Optimal design of reconfigurable parallel machining systems

A reconfigurable machining system is usually a modularized system, and its configuration design concerns the selections of modules and the determination of geometric dimensions in some specific modules. All of its design perspectives from kinematics, dynamics, and control have to be taken into considerations simultaneously, and a multidisciplinary design optimization (MDO) tool is required to support the configuration design process. This paper presents a new MDO tool for reconfigurable machining systems, and it includes the following works: (i) the literatures on the computer-aided design of reconfigurable parallel machining systems have been reviewed with a conclusion that the multidisciplinary design optimization is essential, but no comprehensive design tool is available to reconfigurable parallel machining systems; (ii) a class of reconfigurable systems called reconfigurable tripod-based machining system has been introduced, its reconfiguration problem is identified, and the corresponding design criteria have been discussed; (iii) design analysis in all of the disciplines including kinematics, dynamics, and control have been taken into considerations, and design models have been developed to evaluate various design candidates; in particular, the innovative solutions to direct kinematics, stiffness analysis for the design configurations of tripod-based machines with a passive leg, and concise dynamic modelling have been provided; and (iv) A design optimization approach is proposed to determine the best solution from all possible configurations. Based on the works presented in this paper, a computer-aided design and control tool have been implemented to support the system reconfiguration design and control processes. Some issues relevant to the practical implementation have also been discussed.     

A multi-criteria decision making approach for strategy formulation using Pythagorean fuzzy logic

The objective of this paper is to develop Pythagorean fuzzy (PF) multi-objective optimization by ratio analysis (PF-MOORA) plus full MULTIplicative form (PF-MULTIMOORA) method for solving multicriteria decision making (MCDM) problems with completely unknown information of criteria weights. In the model formulation process, a new distance measure is defined to quantify the difference between PF sets by combining Hamming distance and Hausdorff metric. This distance measure is, subsequently, implemented in entropy weight model for determining unknown weights of criteria, and also in reference point approach for obtaining preference indices of alternatives. To overcome the deficiencies occurred in existing MULTIMOORA method, like multiple comparisons, circular reasoning, and so on, an aggregation-based approach is recommended in the proposed PF-MULTIMOORA. To demonstrate the feasibility and practicality of the proposed method, an example concerning strategy prioritization of a tiles manufacturing company is presented. In the strategy evaluation process, at first, the judgement values provided by the decision maker are expressed in linguistic terms, and then those are converted into PF numbers through a PF weighting scale. The sensitivity of the proposed model is validated by changing of weights of criteria which impact on the ranks of the strategies. To show robustness of the developed method, the result attained by applying PF-MULTIMOORA is compared with existing techniques, not only in crisp and fuzzy quantitative strategic planning matrix context, but also using four other MCDM methods, namely, modified PF-MOORA, as a particular case of the proposed PF-MULTIMOORA technique, PF weighted sum, PF-TOPSIS and PF-VIKOR.     

Fuzzy logic decision making for multi-robot security systems

The paper proposes a fuzzy logic decision making system for security robots that deals with multiple tasks with dynamically changing scene. The tasks consist of patrolling the environment, inspecting for missing items, chasing and disabling intruders, and guarding the area. The decision making considers robot limitations such as maximum floor coverage per robot and remaining robot battery energy, as well as cooperation among robots to complete the mission. Each robot agent makes its own decision based on its internal information as well as information broadcast to it by other robots about events such as intruder sighting. As a result the multi-robot security system is distributive without a central coordinator. The system has been implemented both in simulations and on actual robots and its performance has been verified under different scenarios.     

