MATLAB Simulation of Autonomous Servo Driven Oil-Hydraulic Power Unit

Servo driven hydraulic power units have been implemented in some sectors of industry in order to counteract rising energy costs and reduce our ecological footprint. The advantages associated with the use of these technologies has motivated us to research a new control approach that allows its use independently, with reduced implementation costs and high efficiency. This investigation develops new solutions to concurrently implement and improve volumetric control methodology for oil-hydraulic power units, which aims to produce and provide strictly necessary hydraulic power to the actuators. The approach used is based on a balance of flows present in a hydraulic circuit, reducing the pressure ripple generated by the pumps, valves and actuators, using a hydraulic accumulator. The work begins with the mathematical modeling of a volumetric oil-hydraulic power unit, designed to demonstrate the concepts of the project, its components and the associated advantages. The definitions of the models presented are intended to exemplify the new control strategy and infer about the possibilities that arise from the use of this new methodology for power oil-hydraulic units. In order to carry out the research and conclude about the results of the simulations, two simulations were performed using MATLAB Simulink software for two distinct hydraulic circuits and their control strategy: resistive control and volume control with the use of a servo motor. In the resistive control, an internal gear pump driven by an induction motor with constant speed uses a pressure regulating valve to derive the excess of the flow to the reservoir. Despite their low efficiency, this type of assembly has very low costs and has a very good dynamic compared with traditional volumetric drive systems, avoiding the use of dedicated engineering. The volumetric control makes use of an internal gear pump (to allow direct comparisons with the resistive control method), a servo motor, a hydraulic accumulator and a directional valve which prevent the flow from de accumulator draining into the reservoir during the downtimes. The controller allows you to establish a direct relationship between the accumulator volume and pressure of the hydraulic circuit. The control methodology discussed throughout this work reveals an alternative volumetric control solution to consider, whether in new equipment or in retrofitting even with the different objectives of existing technologies available in the market. The simulations allow us to conclude on energy-saving and environmental advantages of the volumetric control system presented, comparing it with existing systems on the market.

Functional Models With Inherent Behavior: Towards a Framework for Safety Analysis Early in the Design of Complex Systems

This paper represents a step toward a more complete frame-work of safety analysis early in the design process, specifically during functional modeling. This would be especially useful when designing in a new domain, where many functions have yet to be solved, or for a problem where the functional architecture space is large. In order to effectively analyze the inherent safety of a design only described by its functions and flows, we require some way to simulate it. As an already-available function failure reasoning tool, Function Failure Identification and Propagation (FFIP) utilizes two distinct system models: a behavioral model, and a functional model. The behavioral model simulates system component behavior, and FFIP maps specific component behaviors to functions in the functional model. We have created a new function-failure reasoning method which generalizes failure behavior directly to functions, by which the engineer can create functional models to simulate the functional failure propagations a system may experience early in the design process without a separate behavioral model. We give each basis-defined function-flow element a pre-defined behavior consisting of nominal and failure operational modes, and the resultant effect each mode has on its functions connected flows. Flows are represented by a two-variable object reminiscent of a bond from bond graphs: the state of each flow is represented by an effort variable and a flow-rate variable. The functional model may be thought of as a bond graph where each functional element is a state machine. Users can quickly describe functional models with consistent behavior by constructing their models as Python NetworkX graph objects, so that they may quickly model multiple functional architectures of their proposed system. We are implementing the method in Python to be used in conjunction with other function-failure analysis tools. We also introduce a new method for the inclusion of time in a state machine model, so that dynamic systems may be modeled as fast-evaluating state machines. State machines have no inherent representation of time, while physics-based models simulate along repetitive time steps. We use a more middle-ground pseudo time approach. State transitions may impose a time delay once all of their connected flow conditions are met. Once the entire system model has reached steady state in a timeless sense, the clock is advanced all at once to the first time at which a reported delay is ended. Simulation then resumes in the timeless sense. We seek to demonstrate this modeling method on an electrical power system functional model used in previous FFIP studies, in order to compare the failure scenario results of an exhaustive fault combination experiment with similar results using the FFIP method.

