Paul P. Lin

Asymptotic Boundary-Layer Solutions for Mixed Convection from a Vertical Surface in a Micropolar Fluid

Abstract Using the theory of micropolar fluids due to Eringen, asymptotic boundary layer solutions are presented to study the combined convection from a vertical semi-infinite plate to a micropolar fluid. Consideration is given to the region close to the leading edge as well as the region far away from the leading edge. Numerical results are obtained for the velocity, angular velocity and temperature distribution. The missing wall values of the velocity, angular velocity and thermal functions are tabulated. Micropolar fluids display drag reduction and reduced surface heat transfer rate when compared to Newtonian fluids.

On-Line Free Form Surface Measurement Via a Fuzzy-Logic Controlled Scanning Probe

This paper presents a system and methodology for on-line free form surface measurement via a scanning contact probe installed on a CNC (computer numerical control) machine. The scanning probe provides more sampling points than any traditional touch trigger type of probes used on CNC machines, and better measuring accuracy than laser displacement sensing or structured lighting. The presented measuring systems main advantage is that the number of measured points can vary with the change of surface curvature. To improve the measuring stability and continuity, fuzzy logic control, in lieu of traditional PID control, is employed. As a result, the system is capable of continuously detecting the boundaries of a measured object and measuring a relatively large complex surface. Based on the experimental results, the measuring accuracy is estimated between 20 and 30 μm (micrometers). In addition to surface measurement, the reconstructed surface data can be fed to a CAD/CAM system for component making or reproduction, which makes the reverse engineering of models comprised of free form surfaces readily accessible.

Tolerance specification of robot kinematic parameters using an experimental design technique―the Taguchi method

Abstract This paper presents the tolerance specification of robot kinematic parameters using the Taguchi method. The concept of employing inner and outer orthogonal arrays to identify the significant parameters and select the optimal tolerance range for each parameter is proposed. The performance measure based on signal-to-noise ratios (S/N) using the Taguchi method is validated by Monte Carlo simulations. Finally, a step-by-step tolerance specification methodology is developed and illustrated with a planar two-link manipulator and a five-degree-of-freedom Rhino robot.

An improved method for on‐line calculation and compensation of the static deflection at a robot end‐effector

Traditionally, robotic deflection analysis for a low-weight robot has been performed based on an assumption that each link is treated as a cantilever beam, which leads to no angular deflection at a joint. In practice, a robotic intermediate joint is linearly and angularly deflected when a load is applied at the end-effector. It is found in this study that the additional link deflection resulting from the angular deflection of a robotic revolute joint substantially contributes to the end-effectors total deflection. This article presents an improved method via a combination of classical beam theory, energy methods and the concepts of differential relationships to more accurately calculate the static deflection at the end-effector. A systematic approach to deflection calculation through three different Jacobians are presented. The study also shows that the end-effectors deflection heavily depends on robotic arm configurations. The deflection is then compensated based on the selected optimum configuration. The theoretical deflection analysis is verified by experimental results. A planar two-link robot and a six-degree-of-freedom Elbow Manipulator are used for numerical illustration and calculation procedure.

Fault Diagnosis, Prognosis and Self-Reconfiguration for Nonlinear Dynamic Systems Using Soft Computing Techniques

Diagnosis and prognosis are processes of assessment of a systems health. The former is an assessment about the current health of a system based on observed symptoms, while the latter is an assessment of the future health. System reconfiguration or accommodation is essential once faults are detected and identified. This paper presents model-free fault diagnosis, prognosis and self-reconfiguration using soft computing techniques (i.e. neural networks, fuzzy logic and Taguchi methods). A three-tank nonlinear dynamic system was chosen to demonstrate the presented techniques. The diagnosis detects and classifies a fault, and also determines the degree of each fault. The prognosis estimates the remaining life at any time. While a fault cannot be repaired by itself, the faulty system can be quickly reconfigured to avoid reaching the catastrophic level. A fast self-reconfiguration technique by means of instant optimization and fuzzy inference is presented.

Intelligent model-free diagnosis for multiple faults in a nonlinear dynamic system

In terms of fault diagnosis, there are two general approaches: model-based and model-free. This paper presents the fault diagnosis techniques for a nonlinear dynamic system with multiple faults using the model-free approach. A new concept for fault detection by means of a real-time tracker was employed to predict the system outputs from which the residuals could be quickly generated. To classify faults and determine the degree of each fault, soft computing techniques: fuzzy logic and neural network were used. This study consists of three parts: diagnosis of single faults before the system reaches its steady state, diagnosis of simultaneous multiple faults and diagnosis of sequential multiple faults. A three-tank nonlinear dynamic system was chosen to demonstrate the presented techniques. The result showed promise in using the model-free approach for the diagnosis of multiple faults.

