An information-passing strategy for achieving Pareto optimality in the design of complex systems

As engineering systems grow in complexity, it becomes more challenging to achieve system-level designs that effectively balance the trade-offs among subsystems. Lewis and others have developed a well-known, traditional game-theoretic approach for formally modeling complex systems that can locate a Nash equilibrium design with a minimum of information sharing in the form of a point design. This paper builds on Lewis’ work by proposing algorithms that are capable of converging to Pareto-optimal system-level designs by increasing cooperation among subsystems through additional passed information. This paper investigates several forms for this additional passed information, including both quadratic and eigen-based formulations. Such forms offer guidance to designers on how they should change parameter values to better suit the overall system by providing information on directionality and curvature. Strategies for representing passed information are examined in three case studies of 2- and 3-player scenarios that cover a range of system complexity. Depending on the scenario, findings suggest that passing more information generally leads to convergence to a Pareto-optimal set. However, more iterations may be required to reach the Pareto set than if using a traditional game-theoretic approach.     

Ti alloy design strategy for biomedical applications

Based on the empirical rules for the composition design of bulk metallic glasses, a general composition formula for forming nano/ultrafine-structure is suggested. According to this formula a group of quinary Ti alloys have been developed. By controlling the solidification conditions an in situ formed bimodal microstructure consisting of micrometer-sized dendritic β-Ti solid solution dispersed in a nano/ultrafine-structured matrix has been obtained in these quinary Ti alloys. The β-Ti solid solution contributes to the ductility and the low Young's modulus, while the nano/ultrafine-structured matrix contributes to the high strength. The combination of high strength and low Young's modulus offers potential advantages in biomedical applications.     

Minmax defense strategy for complex multi-state systems

This paper presents a general optimization methodology that merges game theory and multi-state system survivability theory. The defender has multiple alternatives of defense strategy that presumes separation and protection of system elements. The attacker also has multiple alternatives of its attack strategy based on a combination of different possible attack actions against different groups of system elements. The defender minimizes, and the attacker maximizes, the expected damage caused by the attack (taking into account the unreliability of system elements and the multi-state nature of complex series-parallel systems). The problem is defined as a two-period minmax non-cooperative game between the defender who moves first and the attacker who moves second. An exhaustive minmax optimization algorithm is presented based on a double-loop genetic algorithm for determining the solution. A universal generating function technique is applied for evaluating the losses caused by system performance reduction. Illustrative examples with solutions are presented.     

Algorithm to design aperiodic optical superlattice for multiple quasi-phase matching

We report a new algorithm, called the self-adjusting algorithm, to construct an aperiodic optical superlattice in which multiple nonlinear optical parametric processes can be realized simultaneously with high conversion efficiency. The numerical simulations show that a self-adjusting algorithm has obvious advantages that are due to its own physical process and feedback function. Especially in comparison with other existing algorithms, a self-adjusting algorithm can eliminate the need to search blindly and is independent of the initial conditions.     

Combining mutual information and stable matching strategy for dynamic evolutionary multi-objective optimization

It is reasonable to assume that the changing of the optimization environment is smooth when considering a dynamic multi-objective optimization problem. Learning techniques are widely used to explore the dependence structure to facilitate population re-initialization in evolutionary search paradigms. The aim of the learning techniques is to discover knowledge from history information, thereby to track the movement of the optimal front quickly through good initialization when a change occurs. In this article, a new learning strategy is proposed, where the main ideas are (1) to use mutual information to identify the relationship between previously found approximated solutions; (2) to use a stable matching mechanism strategy to associate previously found optimal solutions bijectively; and (3) to re-initialize the new population based on a kinematics model. Controlled experiments were carried out systematically on some widely used test problems. Comparison against several state-of-the-art dynamic multi-objective evolutionary algorithms showed comparable performance in favour of the developed algorithm.     

A participatory systems design methodology for changeable manufacturing systems

The ability to adapt to changes in products, processes and technologies is a key competitive factor. Changeable manufacturing paradigms have emerged to address this need, but the industrial implementation remains challenging. In this paper, a participatory design methodology for changeable manufacturing systems is proposed, including requirements specification, selection of appropriate manufacturing paradigm and suitable physical and logical enablers. The methodology supports companies in determining the potential for and mechanisms of transitioning towards changeable manufacturing systems, based on knowledge of products, production, technologies and facilities. The developed methodology is applicable to both new and existing manufacturing systems. It is demonstrated in two industrial cases which highlight its applicability and differences in the appropriate recommended manufacturing systems transition towards changeability as a result of differences in manufacturing characteristics, change requirements and enablers.     

Structure design for precision temperaturemeasurement systems

Translated from Izmeritel'naya Tekhnika, No. 6, pp. 6–8, June, 1990.     

A coordination strategy for distributed multi-agent manufacturing systems

This paper proposes a distributed multi-agent approach for dynamic part routing in automated manufacturing systems. In particular, each part in the system is associated to an intelligent software agent that must select its next destination autonomously (i.e. ignoring the actions of the other agents) and in real time (i.e. at each time it completes an operation on a workstation). Differently from other existing approaches, we overcome the typical myopia of negotiation algorithms based on dispatching rules by allowing the part agents to take decisions not only about the imminent operation, but also for the subsequent ones. The anticipated decisions are transmitted to workstation agents, which are also designated to detect and resolve conflicts by modifying part agents’ decisions. To describe the single agents and their interaction schemes in a formal way, we take advantage of DEVS discrete-event modelling tools, which also allow us to develop a detailed simulation platform for our multi-agent system. The simulation experiments obtained on a detailed model of a manufacturing system derived from the literature confirm the effectiveness of the proposed approach.     