Geometry Design and Contact Ratio Analysis for Circular Arc Parallel-Axis Helical Gears

Based on space curve meshing theory, in this paper, we present a novel geometric design of a circular arc helical gear mechanism for parallel transmission with convex-concave circular arc profiles. The parameter equations describing the contact curves for both the driving gear and the driven gear were deduced from the space curve meshing equations, and parameter equations for calculating the convex-concave circular arc profiles were established both for internal meshing and external meshing. Furthermore, a formula for the contact ratio was deduced, and the impact factors influencing the contact ratio are discussed. Using the deduced equations, several numerical examples were considered to validate the contact ratio equation. The circular arc helical gear mechanism investigated in this study showed a high gear transmission performance when considering practical applications, such as a pure rolling process, a high contact ratio, and a large comprehensive strength.

Acquiring Embodied Knowledge Through Practice: A Wisdom Engineering Approach

Although remarkable progress has been made in the field of explicit knowledge, research about tacit knowledge is still very few. This paper takes up embodied knowledge such as bicycle riding, as one kind of tacit knowledge. As embodied knowledge cannot be articulated and verbalized, it has to be transferred to another person through practice. But how we can acquire embodied knowledge more effectively through practice is still the question at issue. Indeed, there are works to help a learner to acquire embodied knowledge by showing the videos or through OJT. But since features or control points are not explicit, it is very difficult for a learner to acquire a good sense for judgments and for decisions to cope with the changing situations. Although there are many approaches to multivariate analysis, there are very few approaches which provide a holistic perspective. In this sense, pattern-based approach is better than other approaches. This paper points out that pattern-based Recognition Taguchi (RT) approach in Mahalanobis Taguchi System (MTS) is expected to be a very promising and versatile tool to help a learner acquire embodied knowledge because it allows us to take the differences of body behavior from person to person in addition to providing the holistic perspective.

A Methodological Approach to Support the Design of Induction Hobs

Nowadays in many industrial applications, i.e. electrical household appliances, it is necessary to have a robust and safe control for some variables involved in the analysis of the performances of different products. In addition, the recent eco-design directives require products increasingly eco-friendly and eco-efficient, preserving high-performance but a low power consumption. For these reasons, the physical prototypes of products require many expensive and complex tests in term of time, resources and qualified personnel involved. To overcome these limitations, the proposed approach is focused on the use of virtual prototyping tools, which support and reduce the expensive physical experiments. The main objective of this paper is the development, implementation and testing of an innovative methodology, which could be an improvement for the sustainable design of induction hobs. Induction heating applied to the domestic cooking has significantly evolved since the first cooking hobs appeared. Different issues such as maximum power available for heating a pot, dimensional compactness of the hobs, or inverter electronics efficiency have achieved a great development. The proposed methodology provides the development of a multi-physic model which is able to estimate the efficiency of the induction hobs starting from the design data of the project. In particular, the multi-physic model is composed by an electromagnetic simulation and a thermal simulation. The electromagnetic simulation, starting from electrical values such as voltage, current and frequency, is able to simulate the eddy current induced in the bottom of the pot, and resistance leads to the Joulean heating of the material. The thermal simulation is able to measure the energy consumption during the operational phase and the temperature reached by the materials. Therefore, the thermal power obtained by the Joulean heating is, at the same time, the output of the electromagnetic simulation and the input of the thermal one. The proposed model can be applied to design product and simulate the performance considering different operating conditions such as different types of cookers, different coils and different materials. Through the use of virtual prototyping tools is possible to control the heat flux on the whole system (stove, pot, water), and to evaluate the energy efficiency during the operational phase. The proposed tool makes the product-engineer more aware about decision-making strategies in order to achieve an energy saving, calculated over the whole life cycle.

