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Evaluation of walking stability is essential for the elderly population to prevent possible falls due to degradation of their walking stability caused by aging. Previous studies mainly focused on center of pressure (COP) and center of body mass (COM) and their relationship during walking. However COM can not be directly measured and existing COP parameters are mainly investigated within one stride and their effectiveness is not conclusive. In this study, new parameters are proposed based on normalized cross-correlation of subsequent strides (NCSS), derived from comparison of subsequent strides of foot plantar pressure. The mean and standard deviation of NCSS are calculated for three walking conditions that are progressively less stable (1 - normal walking, 2 - walking with eyes closed, and 3 - walking with eyes closed after being spun around) for six healthy subjects. Results show that the NCSS can effectively distinguish normal walking (1) and the less stable walking with eyes closed after being spun around (3). The mean of NCSS decreases from stable to less stable walking conditions, whereas the corresponding standard deviation of NCSS increases. For each subject the value of NCSS of walking with eyes closed (2) is between those of the other two walking conditions (1 and 3), although the value varies somewhat, depending on the controllability and adaptability of the subject.Copyright © 2010 by ASME

Estimating the average resultant cutting force in end milling operations under varying machining conditions is considered in this study. Simulated results presented here show that a neural estimator can be trained to accurately estimate the cutting force without a priori knowledge of the system dynamics. Training of the neural estimator was accomplished using the backpropagation algorithm and the trained neural estimator could be used to train a neural controller to specify the input to the system which will yield the desired output.

This paper presents a framework for integrating the paradigm of case-base reasoning with the knowledge based approach in intelligent process planning. The proposed case based system uses process planning cases to represent the process planning knowledge and possesses self-learning capability in the sense that it develops its knowledge from the past and the new planning experiences. It therefore can overcome some weakness in the knowledge-based expert systems. The proposed framework combines the strength of both the case base reasoning and knowledge based reasoning approaches. The case based system contains four major elements: retriever, modifier, simulator and repairer . A feature based representation scheme is used to represent and index process planning cases in the system's memory. The retriever uses a similarity metric to retrieve an old case which is the most similar case, among all old ones, to the new case. The modifier is then activated to adapt the process plan of the retrieved case to fit the needs for the new case. The simulator is used to verify the feasibility of the modified plan. If the modified plan is proven to be infeasible, the repairer will be used to repair the failed plan. The knowledge based system is used as a complementary unit to assist the case based system in the framework. In this paper, rotational parts are used to show how the four elements in the case based system integrated with the knowledge based system can be used to support learning capability in automated process planning.

3D model matching has been widely studied in computer vision, graphics and robotics. While there is much success made in the matching of natural objects, most of these approaches consider smooth surfaces and are not suitable for computer aided design (CAD) models because of their complex topology and singular structures. This paper presents a novel spectral approach for the 3D CAD model matching in the framework of manifold learning. The 3D models are treated as undirected graphs. A regularized Laplacian spectrum approach is applied to solve this problem where the regularization term is used to characterize the shape geometries. Spectral distributions of different models are obtained and then compared by their divergence for model retrieval. The proposed approach is tested with models from known 3D CAD database for verification.

The internet offers a new channel for product designers to obtain valuable information about customer’s opinions which are very important to product development, especially at the product concept design stage. Due to the rapid growth of such information, it is difficult for humans to manage and analyze all these information. Therefore, an alternative choice is to perform opinion mining with automatic textual mining techniques. In this research, we propose a hybrid opinion extraction (HOE) framework that can extract features and predict semantic orientation of the expressed opinions, from the free format text. The framework is inspired by capturing the characteristics of the way people express opinions, utilizes both statistical regularities of the patterns and some prior knowledge. Compared to previous work, our opinion mining technique has demonstrated its better performance in terms of extracting features and predicting semantic orientations of opinions. Thus it has the potential to be adopted by product designers as an efficient tool for quickly obtaining customer feedback.Copyright © 2009 by ASME

Conceptual design synthesis is recognized as the most critical design task, where functional decomposition and function to structure mapping are widely advocated as two fundamental synthesis strategies. However, these strategies only provide some top-level guidelines to the designers in carrying out such essential design task. Embodiment methods and tools are thus much needed by the designers, especially for the novice. In this paper, we present a design synthesis software system, where functional decomposition is embodied as including not only decomposition of function, but also decomposition of design problem and/or design task. The function to structure mapping is embodied as including not only direct function to structure mapping, but also mapping via behaviour and behaviour process. Besides, both structure and materials are taken as part of design solutions. The object-oriented technology is employed in the implementation of the software, and a design case of a GMM speaker is studied to demonstrate its usage as well as its applicability.