Model-based detection of critical driving situations with fuzzy logic decision making

A precise knowledge about the current driving condition is getting increasingly important for future driver assistance systems like global chassis control or collision avoidance systems for avoiding any critical driving situation. Moreover a precise knowledge about the driving situation can be used in testing, in evaluation, and for comparison of new passenger cars. A two degree of freedom model of vehicle lateral dynamics is used to derive a characteristic velocity stability indicator (CVSI). The CVSI is used to distinguish between different driving and stability conditions (i.e. understeering, oversteering, and neutralsteering). This forms the basis for a driving condition detection system with fixed thresholds. It is then extended to a detection system with fuzzy logic thresholds. The CVSI and the fuzzy systems are compared experimentally using (i) a slalom test drive on an icy road and (ii) a stationary circular test drive on a dry asphalt road.     

基于观测器的可重构机械臂分散自适应模糊控制

提出一种基于观测器的可重构机械臂分散自适应模糊控制方案.将可重构机械臂的动力学描述为一个交联子系统的集合,子系统控制器由自适应模糊系统和鲁棒控制项组成.基于状态观测器观测值构建的自适应模糊系统用于逼近子系统动力学模型和交联项,鲁棒控制项用于抵消模糊逼近误差对轨迹跟踪的影响.数值仿真证明了所提出的分散控制方案的有效性.     

A decision support system for coating selection based on fuzzy logic and multi-criteria decision making

Wear and corrosion are the most important factors that the surface of the engineering parts must confront. The need for protection and improvement of the mechanical characteristics of the surface of engineering parts can be to some extent satisfied by coatings. Coatings are considered as an excellent solution when resistance to corrosion, oxidation or low friction is demanded, but due the complexity of selecting the appropriate one, engineers often avoid them. The need for simultaneous consideration of qualitative and quantitative properties, render the use of classic material selection theories inadequate. An expert system for coating selection is presented in this paper, which can handle both qualitative and quantitative variables. The mathematical model used combines the multi-criteria decision making theories (MCDM) together with the fuzzy sets theory. The “Max-Min set” method is applied to calculate the ordering value of the alternatives while the TOPSIS method is used to rank them. A numerical example is provided to illustrate the method. Finally, the process presented can be easily computerized, to create the relative software.     

Parameterized fuzzy operators in fuzzy decision making

The basic operations of fuzzy sets, such as negation, intersection, and union, usually are computed by applying the one‐complement, minimum, and maximum operators to the membership functions of fuzzy sets. However, different decision agents may have different perceptions for these fuzzy operations. In this article, the concept of parameterized fuzzy operators will be introduced. A parameter α will be used to represent the degree of softness. The variance of α captures the differences of decision agents' subjective attitudes and characteristics, which result in their differing perceptions. The defined parameterized fuzzy operators also should satisfy the axiomatic requirements for the traditional fuzzy operators. A learning algorithm will be proposed to obtain the parameter α given a set of training data for each agent. In this article, the proposed parameterized fuzzy operators will be used in individual decision‐making problems. An example is given to show the concept and application of the parameterized fuzzy operators. © 2003 Wiley Periodicals, Inc.     

Functional diagnostics of dynamic systems using fuzzy rules for fault analysis and decision making

A method of functional diagnostics of dynamic systems based on fuzzy approach is proposed; this method possesses two basic specific features differing it from other known fuzzy methods based on application of fuzzy logics and state observer data base. First, this method uses the data base of interacting observers, while traditionally independent observers are used. The second specific feature of the proposed method is that it presumes the absence of a strict boundary between the serviceable and disabled technical states of the system. This makes it possible to specify the decision making rule in fault diagnosis.     

A structure of fuzzy systems for support of decision making

 The aim of this paper is to introduce the notions of operation and mapping between general fuzzy decision systems (GFDS) over some decision space (𝕍, ℂ), where 𝕍 is a set of variants and ℂ is a set of criteria. The operations between two decision systems make possible to combine several decision system of different experts and the mappings between two decision systems enable to study structural properties of such systems. Relations between utility function h:𝕍→[0,1] and operations, and further, mappings between GFDS are investigated, too.     

