可调节产品族的自底向上优化再设计方法

在建立可调节产品族优化模型的基础上,基于独立优化、平台规划、实例产品设计和方案评价的产品族自底向上设计流程,提出基于敏感度分析和模糊聚类的产品族优化再设计方法.在产品平台规划阶段,首先使用变量敏感度分析初步划分可能的平台常量和可调节变量集合;然后对实例产品间由于变量通用所造成的性能、成本和约束满足变化进行模糊C均值聚类,并引入模糊覆盖指数决定最优的聚类数目,合理规划多平台常量的共享.通过建立平台非通用性指数以定量评价产品族方案的通用程度.最后采用通用电机产品族的再设计实例,证明了该方法的正确性与高效性.     

一种基于模糊聚类和粗糙集的产品族设计知识约简方法*

针对产品族设计中产品族共性与产品个性化之间相互冲突的问题,提出了一种基于模糊聚类和粗糙集的产品族设计知识约简方法。通过模糊聚类对设计参数进行离散化和分类,以粗糙集进行知识约简区分出产品族设计参数和个性化产品设计参数,实现产品族综合性能损失最小。通过对通用电机产品族的实例分析,并经与PPCEM方法比较,验证了该方法的高效性和可行性。     

产品族设计与延迟制造过程决策的主从关联优化

在市场全球化的进程中,延迟仍然是当今企业降低供应链风险的一种有效策略.然而,当前对延迟的研究往往是基于预先已固定好的产品族架构,较少关注到产品族设计与延迟制造过程决策间存在的内在固有耦合关系.鉴于此,提出对这二者的一种主从关联优化方法.首先,通过构建二者间的主从交互评价机制,建立以产品族设计为上层优化、延迟制造过程决策为下层优化的非线性双层规划模型:模型上层为设计产品族架构和决策延迟产品模块类型,从而最大化单位成本的顾客效用;下层分别为非延迟和延迟产品模块决策最优的制造方式以及为终端产品决策最优的组装方式,从而最小化工程成本.然后,设计一种嵌套式遗传算法对模型进行求解,以智能冰箱产品族延迟制造案例验证所提出模型和算法的可行性.最后,通过设计一种嵌套GAPSO算法对嵌套式遗传算法进行改进,并对比分析两种算法的计算过程和结果.     

基于混合移植的复杂产品多尺度设计求解

针对订单型复杂产品在大规模定制过程中存在着的重复求解和实例冗余问题,提出一种基于产品多尺度混合移植的建模方法,并对应用该建模方法的订单型复杂产品的配置设计进行研究.首先在产品族配置平台上建立了配置需求域、配置实例域、配置模型域三者之间的多尺度映射关系,形成产品族映射域;然后把客户需求转化为多尺度配置需求输入到产品族映射域,获取对应尺度的实例和无对应尺度实例的模型进行多尺度混合移植,得到动态配置模型;最后依据多尺度配置需求、配置规则和配置实例对动态配置模型进行多尺度层次求解,得到整体配置方案.该方法已应用在电梯产品全生命周期数字化设计平台中,提高了配置求解的效率和实例的重用率,证明了其在工程应用中的正确性和高效性.     

面向用户认知的汽车产品族前脸造型设计方法

为了帮助新兴的自主汽车企业形成家族特征前脸,提高消费者的产品认知 度,提出了面向用户认知的汽车产品族前脸造型设计的方法。阐述了汽车前脸造型及产品族 的定义、概念与作用,并将具体设计过程划分为完形设计、识别特征设计与细节设计3 个步 骤,进一步阐释了前脸造型特征在产品族中运用的方式,最后以实例进行了说明。     

基于环境资源因子的产品平台模块划分方法

产品平台能够根据用户的个性化需求,利用模块化的零部件快速衍生出不同产品.通过分析基于环境资源因子的产品平台模块划分方法,将绿色理念贯穿产品全生命周期过程中,提出基于环境资源属性的产品集成信息模型.通过引入环境资源因子和零部件关联矩阵,分别建立了基于环境资源因子的绿色度目标函数和基于零部件关联矩阵的聚合度目标函数;提出一种改进的免疫克隆多目标优化算法,对二进制基因对进行变异与跟踪,在克隆选择中筛选出满足环境资源属性的非支配抗体,对产品平台的模块化分问题进行多目标优化求解,构建出能够量化评估产品的环境资源属性的产品平台,从而在满足客户对产品功能需求的同时提高了产品对环境的友好性.最后以采煤机为应用实例,通过与其他算法的对比以及与绿色影响程度的分析评价,对该方法进行了验证.     

