Updating Finite Element Model of Combined Structures on the Basis of Dynamic Test Results

According to the real situation, a new method of updating the finite element model (FEM) of a combined structure step by step is proposed in this paper. It is assumed that there are two types of error when establishing the FEMs. One of them results from the simplifications, in fact, it is severe for complicated structures, which usually assume many simplifications; the other is from the process of identifying structural joint parameters. For this reason, it is recommended that the FEM should be established in two stages. At the first stage, the local physical parameters relating with the simplifications are corrected by using the dynamic test data of the corresponding substructures. Then, the structural joint parameters that link the substructures are corrected by the dynamic test data of the combined structure as a whole. The updating formula is presented and proved, and its algorithm is also described. And the experimental results show that the efficiency and accuracy of the proposed method are quite satisfactory.

Modelling and Analysis of a Grout Delivery Mechanism in the Remote Spray Lining of Shafts

A thorough analysis on the characteristics of a grout delivery mechanism in the lining of shafts has been accomplished. The dynamic equation of this spraying mechanism has been established and can describe the system’s performance properties under different conditions of viscous friction forces. The analysis introduces a combined viscous damping coefficient c* and a ratio λ between viscous friction force and inertia force. It is proved theoretically that the relative velocity of the grout is less than the implicate velocity and the emission angle α described in the paper is always larger than 45 °. Numerical simulations are performed by feeding various different parameters into the model. A full discussion of the effects of different variables is presented. Additionally, a formula for calculating the driving torque and power is developed. These studies provide an understanding of the properties of this mechanism and should prove useful in guiding its design and operation.

Foraging-Directed Adaptive Linear Programming: An Algorithm for Solving Nonlinear Mixed Discrete/Continuous Design Problems

Design models often contain a combination of discrete, integer, and continuous variables. Previously, the Adaptive Linear Programming (ALP) Algorithm, which is based on sequential linearization, has been used to solve design models composed of continuous and Boolean variables. In this paper, we extend the ALP Algorithm using a discrete heuristic based on the analogy of an animal foraging for food. This algorithm for mixed discrete/continuous design problems integrates ALP and the foraging search and is called Foraging-directed Adaptive Linear Programming (FALP). Two design studies are presented to illustrate the effectiveness and behavior of the algorithm.

Direct Differentiation Methods for the Design Sensitivity of Multibody Dynamic Systems

Methods for formulating the first-order design sensitivity of multibody systems by direct differentiation are presented. These types of systems, when formulated by Euler-Lagrange techniques, are representable using differential-algebraic equations (DAE). The sensitivity analysis methods presented also result in systems of DAE’s which can be solved using standard techniques. Problems with previous direct differentiation sensitivity analysis derivations are highlighted, since they do not result in valid systems of DAE’s. This is shown using the simple pendulum example, which can be analyzed in both ODE and DAE form. Finally, a slider-crank example is used to show application of the method to mechanism analysis.

Hider: A Methodology for Early-Stage Exploration of Design Space

This paper describes HIDER, a methodology that enables detailed simulation models to be used during the early stages of system design. HIDER uses a machine learning approach to form abstract models from the detailed models. The abstract models are used for multiple-objective optimization to obtain sets of non-dominated designs. The tradeoffs between design and performance attributes in the non-dominated sets are used to interactively refine the design space. A prototype design tool has been developed to assist the designer in easily forming abstract models, flexibly defining optimization problems, and interactively exploring and refining the design space. To demonstrate the practical applicability of this approach, the paper presents results from the application of HIDER to the system-level design of a wheel loader. In this demonstration, complex simulation models for cycle time evaluation and stability analysis are used together for early-stage exploration of design space.

