A framework for product data

IT support for engineering involves the integration of existing, evolving and future product data, and software that processes that data. Thus, there is increasing interest in the representation of product data in the computer to support CAE applications. To avoid duplication and inconsistency, and to support the use of new implementation technology as it emerges, conceptual models of product data are required. Such models are independent of the software and hardware environments in which they are implemented. System architectures to support the integration of applications at implementation time are becoming an accepted part of engineering information systems. To use these software support environments effectively, integrated product data is required. It must also be possible to extend the integrated product data in a controlled fashion if it is to evolve to support future engineering applications effectively. A framework that is a part of the product data at the conceptual modeling stage helps to satisfy these requirements. The framework presented is a structure for the information content of product data rather than for the implementation of such data. Product data based on the framework can be successfully implemented in a number of different database forms. This paper describes a framework for electromechanical product data that has been implemented in a structure editor and is being used to support a range of engineering applications. The process of product data integration can be improved by using existing integration strategies together with a framework that provides an overall organization for the data     

An approach for capturing the Voice of the Customer based on Virtual Prototyping

The need for companies to improve their competitiveness may lead to innovation and the reconceptualization of traditional products and processes, with companies making an effort to enhance product elements related to functionality, attractiveness, technology and sustainability, and implementing mass-customisation concepts. Mass-customised products are developed to satisfy specific customer needs, in line with increasing demand for product variety and customisation. The analysis of what customers really want, capturing the Voice of the Customer (VOC), is one of the strategies used to establish effective product development processes. Using a VOC survey, it is possible to transform customer needs into the functional and psychological requirements of the product. This paper presents a methodology based on Virtual Reality (VR) technologies to support the capturing of the VOC in regard to the visual, haptic and auditory characteristics of products. This method can be applied to the beginning of the product development process, to allow companies to deduce from the data the requirements of new industrial customised products. A flexible and interactive Virtual Prototype (VP) of a product category is then developed as a product platform in a draft version by designers and configured according to customer needs, using an immersive VR environment. This method, based on the use of VP, reduces the number of physical prototypes that need to be manufactured during the product development process, thus reducing overall costs. In addition, the VP based method supports the mass-customisation process of products through the real-time integration and collection of data for product configuration preferences, involving as many users as possible representative of the target users of the new products. To demonstrate this process a case study concerning the development of the VP for a washing machine, a summary of test sessions with users and results are presented. Specifically, the results presented in this paper are related to improvements in capturing the VOC and reductions in Virtual Prototyping cost and time.     

A visual-inspection system using a self-organizing map

Visual inspections by hand often cause bottlenecks in production processes in industries. Therefore, it is desirable to be mechanized and automated. In order to satisfy these requirements, we apply image recognition using a self-organizing map (SOM) to visual inspection equipment. The SOM maps high-dimensional input data onto a low-dimensional (typically two-dimensional) space. Through the mapping, the data are automatically clustered based on their similarity. Any unknown data which are input onto the self-organized map are also mapped onto it according to their similarity. The categories of the unknown data are thus recognized based on their positions on the map. The reason we use a SOM for inspections is that users can then know the similarity distribution of all data at a glance on the map, and understand the mechanism of the recognition visually. We have developed a visual inspection system using a SOM, and have evaluated it using actual product images. We have obtained high recognition accuracies of 98% and 96% for one- and two-inspection-point tests, respectively, for a real industrial product.     

An internet retailing data framework for supporting consumers and business processes

Abstract. This paper describes an action research case study dealing with the development of an internet retail store with advanced capabilities. The research focuses on the exploitation of information generated through consumer–system interaction by the virtual retailer (i.e. sales, navigation and personal consumer data). The results effectively support the business processes, consumers and product suppliers. To that end, an internet retailing data framework was developed during the user requirements capturing phase of the ACTIVE (Advertising and Commerce Through the Internet in the context of the Virtual Enterprise) project, as a means of structuring the system's information requirements and data processing mechanisms. The purpose of this framework is to describe in detail what consumer information the system needs to collect and to advise the virtual retailer (i.e. ACTIVE's administrator) how to use the available software components to exploit this information in order to effectively support consumers and business processes. Despite the fact that the proposed framework is tailored to the ACTIVE characteristics and needs, it can also be utilized by other virtual retailers to effectively support their key stakeholders (i.e. customers and suppliers).     