Fast Multidisciplinary Design Optimization via Taguchi Methods and Soft Computing

It is difficult to perform multidisciplinary design optimization using traditional searchbased optimization techniques due to possible conflicts among objectives from different disciplines,thetimeconsumingsearch,andthepossibilityofdivergence.Toovercomethedifficulties, this paper presents simulation-based design optimization techniques using Taguchi methods and soft computing (i.e. fuzzy logic and neural networks). An aircraft engine cycle design optimization with four conflicting design objectives is used to validate the presented approach. The result shows significant performance improvement in optimizing single and multiple design objectives. HE emerging field of Multidisciplinary Design Optimization (MDO) seeks to improve design methodology to rapidly and efficiently explore multiple-dimension design spaces with the goal of increasing system performance significantly, thereby reducing end-product cost substantially. Search-based and simulation-based are the two major system design approaches. The former is traditional and mathematical, and has existed for a long time. The optimum solution has to do with the selected starting point, and the optimization method used. A possibility of divergence in solution seeking is a major drawback in this approach. In contrast, the simulation-based approach uses the analysis and evaluation of a candidate solution, and the assessment of the degree to which the candidate satisfies the requirement. This optimum design tool uses the simulation-based approach. 1 With this new approach, the optimum solution can be obtained in real time. The traditional search-based optimization is a typical example of hard computing. In hard computing, the prime desiderata are precision, certainty and vigor. In contrast, in soft computing the principal notion is that precision and certainty carry a cost; and that computation, reasoning, and decision-making should exploit (whenever possible) the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth for obtaining low cost solutions. Fuzzy logic and neural networks, the two major soft computing techniques, have very contrasting application requirements. Fuzzy systems are appropriate if sufficient expert knowledge about the process is available, while neural systems are useful if sufficient data are available or measurable. Furthermore, neural networks possess the ability to learn the input-output relationship. A trained neural network provides instantly input-to-output mapping with reasonably good accuracy, but without knowledge representation. Fuzzy logic, on the other hand, possesses the ability for knowledge representation and inference, but has no capabilities for automated learning. Thus, fuzzy logic and neural networks compensate each other in terms of information processing.

Intelligent observer-based road surface condition detection and identification

Road surface condition is greatly dependent on the surfaces friction coefficient. The abrupt change of the coefficient results in variation of wheel slip which likely leads to vehicle instability. Although the vehicle on-board sensors can measure the vehicles velocities and yaw rate, the measurements, often containing noise and drift, are limited to the surface that the vehicle is engaged. In contrast, an effective observer can be used to estimate the vehicle dynamics for all possible surface conditions. This paper proposes a new observer, called extended state observer (ESO) to estimate the three quantities, and more importantly an additional quantity known as system dynamics. With the aid of the ESO, the following three tasks are performed: (1) noise filtering from the measurement data (2) detection and classification of surface condition change, and (3) identification of the road surface. Fuzzy logic was employed to quickly detect the change of road surface condition and further classify the surface; a neural network was employed to help determine the friction coefficient. The dynamic model used in this study can be applied to four-wheel independent drive vehicles. The presented methods were simulated when a vehicle encountered a significant change from a uniform-mu (i.e. uniform friction coefficient) surface to a split-mu surface (i.e. different friction coefficient on each side of the wheels) during cornering.

Development of a position and force sensor for robotic applications

A position/force sensor is developed to determine the position, distribution and magnitude of the load applied by an object on the sensor. For robotic applications, the sensor can be attached to grippers to aid in determining the relative pose between object and gripper, as well as in determining three forces and three moments. The sensor is designed to function as a tactile sensor and a robot wrist force sensor. Theory of plates is employed for sensor design. Strains are measured from direct contact to provide more precise position and force information. Control algorithns for robot hand motion during assembly are developed from the study and understanding of parts mating.

Optimized multidisciplinary system design for aircraft and propulsion systems

The optimized system design methodology for multiple disciplines is presented. Taguchi techniques, fuzzy logic and neural networks are combined to develop a user-friendly design tool, which can perform fastoptimized multidisciplinary system designs and analyses. Taguchi techniques are used to identify the input factors or parameters that are significant to the output of a system. From the Taguchi analysis, parameter significance index is obtained. This index is then used in a fuzzy inference system to find the optimum solution with multiple design objectives. Then, a compromised optimum solution is obtained at the multidisciplinary system level where the design objectives of the system as a whole are considered. Neural Networks are built to provide fast mappings for analysis and design. The application of this design methodology to aircraft and propulsion systems show significant improvement in optimizing the design objectives, and in predicting the system output in real time within reasonable accuracy.