Virtual reality technology plays an important role in the fields of product design, computer animation, medical simulation, cloth motion, and many others. Especially with the emergence of haptics technology, virtual simulation system provides an intuitive way of human and computer interaction, which allows user to feel and touch the virtual environment. For a real-time simulation system, a physically based deformable model including complex material properties with a high resolution is required. However, such deformable model hardly satisfies the update rate of interactive haptic rendering that exceeds 1 kHz. To tackle this challenge, a real-time volumetric model with haptic feedback is developed in this paper. This model, named as Adaptive S-chain model, extends the S-chain model and integrates the energy-based wave propagation method by the proposed adaptive re-mesh method to achieve realistic graphic and haptic deformation results. The implemented results show that the nonlinear, heterogeneous, anisotropic, shape retaining material properties and large range deformation are well modeled. An accurate force feedback is generated by the proposed Adaptive S-chain model in case study which is quite close to the experiment data.Copyright © 2009 by ASME

In this paper, a haptic modeling and simulation system is developed to assist handheld product design. With haptic feedback, users could create, interact and evaluate the virtual product directly and intuitively without producing the physical prototype. This saves the cost and reduces time-to-market, which is especially meaningful for the rapidly changing handheld mobile devices. To provide a comfortable and accurate operation, a virtual vibration actuator is devised to add into the touch screen. Unlike the previous research that mainly focuses on the design of the product shape, the proposed system also models the interaction between the user (finger) or tool (pen) and handheld device (button/screen). To obtain realistic simulation and replace the physical prototype, the complex shape and deformation of the finger are considered when calculating the feedback force. A computational efficient collision detection method for complex shape objects is proposed to tackle the challenge of a high update rate of more than 1 kHz for real-time realistic haptic rendering. Moreover, the proposed system incorporates the haptic modeling of vibration interaction and menu interface design into the product design simulation system. A case study of handheld device design is used to illustrate the proposed system.Copyright © 2009 by ASME

New Product Development (NPD) is important to sustain the competitiveness of a company in facing the strong market competition. However, past studies on NPD in Singapore in year 2003 and 2004 revealed that only minority of the companies was engaged in NPD. To support the effort of moving Singapore to be a design hub that is internationally recognized, this work presents a preliminary study of the current status of NPD. A better insight into the status of NPD will be useful for the relevant institutions and companies in taking appropriate action to improve and encourage more NPD activities. By using a sample of 114 companies that responded to a survey conducted, this paper examines NPD activities from different aspects. Comparisons with past studies are made to provide a view on the changes in over the years. The results show that only minority of the companies are engaging in NPD. Nevertheless, a comparison with past studies suggests an increase in the level of sophistication of NPD involved.

One of the biggest challenges in product development is to select the product concept with the best market potential. In this work, a selection methodology is developed utilising fuzzy logic. It is an amalgamation of Pugh matrix and utility scoring approach, with the crisp numerical weights and comparison ratings replaced by linguistic fuzzy functions. Experimental results show that the proposed method can effectively perform concept selection in product development process. This method has the potential to identify the strengths and weaknesses of a few candidate concepts and consider the possibility of adopting the strong aspects and improving on the poor ones.