On Pythagorean fuzzy decision making using soft likelihood functions

Multicriteria decision making (MCDM) is to select the optimal candidate which has the best quality from a finite set of alternatives with multiple criteria. One important component of MCDM is to express the evaluation information, and the other one is to aggregate the evaluation results associated with different criteria. For the former, Pythagorean fuzzy set (PFS) is employed to represent uncertain information in this paper, and for the latter, the soft likelihood function developed by Yager is used. To address MCDM issues from a new perspective, the likelihood function of PFS is first proposed in this study and, to improve some of its limitations, the ordered weighted averaging (OWA)-based soft likelihood function is defined, which introduces the attitudinal characteristic to identify decision makers' subjective preferences. In addition, the defined soft likelihood function of PFS is extended by weighted OWA operator considering the importance weight of the argument. Several illustrative cases are provided based on the presented (weighted) OWA-based soft likelihood functions in Pythagorean fuzzy environment for MCDM problem.     

Optimizing logistic processes using a fuzzy decision making approach

This paper addresses the problem of optimizing logistic processes that can be modeled as a birth-and-death process. A fuzzy decision making algorithm is proposed to assign components to orders, which is a common task in a large number of logistic processes. The dynamic assignment of components to orders is the key issue in optimizing logistic processes. This paper proposes several criteria for this optimization. These criteria are combined using weighted fuzzy aggregation in a fuzzy decision making environment. First, a simple but illustrative example shows that the proposed techniques can be applied with good results to this type of processes. Then, the proposed method is applied to a real-world logistic process at Fujitsu-Siemens Computers.     

Adaptive management of natural systems using fuzzy logic

Hypotheses about how management practices influence ecosystem services can be tested using a crisp, probability-based, or fuzzy decision rule. The correct decision rule depends on whether: (1) the observed state of an ecosystem service (x) is non-stochastic or stochastic; (2) the true state of the ecosystem service (y) is non-stochastic or stochastic; and (3) the relationship between x and y is deterministic, stochastic, or uncertain. Crisp and probability-based decision rules are not appropriate when the relationship between y and x is uncertain in the sense that the decision maker is unable or unwilling to specify conditional probabilities of y given x. Under these conditions, a fuzzy decision rule is appropriate. A hypothetical case study is used to illustrate how a fuzzy decision rule is used to test hypotheses about whether selective cutting of timber provides greater or less forest biodiversity than clearcutting of timber. The case study describes how to incorporate the decision rule in an active adaptive management framework to sequentially test the extent to which changes over time in other factors influencing ecosystem services, such as greater spread of invasive species due to global warming, alter the efficacy of timber management practices. The fuzzy adaptive management decision rule can be generalized to account for the effects of management practices on multiple ecosystem services.     

Decision making support system for house purchase by using fuzzy logic

In this paper, we suggest a decision making support system for house purchasers, using fuzzy inference and hierarchic structure of evaluation. Main part of this system consist of macro and micro evaluation. Essential factors are taken into account in macro evaluation, and unessential detailed factors are considered later in micro evaluation. By adopting this structure, many decision makers could get their most suitable result.     

可重构制造系统可重构逻辑控制器设计与实现

针对可重构制造系统的逻辑控制问题,提出一种可重构逻辑控制器的解决方案．该逻辑控制器具有递阶分布式的控制体系结构,并根据模块化的设计思想设计成多个分离的功能模块．然后给出基于CORBA组件模型(CCM)的可重构逻辑控制器软件的开发过程．由递阶分布式体系、模块化设计和软件组件开发技术实现的可重构逻辑控制器具有快速动态重构的能力,能满足可重构制造系统逻辑控制的要求．     

Macro-programmable reconfigurable stream processor for collaborative manufacturing systems

