A rough set approach to design concept analysis in a design chain

The inherent dynamic relationships among design tasks performed concurrently at different organizations characterize the complexities of a design chain where designers with diverse expertise need to collaborate across organizational boundaries. To ensure timely completion of inter-related design tasks, metrics to facilitate the early evaluation of design concepts are crucial. The ability to evaluate and select suitable design concepts at an early stage will ensure better solutions and greater savings in time and effort further downstream. This paper proposes a new approach based on the rough set theory to design concept analysis. The approach aims at early detection of design inadequacy. A so-called information system is constructed using the information gleaned from design concepts and design capabilities, and analyzed using the rough set theory to derive a set of design rules for design concept analysis. The approach embodies a technique for handling attributes with unavailable information, which is a frequent occurrence in design. This paper presents details of the proposed approach, the novel technique, and a case study.     

A new 3D-CAD system for set-based parametric design

So far, there has been an extraordinary development of computer-aided tools intended to generate, present or communicate 3D models. But there has not been a comparable progress in the development of 3D-CAD systems intended to assist designers in representing and manipulating both geometric and non-geometric design information based on solid models, thus facilitating concurrent engineering (CE). Design objects continue to be produced by traditional means using the computer as little more than a drafting tool. In addition, the state-of-the-art 3D-CAD systems are incapable of encoding engineering uncertainties since only precise single-valued assignments are allowed for their modeling operations. Recently, set-based CE (SBCE) has been attracting public attention as an emerging CE paradigm. Such a set-based design (SBD) approach presents many possibilities in handling the uncertainties that are intrinsic at the early phases of design. This paper addresses a novel concept – set-based parametric design (SBPD) – which combines the SBD practice with the parametric modeling technique widely used in most 3D-CAD systems. A preference set-based design (PSD) model and a design information solid (DIS) model are proposed to incorporate the SBPD concept into the current 3D-CAD systems. Finally, a prototype system is implemented to illustrate the potential to achieve a SBPD practice.     

Multiple-attribute decision-making approach for an energy-efficient facility layout design

Due to the trends of energy shortage and energy price rise, energy efficiency, which was always ignored over the past decades, becomes a worldwide hot issue and also a significant challenge for most factories. Therefore, it is necessary to incorporate energy-relevant criterion as a key criterion with traditional criteria in the layout planning phase. As a multiattribute decision-making (MADM) problem, the evaluation and selection of facility layout alternatives are often difficult and time consuming since the criteria generally have different units and conflicting features. In this article, a MADM approach which incorporates the advantages of rough set theory, analytic hierarchy process (AHP), and technique for order preference by similarity to ideal solution (TOPSIS) is proposed to solve the facility layout design problem with considering both traditional layout criteria and energy relevant criteria. At first, rough set theory is integrated with AHP to determine the weights for each criterion of alternatives. Then, TOPSIS is applied to get the final alternative ranking. Besides, sensitivity analysis for both decision weights and production rates is performed, and a comparison among different decision-making approaches for the same problem is also studied to demonstrate the rationality of the final decision. Finally, a practical expanding case is studied to validate the proposed approach.     

Concept optimization for mechanical product using Genetic Algorithm

Conceptual design is the first step in the overall process of product design. Its intrinsic uncertainty, imprecision, and lack of information lead to the fact that current conceptual design activities in engineering have not been computerized and very few CAD systems are available to support conceptual design. In most of the current intelligent design systems, approach of principle synthesis, such as morphology matrix, bond graphic, or design catalogues, is usually adopted to deal with the concept generation, in which optional concepts are generally combined and enumerated through function analysis. However, as a large number of concepts are generated, it is difficult to evaluate and optimize these design candidates using regular algorithm. It is necessary to develop a new approach or a tool to solve the concept generation. Generally speaking, concept generation is a problem of concept synthesis. In substance, this process of developing design candidate is a combinatorial optimization process, viz., the process of concept generation can be regarded as a solution for a state-place composed of multi-concepts. In this paper, genetic algorithm is utilized as a feasible tool to solve the problem of combinatorial optimizalion in concept generation, in which the encoding method of morphology matrix based on function analysis is applied, and a sequence of optimal concepts are generated through the search and iterative process which is controlled by genetic operators, including selection, crossover, mutation, and reproduction in GA. Several crucial problems on GA are discussed in this paper, such as the calculation of fitness value and the criteria for heredity termination, which have a heavy effect on selection of better concepts. The feasibility and intellectualization of the proposed approach are demonstrated with an engineering case. In this work concept generation is implemented using GA, which can facilitate not only generating several better concepts, but also selecting the best concept. Thus optimal concepts can be conveniently developed and design efficiency can be greatly improved.     