Quantifying Design and Manufacturing Robustness Through Stochastic Optimization Techniques

Critical design decisions are often made during the detailed design stage assuming known material and process behavior. However, in net shape manufacturing processes such as stamping, injection molding, and metals casting, the final part properties depend upon the specific tool geometry, material properties, and process dynamics encountered during production. As such, the end-use performance can not be accurately known in the detailed design stage. Moreover, slight random variations during manufacture can inadvertently result in inferior or unacceptable product performance and reduced production yields. These characteristics make it difficult for the designer to select the tooling, material, and processing details which will deliver the desired functional properties, let alone achieve a robust design which is tolerant to process variation. This paper describes a methodology for assessing the design/manufacturing robustness of candidate designs at the detailed design stage. In the design evaluation, the fundamental sources of variation are explicitly modeled and the effects conveyed through the manufacturing process to predict the distribution of end-use part properties. This is accomplished by utilizing optimization of manufacturing process variables within Monte Carlo simulation of stochastic process variation, which effectively parallels the industry practice of tuning and optimizing the process once the tool reaches the production floor. The resulting estimates can be used to evaluate the robustness of the candidate design relative to the product requirements and provide guidance for design and process modifications before tool steel is cut, as demonstrated by the application of the methodology for dimensional control of injection molded parts.

Optimization of Deep Drawing Processes Using Statistical Design of Experiments

For a deep drawing process some important controllable variables (factors) upon the maximum drawing force are analyzed to find a setting adjustment for these process factors that provides a very low force for the metal forming process. For this investigation an orthogonal array L18 with three-fold replication is used. To find the optimum of the process, the experimental results are analyzed in accordance with the robust-design-method according to Taguchi (Liesegang et. al., 1990). For this purpose, so-called Signal-to-Noise-ratios are calculated. The analysis of variance for this S/N-ratios leads to a mathematical model for the deep drawing process. This model allows to find the pressumed optimal settings of the investigated factors. In the following, a confirmation experiment is carried out by using these optimal settings. The maximum drawing force of the confirmation experiment does not correspond with the confidence interval, which was calculated by analysis of variance techniques. So the predicted optimum of the process does not lead to a metal forming process with very low deep drawing force. The comparison with a full factorial plan shows that there are interactions between the investigated factors. These interactions could not be discovered by the used orthogonal array. Thus the established mathematical model does not describe the relation between the factors and deep drawing force in accordance with the practical deep drawing conditions.

Parametric Kinematic Tolerance Analysis of Planar Pairs With Multiple Contacts

We present an efficient algorithm for worst-case limit kinematic tolerance analysis of planar kinematic pairs with multiple contacts. The algorithm extends computer-aided kinematic tolerance analysis from mechanisms in which parts interact through permanent contacts to mechanisms in which different parts or part features interact at different stages of the work cycle. Given a parametric model of a pair and the range of variation of the parameters, it constructs parametric kinematic models for the contacts, computes the configurations in which each contact occurs, and derives the sensitivity of the kinematic variation to the parameters. The algorithm also derives qualitative variations, such as under-cutting, interference, and jamming. We demonstrate the algorithm on a 26 parameter model of a Geneva mechanism.

An Efficient Parallel Algorithm for Rectangular Packing Based on Bintree Expression

In this paper, a method using bintree structure to express the states of the packing space of rectangular packing is proposed. Through the sequential decomposition of the packing space, the optimal packing scheme of various sized rectangular packing can be obtained by every time putting the optimal piece that satisfies specular conditions toward the current packing space and by locating it at the up-left corner of the current packing space. Different optimal packing schemes that satisfy different demands can be obtained by adjusting the values of the ordering factors KA and KB. A parallel algorithm based on SIMD-CREW shared-memory computer is designed through the analysis of the parallelism of the bintree expression. The whole packing process is clearly expressed by the bintree. The computational complexity of the algorithm is shown to be O(n2logn). Both the experimental results and the comparison with other sequential packing algorithms have proved that the parallel packing algorithm is efficient. What is more, it nearly doubles the problem solving speed.

Packing of Generic, Three-Dimensional Components Based on Multi-Resolution Modeling

A method of optimizing layouts of three-dimensional objects of arbitrary geometry with simulated annealing is presented in this paper. Using multi-resolution models, this approach is able to generate optimal layouts of three-dimensional objects in reasonable time. An example of packing highly non-convex objects illustrates the power of this method.