employed to estimate the error between actual DC motor system and approximated first order system. Based on the approximated first order system that represents the DC motor dynamics, the optimal voltage input has been computed using the Lyapunov direct method. The actual DC motor system equipped with encoder has been used for performance evaluation of the proposed control algorithm. The results show good control performance in terms of error convergence and adaption ability.

Drifter is equipment which is hard to localize. Performance of prototype hasn't performed well compared to product of leading companies even though advanced foreign firm's product were dead copied. This study shows cases of approaching the factor which produces performance gap through drifter hydraulic analysis model which is core component of rock drill. Progression of procedure is following. 1) Securing reliability of the analysis model by comparing impact test result with analysis result. 2) Drawing a graph which indicates performance gap between prototype and drifter of advanced foreign firm by using analysis model. 3) Approaching the factor which produces performance gap with analysing variable of the analysis model. Software used for this analysis is SimulationX. Key words : Rock drill, Drifter, Surface roughness, Damper, Impact frequency, Impact energy요 약드리프터는 국산화개발에 어려움을 겪고 있는 장비이며 , 외국선진사의 제품을 데드카피 하여도 시제품의 성능이 뒤떨어지는 현상이 발생하여 왔다 . 본 논문은 천공기의 핵심부품인 드리프터의 해석모델을 활용하여 성능격차를 유발하는 요소를 분석하는 사례를 보여준다 . 진행절차는 다음과 같다 . 우선, 타격시험을 수행하여 해석모델의 신뢰성을 확보한다 . 그리고 해석모델을 활용하여 외국선진사 제품과 시제품의 성능비교그래프를 도출한다 . 마지막으로 해석모델의 변수를 분석하여 성능격차를 유발하는 인자에 접근한다 . 사용된 소프트웨어는 독일 ITI사에서 개발된 SimulationX이다.주요어 : 천공기, 드리프터, 표면조도, 댐퍼, 타격주파수, 타격에너지

The goal of this study drifter is to understand the operating mechanism of a drifter and to suggest a reliable analysis model which can be used for evaluating the drifter`s performance from the viewpoint of impact frequency and energy. For this, the working principle of drifter and functions of its main components were analyzed, and a simulation model was developed based on the analysis. The model was validated using experimental tests on a test-bench. A comparative study of simulation and experimental results indicated that the suggested model accurately represents the real drifter system in terms of impact frequency and impact energy per blow.

The thermal control of a die is crucial for the development of high efficiency injection moulds. For an effective thermal management, this research provides a strategy to identify a thermal dynamic model and to design a controller. The neural network techniques and finite element analysis enable modeling to deal with various cycle-times for moulding process and uncertain dynamics of a die. Based on the system identification which is experimentally validated using a real system, controllers are designed using fuzzy-logic and self-tuning PID methods with backpropagation and radial basis function neural networks to tune control parameters. Through a comparative study, each controller’s performance is verified in terms of response time and tracking accuracy under different moulding processes with multiple cycle-times.