面向对象表达产品族实例知识的智能化快速设计

提出了智能化快速设计系统的总体框架，描述了以层次分解树表达产品族设计模型，给出了实例知识的存储结构和检索策略规则，实现了基于产品族实例知识的快速设计方法并给出了具体应用实例。     

基于多色集合的大规模产品定制形式化解决方法

为了满足大规模定制中客户的个性化要求, 以产品族设计为研究对象, 提出了基于多色集合层次理论的产品族配置建模方法。利用元素、个人颜色、统一颜色等概念, 建立产品族模型与客户需求之间的映射关系, 提供产品族元素选择的形式解决方案, 实现用户需求到不同产品组合方案的推理过程。实例验证结果表明, 该建模方法简便易行, 便于形式化表述和软件实现。     

PDM环境下基于实例推理的变型设计技术研究

针对建立实例库时存在的问题,对基于实例推理的变型设计进行了研究,提出了在PDM环境下建立实例库的方法.分析了实例推理的一般过程,提出了一种基于实例推理的变型设计求解模型,在此基础上, 以汽车产品为例,提出了一种变型设计的检索方法,为实现汽车产品的变型设计奠定了基础.     

面向公共产品平台通用化的聚类分析方法研究

提出了一个平台体系结构的分层构造框架及一种平台元素的获取方式——基于图论的聚类分析方法(GTBCA)．针对企业现有相似产品进行通用性和标准化分析，并以此作为构筑公共平台的基础，辅助设计人员对平台进行合理规划和设计，从而有效地开发产品族，满足大批量定制生产的要求．最后，用一个抽油机的实例说明了文中方法的可操作性和有效性．     

A systematic data-mining-based methodology for product family design and product configuration

Product family design and product configuration based on data mining technology is identified as an intelligent and automated means to improve the efficiency of product development. However, few of previous literatures have proposed systematic product family design method based on data mining technology. To make up for this deficiency, this research put forward a systematic data-mining-based method for product family design and product configuration. First, the customer requirement information and product engineering information in the historical order are formatted into structural data. Second, principal component analysis is performed on historical orders to extract the customers' differentiated needs. Third, association rule algorithm is introduced to mine the rules between differentiated needs and module instances in the historical orders, thus obtained the configuration knowledge between customer needs and product engineer. Forth, the mined rules are used to construct association rule-based classifier (CBA) that is employed to sort out the best product configuration schemes as popular product variants. Fifth, sequence alignment technique is employed to identify modules for popular product variants, so that the module instances are divided into optional, common and special module, respectively, thereby the product platform is generated based on common modules. Finally, according to new customer needs, the CBA classifier is used to recommend the best configuration schemes, and then popular product variants are configured based on the product platform. The feasibility of the proposed method is demonstrated by the product family design example of desktop computer hosts.     

Automated development of neural net analysis and optimization expert

The envisioned role of an Automated Intelligent Designer Associate (AIDA) is to help the creative designer to conveniently explore his design concepts. Such a capability should be able to learn and improve its performance by accumulating experience. As a step towards an AIDA capability, Artificial Neural Nets are shown to capture structural analysis and optimization domain expertise and provide quick estimates of structural behavior and of optimum designs within the domain of their training. In the approach discussed here, an automated procedure generates random instances of structural analysis or optimization “experiences” within a design domain, extracts training patterns, constructs and trains an appropriate network architecture. The final product is a trained neural net that can estimate analysis or optimization results for given design conditions within the domain of its “expertise”.     

Product platform design through sensitivity analysis and cluster analysis

Scale-based product platform design consists of platform configuration to decide which variables are shared among which product variants, and selection of the optimal values for platform (shared) and non-platform variables for all product variants. The configuration step plays a vital role in determining two important aspects of a product family: efficiency (cost savings due to commonality) and effectiveness (capability to satisfy performance requirements). Many existing product platform design methods ignore it, assuming a given platform configuration. Most approaches, whether or not they consider the configuration step, are single-platform methods, in which design variables are either shared across all product variants or not shared at all. In multiple-platform design, design variables may be shared among variants in any possible combination of subsets, offering opportunities for superior overall design but presenting a more difficult computational problem. In this work, sensitivity analysis and cluster analysis are used to improve both efficiency and effectiveness of a scale-based product family through multiple-platform product family design. Sensitivity analysis is performed on each design variable to help select candidate platform design variables and to provide guidance for cluster analysis. Cluster analysis, using performance loss due to commonization as the clustering criterion, is employed to determine platform configuration. An illustrative example is used to demonstrate the merits of the proposed method, and the results are compared with existing results from the literature.     

