Xiaomeng Chang

Development and Utilization of Ontologies in Design for Manufacturing

There are many challenges associated with the design and realization of fast changing highly customized products. One promising approach is to implement design for manufacturing (DFM) strategies aimed at reducing production costs without compromising product quality. For manufacturers doing business in a globally distributed market place, effective reuse and sharing of the DFM knowledge in a collaborative environment is essential. In recent years, ontologies are increasingly used for knowledge management in engineering. Here, ontology is defined as a formal specification of domain knowledge that can be used to define a set of data and structure that enables experts to share information in a domain of interest, to aid information reasoning, and to manage and reuse data. The primary goal of this paper is to put forward the process of ontology development and utilization for DFM and to study the most important phases in the process, including: the concept categorization and class hierarchy development, slot categorization and development, identification and realization of relations among slots, and methods to support knowledge capture and reuse. Four cases are presented to illustrate the promising use of a DFM ontology. These cases prove that the DFM ontology and the process of ontology development and utilization for the DFM can facilitate the reuse of existing data, find the inconsistency and errors in data, reduce the work associated with populating the knowledge base of the ontology, and help designers make decisions by considering complex technical and economical criteria.

APPLICATION OF A GENETIC ALGORITHM TO CONCEPT VARIANT SELECTION

This paper outlines a framework for applying a genetic algorithm to the selection of component variants between the conceptual and detailed design stages of product development. A genetic algorithm (GA) is defined for the problem and an example is presented that demonstrates its application and usefulness. Functional modeling techniques are used to formulate the design problem and generate the chromosomes that are evaluated with the algorithm. In the presented example, suitable GA parameters and the break-even point where the GA surpassed an enumerated search of the same solution space were found. Recommend uses of the GA along with limitations of the method and future work are presented as well.Copyright

Ontology-Based Malfunction Forecast and Root Cause Analysis in Product Design

In product design, passing undetected errors to the downstream can cause error avalanche, could diminish product acceptance and largely increase the overall cost. Yet, it is difficult for designers to collect all the related potential errors from different departments in the initial design phase. In order to deal with these problems, this paper puts forward an ontology based method to integrate related history error data from different data sources of multiple departments in an enterprise. By using the advantages of ontologies and ontology-based information systems in knowledge management and semantic reasoning, the method enables the investigation of the root cause of the related potential malfunctions in the early product design phase. The framework can provide warnings and root causes of related potential errors in design based on history data and further continuously improve the product design. In this manner, this method is expected to reduce the knowledge limitation of designers in the initial design phase, help designers consider the problems in the whole enterprise and the product life cycle more completely, facilitate design improvement more accurately and efficiently, and further reduce the cost of the overall product life cycle.© 2012 ASME

Ontology Development and Optimization for Data Integration and Decision-Making in Product Design and Obsolescence Management

Product development in today’s global marketplace faces many challenges and pressures. Reducing development time, increasing quality and value, and reducing cost implications throughout the life cycle are critical. Strategies for dealing with rapidly changing technologies are very real and significant issues for product development as well. For low volume long-life complex products and systems in particular, such as those utilized by military and avionics applications, rapid advances in technologies have led to an escalation of time and costs challenges as manufacturers scramble to keep up with changes brought on by the obsolescence of components embodied in such systems. While some computer-based tools have been developed to aid product design and the management of obsolescence, benefits have been limited by issues associated with integrating heterogeneous sources of distributed information and knowledge. Data conflicts, data inexplicitness, incompleteness, inconsistency, and lacking information and knowledge needs for decisions associated with the management of obsolescence and product design continue. In recent years, ontology-based methods have presented new and promising approaches to manage knowledge in engineering, integrate multiple data resources, and facilitate the consideration of complex relations among concepts and properties for decision-making. In this chapter, manual and automatic ontology development and maintenance are introduced and ontologies can be optimized through identifying potential relations among distributed ontologies. Details of an ontology-based information system for data integration and decision support are also explained. Case studies are provided to illustrate the utilization of ontology with the proposed approaches to realize efficient product design and obsolescence management.

