Developing a Higher-Cycled Product Design CAE Model： The Evolution of Automotive Product Design and CAE

To help corporations survive amidst worldwide quality competition, the authors have focused on the strategic development of a higher-cycled product design CAE model employing a highly reliable CAE analysis technology component model. Their efforts are part of principle-based research aimed at evolving product design and CAE development processes to ensure better quality assurance. To satisfy the requirements of developing and producing high quality products while also reducing costs and shortening development times, the effectiveness of this model was verified by successfully applying it to the technological problems of loosening bolts and other product design bottlenecks at auto manufacturers.     

面向微博平台的产品市场分析模型研究

通过对面向微博平台的产品市场分析过程进行抽象,提出了3层分析模型。第一层实现了数据准备模块,为兴趣分析提供规范化的数据。第二层通过情感分析提取微博平台上与产品相关的用户兴趣。第三层利用用户兴趣信息计算产品的满意度和关注度来表现产品市场形势。整个模型提供了面向微博平台搭建产品市场分析系统的快速解决方案。     

面向顾客选择的产品联合分析系统的研究与实现

运用联合分析原理建立数学模型,根据数学模型和项目情况进行系统分析设计,运用程序开发等相关技术实现在线产品分析系统,该系统提供给设计和销售人员进行产品调查和分析,从而提供产品各属性水平的效用值、重要性及产品潜在的市场占有率,以指导产品研发设计和产品进货选择.     

Valuing pilot projects in a learning by investing framework: An approximate dynamic programming approach

We introduce a general discrete time dynamic framework to value pilot project investments that reduce idiosyncratic uncertainty with respect to the final cost of a project. The model generalizes different settings introduced previously in the literature by incorporating both market and technical uncertainty and differentiating between the commercial phase and the pilot phase of a project. In our model, the pilot phase requires N$N$ stages of investment for completion. With this distinction we are able to frame the problem as a compound perpetual Bermudan option. We work in an incomplete markets setting where market uncertainty is spanned by tradable assets and technical uncertainty is idiosyncratic to the firm. The value of the option to invest as well as the optimal exercise policy are solved by an approximate dynamic programming algorithm that relies on the independence of the state variables increments. We prove the convergence of our algorithm and derive a theoretical bound on how the errors compound as the number of stages of the pilot phase is increased. We implement the algorithm for a simplified version of the model where revenues are fixed, providing an economic interpretation of the effects of the main parameters driving the model. In particular, we explore how the value of the investment opportunity and the optimal investment threshold are affected by changes in market volatility, technical volatility, the learning coefficient, the drift rate of costs and the time to completion of a pilot stage.     

Chemical deposition of nanocrystalline nickel oxide from urea containing bath and its use in liquefied petroleum gas sensor

Nanocrystalline nickel oxide (NiO) was deposited onto glass substrates using a chemical deposition method from a bath containing nickel (Ni²⁺) ions and urea at 363 K. The deposition process was based on the reaction between Ni²⁺ and hydroxide ions released from the protolysis of ammonia formed in the decomposition of urea heated at 363 K, which caused to form nickel hydroxide. The structural properties of nickel oxide films were studied by means of X-ray diffraction (XRD) and scanning electron microscopy (SEM). The XRD analyses showed that nanocrystalline nature remained after heating at 523 K for 2 h. Surface morphology of the nickel oxide film showed worm-like mesoporous structure with pore size in a nanometer range. The chemically deposited nickel oxide films were effectively used as a liquefied petroleum gas (LPG) sensor and the maximum response of 36.5% was recorded on exposure to 0.3 vol% of LPG at 698 K.     

Influence of Pd doping on morphology and LPG response of SnO2

In the present study nanocrystalline pristine and Pd-doped SnO₂ (Pd:SnO₂) with various mol% Pd have been synthesized by a modified Pechini citrate route. Transmission electron microscopy and X-ray powder diffraction studies were used to characterize the morphology, crystallinity, and structure of the SnO₂ and Pd:SnO₂. The response of the pristine SnO₂ and Pd:SnO₂ was studied towards different reducing gases. The 1.5 mol% Pd doping showed an enhanced response of 75 and 95% towards LPG at as low as 50 and 100 °C, respectively, which were quite large high value as compared with pristine SnO₂ (38 and 35% at 50 and 100 °C, respectively). Structural characterization revealed that Pd doping reduced the crystallite size of SnO₂ and helps in the formation of distinct spherical nanospheres at a calcinations temperature of 500 °C. Thus the increase in LPG response can be correlated with the spherical morphology, a decrease in the crystallite size (11 nm) due to doping with Pd as compared with the pristine SnO₂ (26 nm) and main role of Pd as a catalyst.     

Advanced product planning: a comprehensive process for systemic definition of new product requirements

This paper reports results of research into the definition of requirements for new consumer products––specifically, electro-mechanical products. The research dealt with the derivation of design requirements that are demonstrably aligned with stakeholder needs. The paper describes a comprehensive process that can enable product development teams to deal with statements of product requirements, as originally collected through market research activities, in a systematic and traceable manner from the early, fuzzy front end, stages of the design process. The process described has been based on principles of systems engineering. A case study from its application and evaluation drawn from the power sector is described in this paper. The case study demonstrates how the process can significantly improve product quality planning practices through revision of captured product requirements, analysis of stakeholder requirements and derivation of design requirements. The paper discusses benefits and issues from the use of the process by product development teams, and identifies areas for further research. Finally, the conclusions drawn from the reported research are presented.

Prediction of vehicle reliability performance using artificial neural networks

Product development is an important but also dynamic, lengthy and risky phase in the life of a new product. The optimisation of the product development phase through extensive knowledge of the involved procedures is believed to reduce the risks and improve the final product quality. Artificial intelligence and expert systems have been used successfully in optimising the development phase of some new products as it will be demonstrated by the first sections of this publication. This paper presents the first module of an expert system, a neural network architecture that could predict the reliability performance of a vehicle at later stages of its life by using only information from a first inspection after the vehicle’s prototype production. The paper demonstrates how a tool like neural networks can be designed and optimised for use in reliability performance predictions. Also, this paper presents an optimisation methodology that enabled the neural network to deal with the limited amount of available training data, common during new product development, and to finally achieve acceptable prediction performance with small error. A case example is presented to demonstrate the methodology.     

A dynamic workflow framework for mass customization using web service and autonomous agent techniques

Custom software development and maintenance is one of the key expenses associated with developing automated systems for mass customization. This paper presents a method for reducing the risk associated with this expense by developing a flexible environment for determining and executing dynamic workflow paths. Strategies for developing an autonomous agent-based framework and for identifying and creating web services for specific process tasks are presented. The proposed methods are outlined in two different case studies to illustrate the approach for both a generic process with complex workflow paths and a more specific sequential engineering process.     

A methodology to incorporate product mix variations in cellular manufacturing

The identification of part families and machine groups that form the cells is a major step in the development of a cellular manufacturing system and, consequently, a large number of concepts, theories and algorithms have been proposed. One common assumption for most of these cell formation algorithms is that the product mix remains stable over a period of time. In today’s world, the market demand is being shaped by consumers resulting in a highly volatile market. This has given rise to a new class of products characterized by low volume and high variety. To incorporate product mix changes into an existing cellular manufacturing system many important issues have to be tackled. In this paper, a methodology to incorporate new parts and machines into an existing cellular manufacturing system has been presented. The objective is to fit the new parts and machines into an existing cellular manufacturing system thereby increasing machine utilization and reducing investment in new equipment.