Optimization-based parts-machines matching in flexible manufacturing systems

A new dynamic scheduling strategy, Parts-Machines Matching (PMM), is developed and tested in simulated flexible manufacturing systems. This strategy is aimed to achieve globally optimal matching between parts and machines by a semi-qualitative optimization algorithm, originally developed for the Stable Marriage Problem. Global and Partial implementations of PMM are presented and compared with other conventional part-flow rules. They are found to achieve better shop performance than conventional rules, in terms of system throughput, robustness against travel time uncertainties, and recovery from machine breakdowns. The prospect of bringing about system-wide optimization-based performance improvements into bidding schemes makes the proposed framework very significant.     

Aspects for the design of sputtering systems

The factors causing problems in sputter deposition of thin films are analysed and possible design solutions are offered. ZnO sputtered films are used as a probe for studying problems. The physical parameters of the discharge that have influence on the sputtering processes are described and related to the sputtering parameters. A comparison of discharges used in Diode, Magnetron, and Triode arrangements with respect to their system performances results in the conclusion that Magnetrons are superior to Diodes and that Triodes are better than Magnetrons, in spite of the fact that Triodes' discharges are of the supported kind and their deposited layers are difficult to reproduce.     

Amplitude modulated chaos in two degree-of-freedom systems with quadratic nonlinearities

Summary Two degree-of-freedom systems with weak quadratic nonlinearities are studied under weak external and parametric excitations respectively. All six possible cases, that arise in the presence of 12 internal resonance, are investigated. The method of averaging is used to obtain a set of four first-order amplitude equations that govern the dynamics of the first-order asymptotic approximation to the response. An analytical technique, based on Melnikov's method is used to predict the parameter range for which chaotic dynamics exists in the undamped averaged system. Numerical studies show that such chaotic responses are quite common in these quadratic systems, and they seem to persist even in the presence of damping.     

A silica sol-gel design strategy for nanostructured metallic materials

Batteries, fuel cells and solar cells, among many other high-current-density devices, could benefit from the precise meso- to macroscopic structure control afforded by the silica sol-gel process. The porous materials made by silica sol-gel chemistry are typically insulators, however, which has restricted their application. Here we present a simple, yet highly versatile silica sol-gel process built around a multifunctional sol-gel precursor that is derived from the following: amino acids, hydroxy acids or peptides; a silicon alkoxide; and a metal acetate. This approach allows a wide range of biological functionalities and metals--including noble metals--to be combined into a library of sol-gel materials with a high degree of control over composition and structure. We demonstrate that the sol-gel process based on these precursors is compatible with block-copolymer self-assembly, colloidal crystal templating and the St?ber process. As a result of the exceptionally high metal content, these materials can be thermally processed to make porous nanocomposites with metallic percolation networks that have an electrical conductivity of over 1,000 S cm(-1). This improves the electrical conductivity of porous silica sol-gel nanocomposites by three orders of magnitude over existing approaches, opening applications to high-current-density devices.     

Enhanced similarity-based metamodel updating strategy for reliability-based design optimization

Metamodels are becoming increasingly popular for handling large-scale optimization problems in product development. Metamodel-based reliability-based design optimization (RBDO) helps to improve the computational efficiency and reliability of optimal design. However, a metamodel in engineering applications is an approximation of a high-fidelity computer-aided engineering model and it frequently suffers from a significant loss of predictive accuracy. This issue must be appropriately addressed before the metamodels are ready to be applied in RBDO. In this article, an enhanced strategy with metamodel selection and bias correction is proposed to improve the predictive capability of metamodels. A similarity-based assessment for metamodel selection (SAMS) is derived from the cross-validation and similarity theories. The selected metamodel is then improved by Bayesian inference-based bias correction. The proposed strategy is illustrated through an analytical example and further demonstrated with a lightweight vehicle design problem. The results show its potential in handling real-world engineering problems.     

Root cause analysis strategy for robust design domain recognition

Design domain identification with desirable attributes (e.g. feasibility, robustness and reliability) provides advantages when tackling large-scale engineering optimization problems. For the purpose of dealing with feasibility robustness design problems, this article proposes a root cause analysis (RCA) strategy to identify desirable design domains by investigating the root causes of performance indicator variation for the starting sampling initiation of evolutionary algorithms. The iterative dichotomizer 3 method using a decision tree technique is applied to identify reduced feasible design domain sets. The robustness of candidate domains is then evaluated through a probabilistic principal component analysis-based criterion. The identified robust design domains enable optimal designs to be obtained that are relatively insensitive to input variations. An analytical example and an automotive structural optimization problem are demonstrated to show the validity of the proposed RCA strategy.     

Graph partitioning strategy for the topology design of industrial network

Network topology design problem in industrial network is formulated, which is shown to be equivalent to a multi-constraint optimisation problem: the network design should minimise the amount of inter-network communication, and simultaneously balance the communication load and network size evenly over the resultant sub-networks. To solve this optimisation problem, a graph partitioning strategy is proposed, which can give a good network design by partitioning a graph- based representation of the network optimisation problem. Then, the network optimisation procedures using the graph partitioning strategy are detailed and two experimental, examples are studied. In the experiments, the network designs obtained by the graph partitioning strategy are compared with those obtained by a random partitioning method. The experimental results demonstrate the network designs obtained by the graph partitioning strategy are significantly better than those obtained by the random partitioning method.