Computational Intelligence (CI) has emerged as a rapid growing field over the past decade. Its various techniques have been recognized as powerful tools for intelligent information processing, decision making and knowledge management. Advances of Computational Intelligence in Industrial Systems reports the exploration of CI frontiers with an emphasis on a broad spectrum of real-world applications. Section I – Theory and Foundation presents some of the latest developments in CI, e.g. particle swarm optimization, Web services, data mining with privacy protection, kernel methods for text analysis, etc. Section II – Industrial Application covers the CI applications in a wide variety of domains, e.g. clinical decision support, process monitoring for industrial CNC machine, novelty detection for jet engines, ant algorithm for berth allocation, etc. Such a collection of chapters has presented the state-of-the-art of CI applications in industry and will be an essential resource for professionals and researchers who wish to learn and spot the opportunities in applying CI techniques to their particular problems.

This chapter introduces an approach of deriving taxonomy from documents using a novel document profile model that enables document representations with the semantic information systematically generated at the document sentence level. A frequent word sequence method is proposed to search for the salient semantic information and has been integrated into the document profile model. The experimental study of taxonomy generation using hierarchical agglomerative clustering has shown a significant improvement in terms of Fscore based on the document profile model. A close examination reveals that the integration of semantic information has a clear contribution compared to the classic bag-of-words approach. This study encourages us to further investigate the possibility of applying document profile model over a wide range of text based mining tasks.

It is well known that over 80% of the time required to carry out any real world data mining project is usually spent on data preprocessing. Data preprocessing lays the groundwork for data mining. Before the discovery of useful information/knowledge, the target data set must be properly prepared. But it is unfortunately ignored by most researchers on data mining due to its perceived difficulty. This paper describes an efficient approach for data preprocessing for mining Web based customer survey data in order to speed up the data preparation process. The proposed approach is based on a unified data model derived from analysis of the characteristics of the customer survey data. The unified data model is used as a standard representation for the incoming data so that it can be mined. It not only provides flexibility for data preprocessing but also reduce complexity and difficulty of preparation for mining customer survey data.

This paper presents a new knowledge management paradigm in order to assist knowledge workers to make decision effectively and efficiently in the new economy age. The paradigm is called knowledge infused decision support (KIDS) framework for managing knowledge required for complex decisions in manufacturing processes. KIDS framework tackles two kinds of knowledge: quantitative knowledge and qualitative knowledge. Quantitative knowledge is discovered from enterprise's business databases through data mining, and qualitative knowledge like experience and judgment captured from human in the collaborative decision making process. The infusion of quantitative and qualitative knowledge provides better understanding of business processes and the decision contexts for decision makers to make decisions quickly under the pressures of time-and knowledge-based competition. The knowledge is then codified into enterprise's knowledge repository. A knowledge mapping mechanism is also provided in the framework for not only delivering the relevant knowledge to the knowledge workers, but also enabling them to access knowledge from knowledge repository efficiently and effectively at anytime and anywhere. It finally discusses several applications in manufacturing industry in order to speed up design decision making using quantitative and qualitative knowledge.

In today's global marketplace, customers' demand for personalized product has become a trend. This results in an increasing demand for manufacturers to produce more product variants. Product configurator as an integral part of manufacturing services system plays an important role in generating and managing product variants. In order for product configurator to generate as much variety as possible for the market with as little variety as possible in between products, we introduce a cross family product configuration concept for generating more product variants on-the-fly without designing a new product family. A constraint satisfaction problem (CSP) approach is adopted to model the configuration problem. We define a novel way of semantic modeling to capture the configuration knowledge for a product family, including product family composition information, configuration constraints and their mapping relationship. Based on the model, a semantics extraction algorithm is proposed to extract useful semantics from existing product family knowledge for cross family product configuration. The significance of the model and extraction approach is demonstrated.

Mass customization has become a crucial business strategy for product manufacturers that aims at satisfying individual customer needs with near mass production efficiency. Companies must develop the necessary infrastructure to derive valid product configurations that satisfy the requirements of lifecycle cost along with customer’s constraints. In this paper, to overcome the drawback of current product configurators, we apply a rule mining approach to automatically generate configuration knowledge, and present a hybrid approach based on Activity Based Costing (ABC) and machine learning techniques to estimate LCC of derived product variants from a constraintbased configurator at the design stage. The proposed intelligent techniques would benefit companies in enhancing product development capability in a shorter lifecycle.