A manufacturing-oriented approach for multi-platforming product family design with modified genetic algorithm

With highly fragmented market and increased competition, platform-based product family design has been recognized as an effective method to construct a product line that satisfies diverse customer’s demands while aiming to keep design and production cost-effective. The success of the resulting product family often relies on properly resolving the inherent tradeoff between commonality across the family and performance loss. In this paper, a systematic multi-platforming product family approach is proposed to design a scale-based product family. In the light of the basic premise that increased commonality implies enhanced manufacturing efficiency, we present an effective platform decision strategy to quantify family design configuration using a commonality index that couples design varieties with production variation. Meanwhile, unlike many existing methods that assume a single given platform configuration, the proposed method addresses the multi-platforming configuration across the family, and can generate alternative product family solutions with different levels of commonality. A modified genetic algorithm is developed to solve the aggregated multiobjective optimization problem and an industrial example of a planetary gear train for drills is given to demonstrate the proposed method.     

Green product design through product modularization using atomic theory

With increasing environmental consciousness and the establishment of environmental protection regulations, green product design not only plays a crucial role in a modern industry but is also becoming the main focus of the future market. In this paper, an innovative method is presented that uses the concepts of atomic theory to solve design modularization problems for green product design. With the developed method, products can be modularized based upon given green constraints, e.g., material compatibility, part recyclability, and part disassemblability. The developed method can help engineers effectively create green designs in the initial design stage, based on product lifecycle requirements. With green considerations incorporated into new modules, a new design can be created that improves upon an original design, with respect to environmental impacts. Product designers can use our method to compare differences between their original designs and the new green modules and then perform necessary design modifications. A table lamp and a motor are used as case study examples to show the effectiveness of the atomic-theory-based green product design method.     

Comparing classical performance measures for a multi-period,two-echelon supply chain network design problem with sizing decisions

This paper addresses a new problem to design a two-echelon supply chain network over a multi-period horizon. Strategic decisions are subject to a given budget and concern the location of new facilities in the upper and intermediate echelons of the network as well as the installation of storage areas to handle different product families. A finite set of capacity levels for each product family is available at each potential location. Further decisions concern the quantities of products to be shipped through the network. Two mixed-integer linear programming models are proposed that differ in the type of performance measure that is adopted to design the supply chain. Under a cost minimization objective, the network configuration with the least total cost is to be determined. In contrast, under a profit maximization goal the aim is to design the network so as to maximize the difference between total revenue and total cost. In this case, it may not always be attractive to completely satisfy demand requirements. To investigate the implications that the choice of these performance measures have on network design, an extensive computational study is conducted with randomly generated instances that are solved using CPLEX.     

Joint optimization of product tolerance design,process plan,and production plan in high-precision multi-product assembly

With the ever-increasing product variety faced by the manufacturing industry, investment efficiency can only be maintained by the application of multi-product assembly systems. In such systems, the product design, process planning, and production planning problems related to different products are strongly interconnected. Despite this, those interdependent decisions are typically made by different divisions of the company, by adopting a decomposed planning approach, which can easily result in excess production costs. In order to overcome this challenge, this paper proposes an integrated approach to solving the above problems, focusing on the decisions crucial for achieving the required tolerances in high-precision assembled products. The joint optimization problems related to product tolerance design and assembly resource configuration are first formulated as a mixed-integer linear program (MILP). Then, a large neighborhood search (LNS) algorithm, which combines classical mathematical programming and meta-heuristic techniques, is introduced to solve large instances of the problem. The efficiency of the method is demonstrated through an industrial case study, both in terms of computational efficiency and industrial effectiveness.     

Anomaly detection and critical attributes identification for products with multiple operating conditions based on isolation forest

Performance analysis of the existing mechanical products is critical to identifying design defects and improving product reliability. With the advances of information technologies, product operating data collected through continuous condition monitoring (CM) serve as main sources for analysis of performance and detection of anomaly. Most of the existing anomaly detection methods, however, are not effective when CM data are very high dimensional, leading to poor quality of assessment results. Besides, the effects of multiple operating conditions on anomaly detection are seldom considered in these existing methods. To solve these problems, an integrated approach for anomaly detection and critical behavioral attributes identification based on CM data is developed in this research. Gaussian mixed model GMM) is employed to develop a method for clustering of operating conditions. Isolation forest (iForest) method is used to detect anomaly instances, and further to identify the critical attributes related to product performance degradation. The effectiveness of the developed approach is demonstrated by an application with collected operating data of a wind turbine.