Product modularization for life cycle engineering

Modular products consist of detachable modules, which can be manufactured, assembled, and serviced separately. Some of the modules may be reusable, recyclable or re-manufacturable upon product retirement. Thus, modular design can provide benefits to many aspects of product life cycle. This paper presents an integrated modular design methodology for life cycle engineering. The methodology consists of three phases: problem definition, interaction analysis and module formation. The methodology identifies the factors related to the design objectives, relates these factors to design components through interaction analysis, and clusters components into modules. Two case studies along with detailed analysis are provided to illustrate the methodology and the algorithms.     

The application of surface demoldability and moldability to side-core design in die and mold CAD

In casting, molding and forming processes, the surface geometries of the fabricated products are formed/molded by different functional components of tooling. In plastic injection molding, they are molded by core, cavity or side-cores. In die and mold CAD, how to identify the product surfaces formed/molded by the corresponding tool components for a given product CAD model is critical, as it affects the determination of parting directions, parting lines and parting surfaces, the generation of core and cavity blocks, and finally the design of side-cores and their actuating mechanisms. In this paper, the concepts of surface visibility, demoldability, and moldability are first presented and formulated. The surfaces formed/molded by core, cavity and side-cores are then defined based on the plastic injection molding process. The methodology to identifying and classifying them is further developed. By employing the proposed notions of the demoldability map of surfaces and undercut features, the most preferred demolding direction, the grouping of undercut features, and how to conduct the side-core design is articulated succinctly, and the detailed procedures and processes are presented. Through an industrial case study, the developed methodology for side-core design is systematically presented and the feasibility of the developed approaches is verified.     

Modular platform optimization in conceptual vehicle body design via modified graph-based decomposition algorithm and cost-based priority method

The modular platform design, an increasingly popular method for designing vehicles, involves several unresolved problems such as the lack of effective techniques for dividing structural components into modules. This article presents a methodology for dividing a typical body-in-white structure into modules based on a modified graph-based decomposition algorithm, then selecting the shared modules based on a cost-based priority method. Structural stiffness, manufacturability, and assembling ability are three main optimization objects. Shared modules, parameterized modules, and flexible modules can be easily determined and optimized through the proposed methodology. In the case study, the proposed methodology was applied to design a car product family to validate its feasibility and effectiveness.     

Cost-effective design for injection molding

It is commonly agreed that a large proportion of the ultimate product cost is determined at product design stage. Therefore, a cost-effective design cannot be obtained unless all cost issues are resolved at early design stage. Therefore, instead of performing cost estimation after design, research presented in this paper aims to provide on-line cost evaluation and advisory to help product designers avoid cost-ineffective design. The objective can be obtained by (1) identifying factors that might affect product cost at each product design stage, (2) developing a design for cost effectiveness methodology that accommodates the concepts of concurrent engineering, and (3) developing a computer-based design for cost effectiveness system based on the proposed methodology. In this research, we focus on injection molding product design due to the advantages of injection molding process, such as high production rates, excellent quality and accuracy of the parts, and very long mold life. This paper first reviews and characterizes the conventional molding product development process with an emphasis on the identification of cost factors. Based on the results of process characterization, a cost model is developed, which depicts the relationships between cost factors and product development activities, as well as their relationships with product geometry. According to the product life cycle activities and the cost model, a design for cost effectiveness process is proposed. The process and the cost model are then employed for the development of a computer-based product design for cost effectiveness as one of the module of an integrated design for injection molding environment.     