Due to the variation of business practices from one company to another and the lack of flexibility to support different business practices, enterprise systems need to be customized based on the requirements of individual companies. Customization is one the major activities in the enterprise system implementation. It is mainly a process of redesigning and redeveloping functions of an enterprise information system for a particular company. The extra time required for customization definitely stretches the deployment cycle and increases the implementation cost. It is also a barrier that hampers companies to change their business practices for new business opportunities. Therefore, technologies for developing configurable enterprise systems with flexibility to adapt to business practices in different companies are gaining the attention of solution vendors and researchers. In this research, semantic representations were developed for configurable enterprise systems. These representations can enhance the flexibility of enterprise systems and make enterprise systems able to be rapidly tailored for different companies. The core of the concept is to decouple various logics from systems and represent the logics in a semantic manner. To achieve semantic representations, XML-based declarative language has been developed for representing a diversity of logics. The paper elaborates semantic representations of information entities, entity relationships, functional components and graphical environments. A business process management system based on the semantic representations is also briefly presented.

Product structure is the key information widely used by various business activities performed at different departments and different stages. In the made-to-order environment, product structure representation becomes more complicated because each product can have many variants with slightly different constitutions to fulfill different customer requirements. In such a context, product structure management comes to two interrelated functions: family structure management and variant structure management. At the same time, these two functions need to be seamlessly integrated to ensure the consistency of a family structure and its variant structure. From the business process perspective, throughout the entire product lifecycle, different business activities look at product structure with different purposes. Some activities are carried out based on variants and deem individual variants as different products and some need to be performed based on an entire family. As such, it is imperative to develop a product structure model that is capable of flexibly representing product families and product variants to serve up different processes in a product lifecycle. In this paper, a product structure model based on a master-variant pattern is proposed. The model can explicitly represent common characteristics of a family and particular characteristics of individual variants. Moreover, the variant structure representation is built on the top of the family structure representation. As a result, it provides an effective means to synchronize two types of structures. It also makes product family and variant concepts transparent to various business processes so that effective support can be provided to processes integration in the made-to-order environment.

Today’s highly competitive, global marketplace is redefining the way companies do business. Mass customization (Pine, 1993) provides a new paradigm for manufacturing industries, whereby variety and customization supplant standardized products, heterogeneous and fragmented markets spring from once homogeneous markets, and product life cycles and development cycles spiral downward (Tseng and Jiao 1996, 1998). It has recently received much attention and popularity from both industry and academia, and has been considered as a new battlefield for manufacturing enterprises (Wortmann et al. 1997). Mass customization aims at delivering an increasing product variety to satisfy diverse customer needs while maintaining near mass production efficiency (Tseng and Jiao 1996). Essentially, it is an oxymoron of variety to cater for customization and the low costs of variety fulfillment. To adopt the mass customization paradigm, many companies are being faced with the challenge of providing as much variety as possible in the market place with as little variety as possible between products in order to maintain economies of scale, while satisfying a wide range of customer requirements.

There are many fabrication processes in modern manufacturing, but current modeling and simulation tools only simulate a few unit processes based on different geometry models. To overcome the data exchange problem between different models, this paper studies various in-process geometry models together with their working systems / prototypes for traditional manufacturing processes. Novel hybrid multiple-machining and layered manufacturing processes are presented to identify critical issues. Working towards a vision of pervasive modeling and simulation, a unified Voxel-based in-process geometry model for multiple-machining and layered manufacturing simulations is proposed and discussed.Copyright © 2005 by ASME

With the new global economy, manufacturing companies are focusing their efforts on the product development process which is fast emerging as a new competitive weapon. Several product development solutions allow engineers, suppliers, business partners and even customers to collaborate throughout the entire product lifecycle via the Internet. To gain an additional edge over competitors, it is vital that companies utilize Web logs to discover hidden knowledge about trends and patterns in such a cyberworld. However, existing Web log mining techniques are not designed for web logs generated by product data management processes. In this paper, we propose a method termed Product Development Miner (PDMiner) to mine such Web logs efficiently and effectively using a trie structure and sequential mining techniques. Experiments involving real Web logs show that PDMiner is both fast and practical.