The Reduced Tolerance Allocation Problem

Tolerance allocation is a necessary and important step in product design and development. It involves the assignment of tolerances to different dimensions such that the manufacturing cost is minimum, while maintaining the tolerance stack-up conditions satisfied. Considering the design functional requirements, manufacturing processes, and dimensional and/or geometrical tolerances, the tolerance allocation problem requires intensive computational effort and time. An approach is proposed to reduce the size of the tolerance allocation problem using design of experiments (DOE). Instead of solving the optimization problem for all dimensional tolerances, it is solved for the significant dimensions only and the insignificant dimensional tolerances are set at lower control levels. A Genetic Algorithm is developed and employed to optimize the synthesis problem. A set of benchmark problems are used to test the proposed approach, and results are compared with some standard problems in literature.

A Fringe Signal Processing Method With Multi-Sample Zero-Crossing Detection Based on DSP and its Application in Absolute Gravimeters

The absolute gravitation acceleration (g) is generally measured by observation of a free-falling test mass in a vacuum chamber based on laser interference. Usually the free-falling object trajectory is obtained by timing the zero-crossings of the interference fringe signal. A traditional way to time the zero-crossings is electronic counting method, of which the resolution is limited in principle. In this paper, a fringe signal processing method with multi-sample zero-crossing detection based on Digital Signal Processor (DSP) is proposed and realized for the application in absolute gravimeters. The principle and design of the fringe signal processing method are introduced. The measuring precision is evaluated both theoretically and from numerical software simulations with MATLAB®, and verified by hardware simulated free-falling experiments. The results show that the absolute error of the gravity acceleration measurement introduced by the fringe signal processing method is less than 0.5 μGal (1 μGal = 1×10−8 m/s2), and the impact on the standard deviation is about 2 μGal. This method can effectively reduce the systematic error of the traditional electronic counting method, and satisfy the requirements for precision and portability, especially for field ready absolute gravimeters.

Design of Experiments to Support Automated Assembly Planning

Assembly time estimation is a key factor in evaluating the performance of the assembly process. The overall goal of this study is to develop an efficient assembly time estimation method by generating the prediction model from an experimental design. This paper proposes a way to divide an assembly operation into four actions which consist of a) part movement, b) part installation, c) secure operations, and d) subassembly rotations. The focus of this paper is to design a time estimation model for the secure operation. To model secure times, a design of experiments is applied to collect experimental data based on the physical assembly experiments performed on products that are representative of common assembly processes. The Box-Behnken design (BBD) is an experiment design to support response surface methodology to interpret and estimate a prediction model for the securing operations. The goal is to use a quadratic model, which contains squared terms and variable interactions, to study the effects of different engineering parameters of securing time. The experiment is focused on individual-operator assembly operations. Various participants perform the experiment on representative product types, including a chainsaw, a lawn mower engine, and an airplane seat. In order to optimize the assembly time with different influence factors, mathematical models were estimated by applying the stepwise regression method in MATLAB. The second-order equations representing the securing time are expressed as functions with six input parameters. The models are trained by using all combinations of required data by the BBD method and predict the hold back data within a 95% confidence interval. Overall, the results indicate that the predicted value found was in good agreement with experimental data, with an Adjusted R-Squared value of 0.769 for estimated securing time. This study also shows that the BBD could be efficiently applied for the assembly time modeling, and provides an economical way to build an assembly time model with a minimum numbers of experiments.

CAD Integration in Virtual Reality Design Reviews for Improved Engineering Model Interaction

Leveraging virtual reality (VR) technology to enhance engineering design reviews has been an area of significant interest for researchers since the advent of modern VR. The ability to interact meaningfully with 3D engineering models in these VR design reviews is an important, though often neglected, capability due to the difficulty of performing data translation between native CAD data and VR compatible file formats. An automated synchronization interface was developed between a VR design review environment and a commercial CAD package that stream-lines the data translation process and enables enhanced visualization and manipulation tools. User experiments were performed to explore the hypothesis that allowing users to perform CAD-like view transformations and geometric manipulations in VR design reviews improves design understanding and decision making. Analysis of the experiment results show that enhanced interaction tools provide statistically significant advantages over a baseline VR design review environment for complex 3D models.