粗糙控制中的规则获取策略研究

本文讨论了一种新型的智能控制方法－－粗糙控制,介绍了粗糙集合理论的基本概念。通过产例给出了粗糙控制中的规则获取策略,并与模糊控制方法作了对比研究。实践证明,粗糙集合理论作为一种处理模糊和不确定性问题的有效工具,对于工业过程、智能仪表等的控制具有简单、高效等特性。     

A grey-based rough decision-making approach to supplier selection

In this paper, we propose a grey-based rough set approach to deal with the supplier selection in supply chain management. The proposed approach takes advantage of mathematical analysis power of grey system theory while at the same time utilizing data mining and knowledge discovery power of rough set theory. It is suitable to the decision-making under more uncertain environments. We also provide a viewpoint on the attribute values in rough set decision table under the condition that all alternatives are described by linguistic variables that can be expressed in grey number. The most suitable supplier can be determined by grey relational analysis based on grey number. A case of supplier selection was used to validate the proposed approach.     

Environmentally conscious design by using fuzzy multi-attribute decision-making

Products affect the environment at many points in their life cycles. Once a product moves from the drawing board into the production line, its environmental attributes are largely fixed. Many researchers have focused on developing intelligent systems to provide a variety of design and manufacturing information to help designers make environmentally conscious decisions. However, in the early design stage, not all the information available is precise. A large amount of information, especially those that are based on designer experience, is fuzzy in nature. This paper presents an innovative method, namely green fuzzy design analysis (GFDA), which involves simple and efficient procedures to evaluate product design alternatives based on environmental consideration using fuzzy logic. The hierarchical structure of environmentally conscious design indices was constructed using the analytical hierarchy process (AHP), which include five aspects: (1) energy, (2) recycling, (3) toxicity, (4) cost, and (5) material. After weighting factors for the environmental attributes are determined, the most desirable design alternative can be selected based on the fuzzy multi-attribute decision-making (FMADM) technique. The benefit of using such a technique is to effectively solve the design problem by capturing human expertise.     

Environmentally conscious design by using fuzzy multi-attribute decision-making

Products affect the environment at many points in their life cycles. Once a product moves from the drawing board into the production line, its environmental attributes are largely fixed. Many researchers focus on developing intelligent systems to provide a variety of design and manufacturing information to help designers make environmentally conscious decisions. However, in the early design stage, not all the information available is precise. A large amount of information, especially that which is based on designer experience, is fuzzy in nature. This paper presents an innovative method, namely, green fuzzy design analysis (GFDA), which involves simple and efficient procedures, to evaluate product design alternatives based on environmental consideration using fuzzy logic. The hierarchical structure of environmentally conscious design indices was constructed using the analytical hierarchy process (AHP), which includes five aspects: (1) energy, (2) recycling, (3) toxicity, (4) cost, and (5) material. After weighting factors for the environmental attributes are determined, the most desirable design alternative can be selected based on the fuzzy multi-attribute decision-making (FMADM) technique. The benefit of using such a technique is to effectively solve the design problem by capturing human expertise .     