Balancing homogeneity and heterogeneity in design exploration by synthesizing novel design alternatives based on genetic algorithm and strategic styling decision

Designers constantly and consistently draft and develop both general concepts and directions to identify the solution that best fits the styling objectives of the lead designer. Designers often confront design fixations that cognitively clash to explore different design combinations. As design teams explore the range of possible design spaces of a certain design strategy, there is an opportunity for computational approaches to improve the styling process. By implementing product appearance similarity and styling strategy in computational design synthesis, it is possible to discover combinations that would otherwise remain unexplored by human designers. Numerous studies on design synthesis have been conducted. However, there has been no focus on the morphological synthesis of designs with strategic styling decisions. Considering this, the proposed study develops a method to synthesize car styling based on product appearance similarity for effective design exploration in the concept generation phase. The similarities of products across different generations, product portfolios, and competitors’ products are calculated to evaluate the strategic styling decision. The results of the strategic styling decision are used to formulate a fitness function. Car styling is then synthesized with a genetic algorithm based on this fitness function to generate car styling in accordance with the target strategic styling decision. In this respect, designers can computationally synthesize novel design alternatives that consider both homogeneity (family look in design) and heterogeneity (design trend in the market) by pinpointing the desired design exploration area. Ultimately, the style synthesis methodology proposed in this research can help designers to utilize the gradual visualization of styling strategies for more effective and efficient managerial design decisions. To do this, we conduct five major tasks: first, car design data are collected for design synthesis; second, the product appearance similarity is calculated to measure the strategic styling decision; third, synthesis validation is conducted to test whether the proposed methodology can create outside-the-box designs; fourth, a genetic algorithm is used to synthesize car designs in consideration of the strategic styling decision; finally, a series of in-depth interviews with experts and validation experiments are conducted with in-house automobile designers to examine the impact of the proposed methodology. The results showed that designers can quantitatively measure and compare the styling strategies of each car brand, then implement design upgrades, while still maintaining that specific style. Correspondingly, computationally generated design alternatives improve the satisfaction in ease, time, objective reflection and novelty of design outcomes when formulating design strategies in the concept generation phase.     

An intelligent grouping algorithm for cellular manufacturing

The methodology presented in this paper will provide a means of identifying part families/machine cells using design and manufacturing characteristics simultaneously. The technique used is a self-organizing neural network called interative-activation and competition (IAC) which acts as a content-addressable memory. This neural network is used to define a similarity index of the pairwise comparisons of parts based on a variety of design and manufacturing characteristics. A bond energy algorithm partitions the matrix of part similarity indices to create part families and inferred from the part families are machine cells. A brief example will be examined as well as discussion of the results.     

Modular product design with grouping genetic algorithm—a case study

Modular products are products that fulfill various functions through the combination of distinct modules. These detachable modules are constructed both according to the maximum physical and functional relations among components and maximizing the similarity of specifically modular driving forces. Accordingly, a non-linear programming is proposed to identify separable modules and simultaneously optimize the number of modules. This paper presents a systematic approach to accomplish modular product design in four major phases. Phase 1 is by means of functional and physical interaction analysis to format a component-to-component correlation matrix. Phase 2 is the exploration of design requirements to evaluate the relative importance of each modular driver. In phase 3, non-linear programming is used to formulate the objective function. In the final phase, a heuristic grouping genetic algorithm is adopted to search for the optimal or near-optimal modular architecture. This process and its application are illustrated by a real case of an electrical consumer product provided by an Original Design Manufacturer. The results demonstrate that the designer could direct a new approach to establish product modules according to the relative importance of modular drivers and the interaction among components.     

Feature-based design for heterogeneous objects

Heterogeneous objects are objects composed of different constituent materials. In these objects, multiple desirable properties from different constituent materials can be synthesized into one part. In order to obtain mass applications of such heterogeneous objects, efficient and effective design methodologies for heterogeneous objects are crucial.In this paper, we present a feature based design methodology to facilitate heterogeneous object design. Under this methodology, designers design heterogeneous objects using high-level design components that have engineering significance. These high level components are form features and material features. In this paper, we first examine the relationships between form features and material features in heterogeneous objects. We then propose three synthesized material features in accordance with our examination of these features. Based on these proposed features, we develop a feature based design methodology for heterogeneous objects. Two enabling methods for this design methodology, material heterogeneity specification within each feature and combination of these material features, are developed. A physics (diffusion) based B-spline method is developed to (1) allow design intent of material variation be explicitly captured by boundary conditions, (2) ensure smooth material variation across the feature volume. A novel method, direct face neighborhood alteration, is developed to increase the efficiency of combining heterogeneous material features.Examples of using this feature based design methodology for heterogeneous object design, such as a prosthesis design, are presented.     