Engineering changes are inevitable in a product development life cycle. The requests for engineering changes can be due to new customer requirements, emergence of new technology, market feedback, or variations of components and raw materials. Each change generates a level of impact on costs, time to market, tasks and schedules of related processes, and product components. Change management tools available today focus on the management of document and process changes. Assessments of change impact are typically based on the “rule of thumb”. Our research has developed a methodology and related techniques to quantify and analyze the impact of engineering changes to enable faster and more accurate decision-making in engineering change management. Reported in this paper are investigations of industrial requirements and fundamental issues of change impact analysis as well as related research and techniques. A framework for a knowledge-supported change impact analysis system is proposed. Three critical issues of system implementation, namely integrated design information model, change plan generator and impact estimation algorithms, are addressed. Finally the benefits and future work are discussed.Copyright © 2003 by ASME

Engineering design is essentially a collaborative decision-making process that requires rigorous evaluation, comparison and selection of design alternatives and optimization from a global perspective on the basis of different classes of design criteria. Increasing design knowledge and supporting designers to make right and intelligent decisions can achieve the improvement of the design and design efficiency. This paper develops a knowledge-based decision support model and framework that can be extensively applied for an engineering system, which allows for the seamless / smooth integration of collaborative product development with optimal product performance. The developed hybrid robust design decision support model quantitatively incorporates qualitative design knowledge and preferences of multiple, conflicting attributes stored in a knowledge repository so that a better understanding of the consequences of design decisions can be achieved from an overall perspective. Two new concepts and mechanisms, transforming bridge and regulatory switch, are introduced in integration of decision support models. The results of this work provide a framework for an efficient decision support environment involving distributed resources to shorten the realization of products with optimal life-cycle performance and competitiveness. The developed methodology and framework are generic and flexible enough to be used in a variety of decision problems. Case application and studies for concept evaluation and selection in design for mass customization are provided.

The explosion of raw data in our information age necessitates the use of unsupervised knowledge discovery techniques to understand mountains of data. Cluster analysis is suitable for this task because of its ability to discover natural groupings of objects without human intervention. However, noise in the data greatly affects clustering results. Existing clustering techniques use density-based, grid-based or resolution-based methods to handle noise but they require the fine-tuning of complex parameters. Moreover, for high-dimensional data that cannot be visualized by humans, this fine-tuning process is greatly impaired. There are several noise/outlier detection techniques but they too need suitable parameters. In this paper, we present a novel parameterless method of filtering noise using ideas borrowed from association rule mining. We term our technique, FLUID (Filtering Using Itemset Discovery). FLUID automatically discovers representative points in the dataset without any input parameter by mapping the dataset into a form suitable for frequent itemset discovery. After frequent itemsets are discovered, they are mapped back to their original form and become representative points of the original dataset. As such, FLUID accomplishes both data and noise reduction simultaneously, making it an ideal preprocessing step for cluster analysis. Experiments involving a prominent synthetic dataset prove the effectiveness and efficiency of FLUID.

The study of foot and ankle kinematics is important in clinical cases such as abnormal foot functions and foot deformities. The objective of this study is to investigate kinematics of foot and ankle during stance phase of normal walking. This kinematic study includes joint motions between five rigid foot segments with new reference positions, and four new functional angles for describing physical features of walking. The results show that time-histories of the five joint motions present good agreement with previous literature. The new reference positions perform well for reducing variance among subjects. The results of four functional angles are consistent with gait and can more intuitively describe foot function features with good intra-subject repeatability. This study provides a comprehensive understanding of foot and ankle motions during stance phase of normal walking.

This paper aims to develop a haptics-based virtual simulation system for product design. To fully reach the potential of such a system, the modelling methods of the user-product interaction should be based on physics to enable users to feel the physical behaviours of various products. The key challenge is the balance between the heavy computational load and the high update rate for haptic feedback. In this paper, the modelling of rigid and deformable objects for haptics-based simulations is developed, including button click and vibration feeling as well as the interaction with heterogeneous deformable material. The commercial haptic device PHANToM Desktop from SensAble Technologies is integrated with the models into the implemented prototype system. It is validated that the proposed simulation system can allow users to experience and manipulate virtual objects in cyber space. The simulation results demonstrate that the proposed modelling methods are computationally efficient to provide real-time haptic feedback.