Environmentally conscious design by using fuzzy multi-attribute decision-making

Products affect the environment at many points in their life cycles. Once a product moves from the drawing board into the production line, its environmental attributes are largely fixed. Many researchers have focused on developing intelligent systems to provide a variety of design and manufacturing information to help designers make environmentally conscious decisions. However, in the early design stage, not all the information available is precise. A large amount of information, especially those that are based on designer experience, is fuzzy in nature. This paper presents an innovative method, namely green fuzzy design analysis (GFDA), which involves simple and efficient procedures to evaluate product design alternatives based on environmental consideration using fuzzy logic. The hierarchical structure of environmentally conscious design indices was constructed using the analytical hierarchy process (AHP), which include five aspects: (1) energy, (2) recycling, (3) toxicity, (4) cost, and (5) material. After weighting factors for the environmental attributes are determined, the most desirable design alternative can be selected based on the fuzzy multi-attribute decision-making (FMADM) technique. The benefit of using such a technique is to effectively solve the design problem by capturing human expertise.     

A systematic approach for green design in modular product development

In response to the changing market trend of having large varieties within a small production, green design and modular design have assumed significant roles in the product development process. This research applied quality function deployment (QFD) to achieve the goal of analyzing customers' needs. It also applied design structure matrix (DSM) based on the modular concept to group different design parts together to obtain optimized module efficiency. A balance between product development and ecological (ECO) friendliness can be achieved in advance if green QFD (GQFD) and DSM were introduced during the early design and manufacturing stages. Furthermore, using the result of GQFD, we proposed the analysis of DSM to find the result of coupled tasks. A pairwise comparative algorithm and loop analysis algorithm in DSM were utilized to decrease the number of module groups and find the most suitable clustering approach for further green design consideration. In this research, an electronic translator was used as a case study for the integration of green design with modularity. The main goal of this paper is to sort the importance weight values for green consideration by QFD during product development. These results can then be applied to finish modular grouping by DSM for subsequent green assembly and disassembly considerations of the products.     

Development of a fuzzy FMEA based product design system

The demand for high-quality and low-cost products with short development time in the dynamic global market has forced researchers and industries to focus on various effective product development strategies. The authors are carrying out research studies to explore the applicability of fuzzy logic and knowledge-based systems technologies to today’s competitive product design and development, with an emphasis on the design of high quality products at the conceptual design stage. A framework of a fuzzy FMEA (failure modes and rffects analysis) based evaluation approach for new product concepts is proposed in this paper. Based on the proposed approach and methodologies, a prototype system named EPDS-1, which can assist inexperienced users to perform FMEA analysis for quality and reliability improvement, alternative design evaluation, materials selection, and cost assessment, thus helping to enhance robustness of new products at the conceptual design stage. This paper presents the underlying concepts of the development and shows the practical application with the prototype system with a case study.     

基于粗糙集和ART2 神经网络的多传感器信息融合

神经网络是信息融合的一种重要方法,粗糙集理论是处理不完备信息的一种技术。本文提出了一种基于粗糙集和ART2神经网络的多传感器信息融合方法。ART2网络是一种无监督神经网络,能够实现对输入的任何模式信号自动识别和分类。而对信息融合中常遇到的数据超载问题,提出采用粗糙集与神经网络结合的方法解决。文章给出了基于粗糙集理论的组合神经网络的模型结构,最后用一个脱机手写体数字识别的实例验证了该方法的有效性。     

A hybrid approach of rough set theory and genetic algorithm for fault diagnosis

This paper proposes an integrated intelligent system that builds a fault diagnosis inference model based on the advantage of rough set theory and genetic algorithms (GAs). Rough set theory is a novel data mining approach that deals with vagueness and can be used to find hidden patterns in data sets. Based on this approach, minimal condition variable subsets and induction rules are established and illustrated using an application for motherboard electromagnetic interference (EMI) test fault diagnosis. This integrated system successfully integrated the rough set theory for handling uncertainty with a robust search engine, GA. The result shows that the proposed method can reduce the number of conditional attributes used in motherboard EMI fault diagnosis and maintain acceptable classification accuracy. The average diagnostic accuracy of 80% shows that this hybrid model is a promising approach to EMI diagnostic support systems .     