Ontology-Based Multiplatform Identification Method

This paper puts forward a multiplatform identification method to overcome the limitations of a single platform strategy when mass customization is required. The method is applied to redesign or consolidate an existing product family. The method consists of four steps: (1) the determination of component values, (2) the estimation of component redesign efforts, (3) the platform component identification, and (4) the formation of multiple platform instances. An ontology-based framework is also provided to facilitate the information representation and the data integration in the identification of multiplatform structure. Once the platforms are identified, an ontology reasoning mechanism verifies the platform sharing among products and determines the possible multiplatform coalition. A water cooler product family is used to illustrate the ontology-based multiplatform identification method.

Reducing errors in the development, maintenance and utilisation of ontologies

Ontologies and ontology-based information systems are becoming more commonplace in knowledge management. For engineering applications such as product design, ontologies can be utilised for knowledge capture/reuse and frameworks that allow for the integration and collaboration of a wide variety of tools and methods as well as participants in design (marketing/sales, engineers, customers, suppliers, distributors, manufacturing, etc.) who may be distributed globally across time, location, and culture. With this growth in the use of ontologies, it is critical to recognise and address errors that may occur in their representation, maintenance and utilisation. Passing undetected and unresolved errors downstream can cause error avalanche and could diminish the acceptance, further development and promise of significant impact that ontologies hold for product design, manufacturing, or any knowledge management environment within an organisation. This paper categorises errors and their causal factors, summarises possible solutions in ontology and ontology-based utilisation, and puts forward an ontology-based Root Cause Analysis (RCA) method to help find the root cause of errors. Error identification and collection methods are described first, followed by an error taxonomy with associated causal factors. Finally, an error ontology and associated SWRL (Semantic Web Rule Language) rules are built to facilitate the error taxonomy, the root cause analysis and solution analysis for these errors. Ultimately, this work should reduce errors in the development, maintenance and utilisation of ontologies and facilitate further development and use of ontologies in knowledge management.

Ontology-Enabled Data Inquiry for Cost Analysis and Obsolescence Mitigation

Cost analysis is essential to enterprises developing plans to deal with product obsolescence. Indeed, cost analysis drives the optimization behind obsolescence mitigation planning and the maintenance of long field life sustainment-dominated systems. There are many different obsolescence mitigation solutions. Determining the optimum plan requires inputs from multiple departments within the enterprise such as maintenance, manufacturing, inventory, marketing, purchasing, etc. Moreover, proper analysis requires system records over a long period. As one might expect, these needs present challenges since proper data comes from different sources across multiple departments. In recent years, ontological models have been shown to be good at relation representation and knowledge management. Ontologies have been used to help with data integration and decision-making. This paper puts forward an ontology-based model and data inquiry method to help locate appropriate departments and related heterogeneous data for current and legacy data sources. The ontology-enabled data inquiry can then more accurately and efficiently improve cost analysis and the planning and management of obsolescence mitigation activities.Copyright

A Framework for Ontology-Based Heterogeneous Data Integration for Cost Management in Product Family Design

High quality, high impact and economical products and systems are important goals for an enterprise. The usage of product families can be strategic to achieving these goals, yet defining these families can be challenging, requiring the consideration of numerous cost factors. This requires bringing together a great number of heterogeneous data sources of varying formats in a manner that allows the product development team to easily locate and reuse information in a collaborative manner across time and space. To date, our work has focused on the development and use of an Activity-Based Cost ontology (ABC ontology) to guide designers drill down to get at information for product family design. However, this ontology is built in such a way that it can only support information retrieval from the ontology and does not bring together and connect heterogeneous data resources. It does not address the problem of designers who struggle with obtaining relevant details from different departments in an enterprise. While there have been several semantic data schema integration tools for heterogeneous data resources integration, these tools cannot guide users to related information, that would lead to the root cause of the high cost. In this paper, in order to better manage cost in product family design, an ontology-based framework is put forward that builds on our prior work and combines the advantages of ABC ontology and data schema integration tools. The ontology-based framework can guide users to the proper information aspects through querying the central ontology, and give users detailed information about these aspects from heterogeneous data resources with the support of local ontologies. Ultimately, this framework will facilitate designers with better utilization of cost-related factors for product family design from a whole enterprise perspective.Copyright