大规模定制环境下基于模块化的产品制造时间制定研究

针对大规模定制环境下产品制造时间受定制因素影响而无法准确获得的问题, 对定制产品制造时间的制定进行了探索研究。基于模块化思想将产品制造时间按层级划分为特征时间模块（物理时间模块、逻辑时间模块）和子特征时间模块（作业时间模块、定制时间模块）, 采用基于案例推理技术确定作业时间模块定额, 分析定制因素对制造时间的影响机理并构建定制时间模块定额模型, 通过对特征时间模块的集成研究得到产品制造时间的数学模型。最后, 以某公司G变压器的制造为例验证了该模块化制造时间制定思路和方法的正确性。     

大规模定制环境下的工程机械模块化设计策略

随着客户对于工程机械产品多样化和个性化的要求越来越高,工程机械制造企业必须在最短时间内设计出适应客户要求的产品,才能够迅速抓住快速多变的市场。因此本文在工程机械制造企业引入大规模定制理念的基础上,研究模块化设计思想在大规模定制环境下的新特点和新变化,提出在此环境下的工程机械模块化设计策略,以达到缩短定制产品的开发周期,快速响应客户和市场,提高企业核心竞争能力的目的。     

基于3D打印的订单管理系统分析与设计

使用3D打印快速成型技术,为用户定制专属产品,可以快速满足用户个性化的需求,采用配置设计和调整的设计方法,可以实现模块化和系统化的设计生产。因此,笔者提出了一种基于私人定制的3D打印订单系统分析设计。该系统要解决的是为满足更多人的个性化设计需求所面临的问题,可以满足私人定制3D打印系统的基本要求。系统能根据用户的不同需求,直接下单定制和设计人员反馈意见并进行调整修改,快捷方便地为用户提供服务,此次研究的课题为私人定制的3D打印订单管理系统方面的研究提供了一定的借鉴。     

面向MES的服装大规模定制系统

信息化的发展,使大规模定制开始进入服装制造产业. 在服装大规模定制系统实施中,势必要面临如何组织与管理各种差异化的服装产品共线协同生产的组织和管理问题. 本文在服装大规模定制系统的研究中采用的解决方案是,围绕产品族和服装模块化,构建面向MES的企业生产资源知识库,采用MES管理异构产品的共线生产.     

基于遗传算法的产品基因重组设计

针对客户定制产品设计灵活性差的问题,提出一种基于遗传算法的产品基因重组的设计方法。在产品的重组 设计中引入基因工程,利用遗传算法对现有的产品设计方案进行优化,以满足制造业客户定制产品的多样化需求,实现客户定 制产品的智能设计。以某机械制造企业的数控机床为例,完成了遗传算法的关键技术：适应度函数、选择算子、交叉算子的设 计开发,验证了该方法的可行性和有效性。     

云制造环境下大规模产品定制技术研究

结合云制造及大规模产品定制的特点,提出了一种云制造环境下大规模产品定 制的组织模式以及相配套的需求转换模型。需求转换模型涵盖了完整的需求分解、重组和再 分配过程,对其中的基于产品结构树的需求分解、基于相似性聚类的制造批次重组都进行了 详细阐述,并构建了需求再分配的非线性规划模型,从而可以利用最优化方法实现制造需求 寻优分配。最后,以轮式装载机产品定制为示例,论述了需求转换的详细过程,验证了方法 的有效性和可行性。     

面向定制化牙科产品的协同设计系统的研究

为了快速设计和制造定制化牙科产品,结合牙科产品的特点,分析了定制化牙科产品协同设计系统中各组织间的协同作用、产品协同设计过程以及协同设计系统的结构;以定制化托槽为例,研究了结合托槽设计及选区激光熔化快速成型的协同设计系统的产品设计制造流程,运用统一建模语言建立了定制化托槽的协同设计过程.实例表明,采用该系统,牙科产品开发商与牙科医生协作实现快速设计并制造定制化产品.     