An integrated framework for mechatronics based product development in a fuzzy environment

New product development (NPD) is a term used to describe the complete process of bringing a new concept to a state of market readiness. Mechatronics based product requires a multidisciplinary approach for its modeling, design, development and implementation. An integrated and concurrent approach focusing on integrating the mechanical structure with basic three components namely sensors, controllers and actuators is required. This paper aims at developing a framework for a new Mechatronics product development. For conceptual design of Mechatronics system, various tools like Fuzzy Delphi Method (FDM), Fuzzy Interpretive Structural Modeling (FISM), Fuzzy Analytical Network Process (FANP) and Fuzzy Quality Function Deployment (FQFD) are used. Based on the prioritized design requirements, the functional specifications of the required components are developed. Then, Computer Aided Design and control system software are used to develop the detailed system design. Then, a prototype model is developed based on the integration of mechanical system with Sensor, Controller and Electrical units. Performance of the prototype model is monitored and Fuzzy failure mode and effect analysis (FMEA) is then used to rank the potential failures. Based on the results of fuzzy FMEA, the developed model is redesigned. The proposed framework is illustrated with a case study related to developing automatic power loom reed cleaning machine.     

面向查询式实体解析的多属性数据索引技术

实体解析是数据集成的关键方面,也是大数据分析与挖掘的必要预处理步骤.大数据时代,随着查询驱动的数据应用需求的不断增长,查询式实体解析成为热点问题.为了提升查询-解析效率,研究了面向实体缓存的多属性数据索引技术.涉及两个核心问题:(1)如何设计多属性数据索引?设计了基于R-树的多属性索引结构.为了满足实体缓存在线生成需求,提出了基于空间聚类的在线索引构建方法.提出了基于“过滤-验证”的多维查询方法,利用多属性索引有效地过滤掉不可能命中的记录,然后采用相似性函数或距离函数逐一验证候选记录.(2)如何将不同的字符串属性插入到树形索引中?解决思路是,将字符串映射到数值空间.针对Jaccard相似性和编辑相似性,提出了基于q-gram的映射方法,并提出了基于向量降维的优化和基于z-order的优化,实现高质量的“字符串→数值”映射.最后,在两个数据集上进行实验评估,验证多属性索引的有效性,并测试其各个方面.     

多元统计过程监控与安全生产

统计过程控制是一种改善产品质量及保证安全生产的有力工具。针对现有多元统计监控技术大多假定所考察的生产过程本身仅存在一个标准运行条件,导致实际应用时往往引发大量的连续报警的问题,本文基于主角度建立了任意两个主元模型相似性的度量,提出了一种基于多主元模型的过程监控方法。通过该方法能有效地检测、诊断工业过程中的异常,以避免事故的发生,将带来巨大的经济效益。最后,讨论了相应的软件实现平台EZMon及其应用。     

Belief rule-based methodology for mapping consumer preferences and setting product targets

Rapid and accurate identification of consumer demands and systematic assessment of product quality are essential to success for new product development, in particular for fast moving consumer goods such as food and drink products. This paper reports an investigation into a belief rule-based (BRB) methodology for quality assessment, target setting and consumer preference prediction in retro-fit design of food and drink products. The BRB methodology can be used to represent the relationships between consumer preferences and product attributes, which are complicated and nonlinear. A BRB system can initially be established using expert knowledge and then optimally trained and validated using data generated from consumer or expert panel assessments or from tests and experiments. The established BRBs can then be used to predict the consumer acceptance of new products or set product target values in retro-fit design. The proposed BRB methodology is applied to the design of a lemonade drink product using real data provided by a sensory product manufacturer in the UK. The results show that the BRB methodology can be used to predict consumer preferences with high accuracy and to set optimal target values for product quality improvement.     

