Applying Axiomatic Design Theory to the Multi-objective Optimization of Disk Brake

Abstract. The multi-objective optimization involves multiple, competing functionality requirements, which is mainly limited to downstream detailed design. Axiomatic design provides the theory to design a complex system top down and deals with multiple functional requirements (FR). It has demonstrated its strength in various types of design tasks. In fact, the objective function is a FR and those variables affecting the objective function are the design parameters (DPs). This paper presents an application of axiomatic design to multi-objective optimization. First, identify the relationship between FRs and DPs in terms of contribution of each DP to each FR by using orthogonal experiment and analysis of variance (ANOVA); then identify important design parameters to a FR and classify design variables into groups based on uncoupled design philosophy; and then establish the function dependence table, and sequentially optimize every objective function. An application in a disk brake design is used to demonstrate the use of the proposed method in dealing with real-world design problems. The results show that the proposed method provides a promising approach to optimize multiple, competing design objectives

The Changes of Intrinsic Excitability of Pyramidal Neurons in Anterior Cingulate Cortex in Neuropathic Pain

To find satisfactory treatment strategies for neuropathic pain syndromes, the cellular mechanisms should be illuminated. Central sensitization is a generator of pain hypersensitivity, and is mainly reflected in neuronal hyperexcitability in pain pathway. Neuronal excitability depends on two components, the synaptic inputs and the intrinsic excitability. Previous studies have focused on the synaptic plasticity in different forms of pain. But little is known about the changes of neuronal intrinsic excitability in neuropathic pain. To address this question, whole-cell patch clamp recordings were performed to study the synaptic transmission and neuronal intrinsic excitability 1 week after spared nerve injury (SNI) or sham operation in male C57BL/6J mice. We found increased spontaneous excitatory postsynaptic currents (sEPSC) frequency in layer II/III pyramidal neurons of anterior cingulate cortex (ACC) from mice with neuropathic pain. Elevated intrinsic excitability of these neurons after nerve injury was also picked up, which was reflected in gain of input-output curve, inter-spike interval (ISI), spike threshold and Refractory period (RP). Besides firing rate related to neuronal intrinsic excitability, spike timing also plays an important role in neural information processing. The precision of spike timing measured by standard deviation of spike timing (SDST) was decreased in neuropathic pain state. The electrophysiological studies revealed the elevated intrinsic excitation in layer II/III pyramidal neurons of ACC in mice with neuropathic pain, which might contribute to central excitation

Modelling and analysis of system robustness for mechanical product based on axiomatic design and fuzzy clustering algorithm

Robust performance is the most important concern in the design of any product, especially in system design stage that precedes parameter design, because it actually determines the attainable level of product robustness in the parameter design phase. In this article, a framework of modelling and analysis of system robustness is proposed, which includes system modelling, cluster analysis and design of experiments. In the process of system modelling, the metamodel of general design theory was utilized to describe the function–structure model of product design, and the customer needs are transformed into functional requirements. Based on the independent axiom and zigzag mapping mode of axiomatic design, the functional requirements are mapping to design parameters, and the design matrix is created, which is then converted into design structure matrix by identifying the relationship between functional requirements and the sensitivity of functional requirements to design parameters. The fuzzy clustering algorithm is utilized to cluster the design parameters and to group the system components into modules in design structure matrix, and the interface among modules can be identified and system robustness incidence matrix is developed. Then the incidence parameters are considered as controllable factors, and experimental design techniques are utilized to analyse the influence of incidence parameters on the design objectives, if any, that may result in a robust system. The proposed framework is illustrated with the trolley design of overhead travelling crane

Reciprocity Inspired Learning for Opportunistic Spectrum Access in Cognitive Radio Networks

International audienceThis paper addresses opportunistic spectrum access (OSA) in non-cooperative cognitive radio networks (CRNs). The selfish behaviors of the secondary users (SUs) will cause a CRN to collapse. The SUs are thus enabled to build beliefs about how other SUs would respond to their decision makings. The interaction among the SUs is modeled as a stochastic learning process. In this way, each SU can independently learn the behaviors of the competitors, optimize the OSA strategies, and finally achieve the goal of reciprocity. Two learning algorithms are proposed to stabilize the stochastic CRNs, the convergence properties of which are also proven theoretically. Simulation results validate the performance of the proposed results, and show that the achieved system performance outperforms some existing protocols