Growing demand for high speed processing of streamed data (e.g. video-streams, digital signal streams, communication streams, etc.) in the advanced manufacturing environments requires the adequate cost-efficient stream-processing platforms. Platforms based on the embedded microprocessors often cannot satisfy performance requirements due to limitations associated with the sequential nature of data execution process. During the last decade, development and prototyping of the above embedded platforms has started moving towards utilization of the Field Programmable Gate Array (FPGA) devices. However, the programming of an application to the FPGA based platform became an issue due to relatively complicated hardware design process. The paper presents an approach which allows simplification of the application programming process by utilization of: (i) the uniformed FPGA platform with the dynamically reconfigurable architecture, (ii) a programming technique based on a temporal partitioning of the application in segments which can be described in terms of macro-operators (function specific virtual components). The paper describes the concept of the approach, presents the analytical investigation and experimental verification of the cost-effectiveness of the proposed platform comparing to the platforms based on sequential micro-processors. It is also shown that the approach can be beneficially utilized in collaborative design and manufacturing.     

Hardware-software co-synthesis of hard real-time systems with reconfigurable FPGAs

Real-time systems cover a wide application domain. This paper presents an efficient heuristic algorithm for enforcing the schedulability of aperiodic hard real-time tasks arriving simultaneously with precedence constraints and individual deadlines. The proposed co-synthesis algorithm integrates partitioning and non-preemptive scheduling. Reconfigurable FPGAs are incrementally added when schedulability suffers in a uniprocessor system. Initially, a schedule that minimizes the maximum lateness and satisfies the precedence constraints is made. If individual timing constraints are not met in this schedule, some tasks are selected and transferred to dynamically reconfigured FPGAs. The proposed algorithm was implemented and tested with a large number of task graphs with task size as high as 700 nodes. The algorithm could not only achieve schedulability but also could reduce the total completion time of the task graph. Moreover, incremental addition of reconfigurable FPGAs yielded a cost effective solution.     

First break refraction event picking using fuzzy logic systems

First break picking is a pattern recognition problem in seismic signal processing, one that requires much human effort and is difficult to automate. The authors' goal is to reduce the manual effort in the picking process and accurately perform the picking. Feedforward neural network first break pickers have been developed using backpropagation training algorithms applied either to an encoded version of the raw data or to derived seismic attributes which are extracted from the raw data. The authors summarize a study in which they applied a backpropagation fuzzy logic system (BPFLS) to first break picking. The authors use derived seismic attributes as features, and take lateral variations into account by using the distance to a piecewise linear guiding function as a new feature. Experimental results indicate that the BPFLS achieves about the same picking accuracy as a feedforward neural network that is also trained using a backpropagation algorithm; however, the BPFLS is trained in a much shorter time, because there is a systematic way in which the initial parameters of the BPFLS can be chosen, versus the random way in which the weights of the neural network are chosen     

Approach to decision making in fuzzy environment

A general approach to solving a wide class of optimization problems with fuzzy coefficients in objective functions and constraints is described. It is based on a modification of traditional mathematical programming methods and consists in formulating and solving one and the same problem within the framework of interrelated models with constructing equivalent analogs with fuzzy coefficients in objective function alone. This approach allows one to maximally cut off dominated alternatives from below as well as from above. The subsequent contraction of the decision uncertainty region is associated with reduction of the problem to multicriteria decision making in a fuzzy environment. The approach is applied within the context of fuzzy discrete optimization models, that is based on a modification of discrete optimization algorithms. The results of the paper are of a universal character and are already being used to solve problems of the design and control of power systems and subsystems.     

Automatic folding of cartons using a reconfigurable robotic system

This paper proposes new theories and methodology for the study of folding carton and reconfigurable packaging systems and presents both virtual and experimental systems to support the theories. Equivalent mechanisms of cartons were established by describing carton creases as joints and carton panels as links. With the analysis of the equivalent mechanism, the gusset vertexes of cartons were analyzed based on their equivalent spherical linkages and were identified as guiding linkages that determine folding. A reconfigurable robotic system was developed to demonstrate the ability to erect diverted cartons in the reconfigurable packaging system which could previously only be achieved manually.