A quantitative methodology for acquiring engineering characteristics in PPHOQ

Quality function deployment (QFD) is a planning and problem-solving methodology that is widely used for translating customer requirements (CRs) into engineering characteristics (ECs) of a product. Acquiring ECs in product planning house of quality (PPHOQ) is a crucial step of QFD methodology. In previous studies, methods for acquiring ECs are qualitative and subjective. In this paper, a novel quantitative methodology for acquiring ECs in PPHOQ is presented. In the proposed methodology, a hierarchical framework for generating the refined set of EC candidates from the EC candidates obtained by brainstorming is established. A concept of a general relationship between a CR and an EC is introduced, and then a novel approach for identifying the general relationship measures with the help of intelligent characteristics of rough set is proposed. Another concept of the intension threshold of the general relationship corresponding to a CR is introduced, and then a set-operation-based method of acquiring ECs used in PPHOQ is presented. An example of a two-cylinder washing machine is used to demonstrate the proposed methodology.     

Design of cellular manufacturing systems with assembly considerations

This research proposes incorporating assembly aspects associated with a product into the design of Cellular manufacturing System (CMS). The literature on CMS design implicitly assumes that finished part is the end product by itself. In practice, often, manufacturers produce parts which are assembled into a finished product. The methodology employs a part–subassembly matrix derived from the product structure in addition to the part–machine matrix. A mathematical programming model is developed which determines an assignment of parts, machines and subassemblies to manufacturing cells. The proposed model employs a new similarity coefficient between part, machine and subassembly. The model resulted in a nonlinear program with 0-1 variables. A case study has been analyzed based on a published part–machine matrix and a randomly generated product structure. The analysis reveals that it may be required to forego some of the efficiencies of Group Technology (GT) in order to achieve integration of assembly operations with production of parts. From a practical stand point of view it is preferred to have a system design which has a mix of GT and integration efficiencies, compared to a design which outperforms on GT criteria and completely lacks integration of assembly operations with production of parts.     

Defining relevant distances between server workloads

Computer system design studies traditionally involve only small collections of benchmarks. Detailed benchmark analysis is extremely time consuming and requires a large amount of human and machine resources. Therefore, it is essential that the benchmark collection be representative of the customer workloads for which an architecture is developed. In recent work, interworkload distances have been proposed as a way of characterizing workload similarity. These distances are based on measurable/computable program characteristics, such as instruction mix or dependence distance. In the literature, these characteristics enter the distances symmetrically. We observe that the program behavior impact of different characteristics varies significantly. We propose a method of estimating the program behavior impact via a regression model. Its components then enter the distance definition directly, thus emphasizing high-impact characteristics. We also propose a data collection methodology that can be deployed at a customer site without requiring code instrumentation and/or a detailed simulation setup. We build a dataset consisting of 84 program characteristics for each of the 106 workloads and apply the proposed distance methodology to it.     

Grouping part/product variants based on networked operations sequence

Operations flow based similarity is an important criterion for grouping variants. Similarity coefficient for product variants with networked sequence of operations has not been considered in the literature. Previously proposed similarity coefficients, which are based on operation/assembly sequence, focused on variants with serial operations sequences where the order of processing operations is fixed; while in practice, there are many part/product variants with flexible operations sequence options. A novel similarity coefficient for part/product variants is proposed based on the networked operations sequence similarity inspired by the analysis used in the field of biology (e.g. enzymes structures comparison). An extension of the proposed coefficient is also presented with an example for illustration. A more comprehensive similarity coefficient is developed by including operations similarity and production volume criteria. The popular operations similarity coefficient, called Jaccard's similarity, is applied and extended. A new coefficient using volume similarity criterion is also developed. Part/product variants are then clustered and grouped based on the combined similarity coefficient using the average linkage clustering (ALC) algorithm. The main applications of the proposed similarity coefficient are addressed. The grouped variants are sequenced as a secondary application of the proposed similarity coefficient. The sequence obtained from the proposed approach is compared with that obtained from a developed mathematical model. The result shows the accuracy of the proposed sequencing approach and can serve as a good preliminary sequence. A case study is also provided for demonstration.     