Approximate Estimation of the Product Life Cycle Cost Using Artificial Neural Networks in Conceptual Design

In order to improve the design of products and reduce design changes, cost, and time to market, life cycle engineering has emerged as an effective approach to address these issues in today’s competitive global market. As over 70% of the total life cycle cost of a product is committed at the early design stage, designers can substantially reduce the life cycle cost of products by giving due consideration to the life cycle implications of their design decisions. During the early design stages there may be competing requirements. In addition, detailed information is scarce and decisions must be made quickly. Thus, both the overhead in developing parametric life cycle cost (LCC) models for a wide range of concepts or requirements, and the lack of detailed information make the application of traditional LCC models impractical. A different approach is required because a traditional LCC method should be incorporated in the very early design stages. This paper explores an approximate method for providing the preliminary life cycle cost. Learning algorithms trained to use the known characteristics of existing products can perhaps allow the life cycle cost of new products to be approximated quickly during the conceptual design phase without the overhead of defining new LCC models. Artificial neural networks are trained to generalise product attributes and life cycle cost data from pre-existing LCC studies. Then, the product designers query the trained artificial model with new high-level product attribute data to obtain an LCC for a new product concept quickly. Foundations for the learning LCC approach are established, and then an application is provided. This paper has been developed to provide designers with LCC information to guide them in conceptual design.     

A tolerancing optimisation method for product design

Although extensive research has been carried out in the area of tolerancing techniques for product design, concurrent engineering is still very seldom used in this context. This paper introduces a unique tolerancing method which applies the concept of concurrent engineering. The proposed method essentially allocates the required functional assembly tolerances to the component tolerances by formulating the tolerancing problem into a mathematical model and solving the model using a linear programming approach. The component tolerances are first represented in terms of the process tolerances, assigned by process planners at an early stage of the product design. The objective function of the mathematical model, which is to maximise the residual tolerances of the processes, is then established and the constraints formulated based on the assembly requirements and process constraints. The model is subsequently solved using a linear programming approach. Finally, the proposed method is tested on a practical example.     

采用粗糙集数据融合的车轮踏面擦伤预示诊断

介绍了一种将粗糙集理论与小波分析相结合对车轮踏面擦伤进行融合预示诊断的方法。在分析踏面擦伤危害和多传感器数据融合特点的基础上，将粗糙集理论和小波分析引入多传感器数据融合。该方法采用小波分析对多传感器信号进行多特征提取识别，然后利用粗糙集选择特征，建立诊断规则，从而实现对车轮踏面擦伤的融合预示诊断。     

设计特征在机械产品概念设计过程中的应用

设计特征能作为功能单元建立用户需求与原理解的关系,也能将设计信息带到下游应用.因此提出了基于设计特征的概念设计过程.功能设计由设计说明书开始,包括功能需求和功能约束,由实现功能的行为方案的不同对功能进行分解,设计问题被描述为功能和实现功能的行为的层次模型.通过功能推理步骤来指导功能设计,包括因果行为推理和功能分解.当功能单元可以在设计特征库中找到实现原型时,功能分解结束,设计信息被带到CAD系统低层.最后结合实例说明了该方法及其原型系统.     

基于矢谱和粗糙集理论的旋转机械故障诊断

矢谱融合了转子同源双通道的信息,能准确反映转子运动状态．粗糙集理论是一种对决策表进行简化,去除冗余属性的数据分析和处理方法．提出了基于矢谱和粗糙集理论的旋转机械故障诊断方法．计算了旋转机械振动4种典型故障的矢谱征兆,使用粗糙集理论对其进行约简,根据约简的结果生成矢谱诊断规则,并利用得到的规则对故障测试样本进行了诊断．结果表明：相对于单通道数据,基于矢谱和粗糙集理论的故障诊断不仅简化了诊断规则,而且明显提高了故障诊断的准确率．     

中小企业协同设计中基于不确定语言关联的粗粒度任务规划

针对中小企业协同设计中粗粒度任务规划问题，在分析现有理论和方法的基础上，本文提出了一种基于不确定多属性决策的粗粒度任务规划方法。该方法根据设计任务之间、设计单位和设计任务之间的不确定语言等关联，采用不确定决策理论合理地进行任务的排序和时间安排，并科学性地选择设计伙伴。文章给出了该方法的基本原理和基本步骤，建立了其相应数学模型，并通过实例验证了其正确性和科学性，对协同设计的任务规划具有较好的实用参考价值。