面向订单的模块化时间定额的确定方法*

现代制造环境下,生产模式从批量生产逐渐变为面向订单的定制生产.由于定制产品的重复度低,生产数量少,使得传统的工时定额方法已经无法有效地对其工时定额进行快速、精确的计算.由此,根据成组技术和模块化思想提出了一种新的工时定额方法.该方法首先结合产品特点对定制产品进行模块划分;根据定制特点建立通用模块和定制模块;再由通用模块...     

3D设计和仿真工具开启亚洲制造业新篇章

亚洲制造企业即将迎来一场新的工业革命,以创新为催化剂,重新定义企业的成功之路。然而伴随巨大机遇而来的是严峻的挑战--当前亚太地区的制造业正受到诸多强有力的因素牵制。面对缩短产品上市时间、设计高度定制化和环保节能等压力,企业必须重新思考其制造战略。     

客户驱动的大批量定制模型研究

针对具有模块化特性的产品大批量定制问题，提出了一种以客户驱动为基础的大批量定制模型．通过客户驱动参数描述、参数映射、模块特性参数编码规则的建立，得出定量描述的数学模型，从而使产品定制问题转变为模块的组合优化问题．该模型正在汽车产品的客户定制中进行实验研究．     

大批量定制产品的动态组织管理方法研究

用户的个性化需求决定了最终定制产品的多样性,多样化的定制产品的有效管理与组织生产是大批量定制生产的重要内容。研究了大批量生产模式下个性化产品(Customized Product for Mass Customization,CPMC)的数据模型,提出了基于产品属性信息的CPMC多维视图管理方法,将定制产品不同层次的属性信息投影到树型的数据结构从而实现CPMC的动态组织管理。最终,以定时器定制产品的管理为例对上述管理方法进行了实践。     

基于混流装配模式的MC/MP供应链优化模型*

基于混流装配模式,分析了MC/MP供应链的结构,给出了MC/MP供应链的优化模型。研究结果即针对单位周期内定制产品、标准产品的需求以及定制产品对标准产品需求量的影响,优化各种产品的装配计划和配送计划。通过算例验证了该模型的合理性和有效性。     

Product platform design to improve commonality in custom products

Many companies find it difficult to maintain commonality and economies of scale in products with strict customer design requirements that may vary greatly from contract-to-contract or piece-to-piece. These strict and varied requirements typically result in highly customized products that are costly to manufacture, involve small production runs, and require long delivery times. In this paper, we discuss how the strategic incorporation of product platforms into the design process can leverage the design effort of individually customized products. The example involves the design of cross-sections for yokes used to mount valve actuators in the nuclear power industry. Through this example we demonstrate the process of creating a market segmentation grid, choosing a targeted segment, creating a product platform for the yoke cross-section, and subsequently defining the yoke product family using the product platform concept exploration method. The end result is a platform-based product family that will improve response to customer requests, reduce design and manufacturing costs, and improve time to market for companies that make small production runs of highly customized products.     

多媒体统一处理平台产品化定制的设计与实现

给出了基于网络多媒体开放系统的多媒体统一处理平台的产品化定制设计需求以及软件实现方法,介绍了该定制方法承担的任务,重点介绍了如何定制所需要的各类产品。该方法设计合理,使用方便,具有很强的实用性,对于计算机行业中其他领域产品的定制也具有指导作用。     

A KE-LSA approach for user-centered design

In today’s highly competitive business environment, most successful companies use ‘user-centered’ design techniques, to create products that meet user needs. Designers have used traditional user-centered design techniques, with some degree of success. However, in today’s market, companies need more accurate, more advanced user-centered design techniques, to mass produce customized products for individual users. This study introduces a new KE-LSA approach for user-centered design. Prior techniques used Kansei engineering (KE) for product design. Prior techniques used latent semantic analysis (LSA) for document indexing. The KE-LSA approach uses KE and a KE-LSA semantic space for individually customized product design. A case study is used to describe the KE-LSA approach. The case study uses the KE-LSA approach to match Kansei values for individual users’ needs to cell phone designs for each user. The case study also determines accuracy for matching KE-LSA designs to each user’s actual choice for a cell phone. In the case study, an iterative KE-LSA approach matched designs to users’ choices, with 82.3% overall accuracy. The iterative KE-LSA approach improved overall accuracy 11.4%, compared to a traditional iterative KE-QT1 approach. The KE-LSA approach improves matching accuracy for mass producing individually customized cell phone designs. Therefore, this study improves individually customized product design and product quality.     