Extension of the target cascading formulation to the design of product families

The target cascading methodology for optimal product development is extended to product families with predefined platforms. The single-product formulation is modified to accommodate the presence of shared systems, subsystems, and/or components and locally introduced targets. Hierarchical optimization problems associated with each product variant are combined to formulate the product family multicriteria design problem, and common subproblems are identified based on the shared elements (i.e. the platform). The solution of the overall design problem is coordinated so that the shared elements are consistent with the performance and behaviour of the product variants. A simple automotive design example is used to demonstrate the proposed methodology.     

基于结构熵权 TOPSIS 法的产品设计方案评估研究

为了提高企业在产品设计方案评估阶段进行合理决策的效率,提出了一种基于结 构熵权双基点法(TOPSIS)的产品设计方案评估方法。在评估流程中,首先确定产品设计方案评 价指标,专家对各评价指标进行重要度排序,在得到了“典型排序”后经过结构熵计算和“认识盲 度”计算,得出各评价指标的权重,以替代双基点法中专家主观设定的权重系数;其次,构建设 计方案各指标加权标准决策的矩阵,进而求得各指标的理想解,基于以上可得到各设计方案距 离理想解的相对贴近度,经对比后确定设计方案的排序情况,从而得到各方案的优劣。最后, 将该评估流程应用于录音笔设计方案评估的决策中,验证了该方法的可行性与有效性。     

Integrated product line design and supplier selection: A multi-objective optimization paradigm

Product line design is commonly used to provide higher product variety for satisfying diversified customer needs. To reduce the cost and development time and improve quality of products, companies quite often consider sourcing. Conventionally, product line design and supplier selection are dealt with separately. Some previous studies have been attempted to consider product line design and supplier selection simultaneously but two shortcomings were noted. First, the previous studies considered several objectives as a single objective function in the formulation of optimization models for the integrated problem. Second, positions of product variants to be offered in a product line in competitive markets are not clearly defined that would affect the formulation of marketing strategies for the product line. In this paper, a methodology for integrated product line design and supplier selection is proposed to address the shortcomings in which a multi-objective optimization model is formulated to determine their specifications and select suppliers for maximizing the profit, quality and performance as well as minimizing the cost of the product line. In addition, joint-spacing mapping is introduced to help estimate market share of products and indicate positions of product variants. The proposed methodology can provide decision makers with a better tradeoff among various objectives of product line design, and define market positions of product variants explicitly. The results generated based on the methodology could help companies develop product lines with higher profits, better product quality and larger market share to be obtained. A case study of a product line design of notebook computers was performed to illustrate the effectiveness of the proposed methodology. The results have shown that Pareto optimal product line designs and the specifications of product variants can be determined. Suppliers of components and modules can be selected with considerations of minimum sourcing cost, and maximum performance and quality of product variants. Prices and positions of the product variants can also be determined.     

Product interface reengineering using fuzzy clustering

Reducing interface redundancy and regularizing interface form and dimension can help improve production batch and design originality if the interfaces can be reengineered, and be shared and remain constant from product to product, within a given product family. This paper investigates a methodology for achieving the goal. First, the interface reengineering problem is formalized, and similarity degrees between interfaces are measured by Euclidean proximity through considering a rule of interface similarity between modules or parts in the isostructure property, geometry property, precision property, and performance property. Second, a similarity matrix is introduced to identify the relationships between the interfaces, and steps of interface reengineering are advanced based on fuzzy clustering. Third, detailed analysis and adjustment of interface reengineering are discussed. Finally, the proposed methodology is effectively demonstrated in an instance of motorcycle–hydraulic-disk brake interface reengineering.