A fuzzy QFD approach to determine supply chain management strategies in the dairy industry

The aim of this study is to identify the crucial logistics requirements and supply chain management (SCM) strategies for the dairy industry. For product or service development, quality function deployment (QFD) is a useful approach to maximize customer satisfaction. The determination of design requirements and supply chain management strategies are important issues during QFD processes for product or service design. For this reason, a fuzzy QFD methodology is proposed in this study to determine these aspects and to improve customer satisfaction. Qualitative information is converted firstly into quantitative parameters, and then this data is combined with other quantitative data to parameterize two multi-objective mathematical programming models. In the first model, the most important logistic requirements for the company are determined based on total technical importance, total cost, total feasibility and total value increment objectives, and in the second model, based on these objectives, appropriate supply chain management strategies are determined. Finally, a case study from the Turkish dairy industry is given to illustrate the proposed approach.     

An object-oriented product database using ROSE

A product database for a design and manufacturing enterprise serves as a repository for the data needed to manufacture a product. This paper discusses how an object-oriented persistent object manager named ROSE, under development at Rensselaer, has been used in the Rensselaer CIM program to develop a STEP/PDES-compliant product database for its research test bed. In so doing, it provides an overview of the requirements for a product database system in an integrated design and manufacturing facility, and it discusses how the chosen system meets these requirements.     

A method for computer-aided specification of geometric tolerances

The paper describes a method for the generation of tolerance specifications from product data. The problem is nontrivial due to the increasing adoption of geometric dimensioning criteria, which call for the use of many types of geometric tolerances to completely and unambiguously represent the design intent and the many constraints deriving from manufacturing, assembly and inspection processes. All these issues have to be modeled and explicitly provided to a generative specification procedure, which may thus need a large amount of input data. The proposed approach tries to avoid this difficulty by considering that most precision requirements to be defined relate to the assembly process, and can be automatically derived by analyzing the contact relations between parts and the assembly operations planned for the product. Along with possible user-defined additional requirements relating to function, assembly requirements are used in a rule-based geometric reasoning procedure to select datum reference frames for each part and to assign tolerance types to part features. A demonstrative software tool based on the developed procedure has allowed to verify its correctness and application scope on some product examples.     

Quantifying the impact of AIDC technologies for vehicle component recovery

Recovering value from end-of-life vehicles (ELV) has become increasingly important in recent years due to legislative pressures. In this context, cannibalisation of valuable components for possible reuse in secondary markets is becoming a popular option. However, recovery processes are characterised by high levels of uncertainty regarding the quality of components returned at their end-of-life. Hence, the key to making component recovery an cost-effective option is to provide more information regarding the condition of components in the ELV to the recoverer. Emerging automated identification and data capture (AIDC) technologies such as RFID tags and sensor networks can have significant impact on the effectiveness with which product information is generated and shared among the various actors in the product lifecycle. This paper illustrates how decision-making during product recovery can be improved by collecting critical usage data during a vehicle’s lifecycle and making it readily available to key decision-makers. In particular, we use a probabilistic model to quantify the benefits of automated information capture technologies in improving product recovery processes.     

Effects of visual and textual information in online product presentations: looking for the best combination in website design

Online product presentation constitutes a key challenge for marketers and designers who want to satisfy consumers’ needs. Visual and textual product information affect recognition and knowledge about the product. This research examines whether the presence (vs absence) of a product image and textual information in a schematic mode (vs paragraph mode) affect users’ recall and perceptions of the quality of the electronic products’ information. The moderating role of user's familiarity with the website and product also may play a role; the evolution of these variables likely will determine trends in e-commerce research. The results show that a schematic display of textual information improves perceptions of information quality. When a picture of the product appears together with textual information, users remember more information and consider it easier to remember if that information appears schematically. However, without a product picture, users allocate more resources to process paragraph information and therefore recall more information and perceive it as easier to recall. The degrees of familiarity also have important directional effects on these relationships.     

中文网络评论的产品特征提取及情感倾向判定

随着互联网和电子商务的发展,用户在购买或使用商品之后会在网络站点上发表对产品的评论,大量的产品评论中所包含的丰富信息,可以为生产厂商和用户提供重要的决策依据。基于文本的语义和语言分析,提出了从产品评论中提取用户关注的产品特征的方法,并根据用户的关注程度对产品特征进行排序;同时,根据观点词的极性值判定用户对产品特征的情感倾向以及情感倾向强度。本研究采用从互联网上获得的针对笔记本电脑的产品评论作为实验对象,实验结果初步证明该方法具有良好的准确率和召回率。