Customer satisfaction evaluation method for customized product development using Entropy weight and Analytic Hierarchy Process

Providing customized products with high customer satisfaction (CS) has become an inevitable trend for modern customized companies to remain competitive. This study proposes a CS evaluation method for customized product development using Entropy weight and Analytic Hierarchy Process (AHP). Firstly, customer requirements are identified based on Voice-of-Customer (VoC), and classified into four categories (positive, negative, must-be and fuzzy attributes) according to the characteristics of CS criteria. Then, CS evaluation model and its solution method are introduced in detail. This study especially focuses on the quantitative methods for each category of the evaluation criteria through formulating several mathematical models, and criteria weights are obtained through integrating Entropy weight and AHP method. Finally, a case study of applying the proposed methodology to customized portrait-based product industry demonstrates the functionality of the proposed methodology.     

How online self-customization creates identification: Antecedents and consequences of consumer-customized product identification and the role of product involvement

Online self-customization (OSC) enables customers to design a product tailored to their preferences and needs via the online platform. This study mainly argues that a successful OSC experience goes beyond simply increasing a consumer’s preference fit; it provides an opportunity to develop a meaningful relationship with customers by allowing them to embed their sense of self into the customized products and thus identify themselves with the products. Consumer-customized product identification (C-C identification) was proposed as central to our understanding of why and under what conditions OSC processes enable consumers articulate their identities. This study is theoretically based on internal motivations from social identity theory and identification literature to develop a model. The model was tested using a scenario-based survey with respondents collected from Amazon’s Mechanical Turk. Structural equation modeling analyses showed that value congruence and distinctiveness of the customized products positively influenced C-C identification, which in turn positively influenced attitudinal responses. The results further showed that the relative impact of two antecedents on C-C identification varies with a consumer’s product involvement. A similar pattern of results was obtained in two product categories. Theoretical and managerial implications for OSC marketers are also discussed.     

Predictive models with endogenous variables for quality control in customized scenarios affected by multiple setups

The crescent demand for customized products has challenged industries with reduced lot sizes. As a result, frequent product model changing and short series of observable variables decreased the performance of many traditional tools used in process control. This paper proposes the use of endogenous variables in predictive models aimed at overcoming the multiple setup and short production runs problems found in customized manufacturing systems. The endogenous variables describe the type/model of manufactured products, while the response variable predicts a product quality characteristic. Three robust predictive models, ARIMA, structural model with stochastic parameters fitted by Kalman filter, and Partial Least Squares (PLS) regression, are tested in univariate time series relying on endogenous variables. The PLS modeling yielded better predictions in real manufacturing data, while the structural model led to more robust results in simulated data.     

A semantic-expansion approach to personalized knowledge recommendation

The rapid propagation of the Internet and information technologies has changed the nature of many industries. Fast response and personalized recommendations have become natural trends for all businesses. This is particularly important for content-related products and services, such as consulting, news, and knowledge management in an organization. The digital nature of their products allows for more customized delivery over the Internet. To provide personalized services, however, a complete understanding of user profile and accurate recommendation are essential.In this paper, an Internet recommendation system that allows customized content to be suggested based on the user's browsing profile is developed. The method adopts a semantic-expansion approach to build the user profile by analyzing documents previously read by the person. Once the customer profile is constructed, personalized contents can be provided by the system. An empirical study using master theses in the National Central library in Taiwan shows that the semantic-expansion approach outperforms the traditional keyword approach in catching user interests. The proper usage of this technology can increase customer satisfaction.