Ultra-precision micro-structured surfaces are becoming increasingly important in a range of application areas, including engineering optics, biological products, metrology artifacts, data storage, etc. However, there is a lack of surface characterization methods for the micro-structured surfaces with sub-nanometer accuracy. Although some research studies have been conducted on 3D surface characterization, most of them are on freeform surfaces, which are difficult to be applied on the micro-structured surfaces because of their limited characterization accuracy and the repeated surface feature patterns in the micro-structured surfaces. In this paper, an automatic form error evaluation method (AFEEM) is presented to characterize the form accuracy of the micro-structured surfaces. The machined micro-structured surface can be measured by any 3D high resolution measurement instrument. The measurement data are converted and pre-processed for the AFEEM, which mainly consists of a coarse registration and a fine registration process. The coarse registration estimates an initial position of the measured surface for the fine registration by extracting the most perceptually salient points in the surfaces, computing the integral volume descriptor for each salient point, searching for the best triplet-point correspondence and calculating the coarse registration matrix. The fine registration aligns the measured surface to the designed surface by a proposed adaptive iterative closest point algorithm to guarantee sub-nanometer accuracy for surface characterization. A series of computer simulations and experimental studies were conducted to verify the AFEEM. Results demonstrate the accuracy and effectiveness of the AFEEM for characterizing the micro-structured surfaces.

Computer-Integrated Manufacturing (CIM) system plays an important role in helping manufacturing companies move towards enterprise-wide automation in achieving competitive advantages. Manufacturing process planning plays a significant role by providing concurrent integration capabilities, from design to planning, scheduling and manufacturing. A concurrent manufacturing process planning architecture has been developed to support the integration of process planning with design, manufacturing, Capacity Planning and Scheduling (CPS) for tooling production. A prototype system based on this system architecture has been developed and tested in a tooling job shop, with satisfactory results.

The manufacturing industry is facing unprecedented challenges in this dynamic economy and social environment. Factors like incorporation of quality services, environmental sustainability and the successful effective leveraging of industrial informatics for virtual enterprises are new determinants for a manufacturing enterprise to stay competitive. Among these, the

Manufacturing process planning plays a significant role by providing collaborative integration capabilities, from design to manufacturing. This situation is more acute in the tooling production industry, which is characterised by short lead-time and frequent dynamic customer requirements. A framework for collaborative integration between computerised design and manufacturing has been developed to support the integration of product design with manufacturing process planning. A prototype system based on this framework has been developed and tested in a tooling job shop with satisfactory results. The integration framework is also expected to be effectively applied in other similar enterprise application domains.

Plastics are an essential part of most Fast Moving Consumer Goods’ (FMCG) packaging; however, plastics could pose a significant threat to environment. As FMCG packages are characterized by short life spans and high turnover rates, FMCG packaging is contributing to enormous amount of plastic pollution. On the other hand, companies attempt to differentiate their FMCG packaging through value creation from consumer experience and product quality. The paper presents the development of a supporting tool for design engineer to evaluate the design with the balance of environmental impact, material cost and value creation of the FMCG packaging. A simplified version of pairwise comparison is proposed to evaluate different packaging designs. A case study is presented to illustrate the proposed methodology.

The design structure matrix (DSM) method is a powerful network modelling tool to highlight the system architecture via presenting the interactions between the elements comprising a system. In DSM, measuring the interaction strength between elements is the first and crucial step. However, since the system elements are generally pre-identified by experts, their interaction relationships are often obtained from experts’ evaluation, which is time consuming and error-prone. To address this critical issue, in this paper, we aim to discover the hidden tasks of a process and their interactions from email logs with domain-specific contents. To this purpose, we have proposed a three-stage numerical DSM modeling approach, where text mining and clustering techniques are studied to discover hidden tasks. Furthermore, a metric concerning the measurement of interaction strength based on the overlapping time and the volume of exchanged information is presented. A case study is provided based on the emails collected from a design project.