Farhad Ameri

Product Lifecycle Management: Closing the Knowledge Loops

Competitive success of manufacturing firms is by and large determined by the success of the products they introduce to the market. This is why companies continuously try to improve the efficacy of their product realization process. Product Lifecycle Management (PLM) is a business solution which aims to streamline the flow of information about the product and related processes throughout the product’s lifecycle such that the right information in the right context at the right time can be made available. Yet, few organizations are positioned to reap the true benefits of PLM. One major reason for this is a lack of clear understanding of what PLM is, its core features and functions, and its relationship to the myriad of current software tools. This paper aims to do that and also elaborates on the role of PLM as a knowledge management system.

Digital manufacturing market: a semantic web-based framework for agile supply chain deployment

Manufacturing Market is a market in which manufacturing process capacity is the object of trade. In a market, units of capacity, represented as manufacturing services, can be acquired as needed and when needed, thus making supply chains more responsive to fluctuations in supply and demand. Although Manufacturing Market can be built physically as a spot market, its benefits can be better realized in a web-based framework. We refer to the web-based version of Manufacturing Market as Digital Manufacturing Market (DMM). The major challenges in deployment of a virtual market for manufacturing services include standard representation of manufacturing needs and capabilities, incorporation of intelligent supplier search and evaluation mechanism, and automation of supply chain configuration process. This paper introduces DMM through its major components including a multi-agent framework, a formal ontology for representation of manufacturing services as well as a matchmaking methodology used for connecting buyers and sellers of manufacturing services based on their semantic similarities. The ultimate goal of the proposed framework is to enable autonomous deployment of manufacturing supply chains based on the specific technological requirements defined by particular work orders.

A Matchmaking Methodology for Supply Chain Deployment in Distributed Manufacturing Environments

In modern manufacturing era, supply chains are increasingly becoming global and agile. To build agile global supply chains, companies first need to have access to a large supply base and secondly need an efficient mechanism for cost-effective and rapid location, evaluation, and selection of suppliers. This work introduces a matchmaking algorithm for connecting buyers and sellers of manufacturing services based on their semantic similarities in terms of manufacturing capabilities. The proposed matchmaking algorithm operates over Manufacturing Service Description Language (MSDL), an ontology for formal representation of manufacturing services. Since MSDL descriptions can be represented as directed labeled trees, a tree matching approach is implemented in this work.

Design-to-fabrication automation for the cognitive machine shop

To meet the rising demands for pure customization of products, new approaches for automated fabrication of customized part geometry are needed, on both the software and hardware side, that balance flexibility, robustness and efficiency. This is a great challenge since today it requires significant human expertise supported, only partially, by computer-aided approaches. This paper introduces a new approach and framework for an autonomous design-to-fabrication system that integrates cognitive capabilities, such as reasoning from knowledge models and autonomous planning, and embeds these in the machines themselves to automatically fabricate customized parts. The framework integrates into a common process automatic workpiece selection using an ontology, generative CNC machining planning using shape grammars and automated fixture design, based on a novel flexible fixture device hardware. Initial results are given for the machining planning approach applied to 2.5D parts with a defined approach direction and the prototyped fixture device is presented. The advantages and potential of the framework stem mainly from applying the principles of cognitive technical systems to a fabrication system to develop an integrated and on-line approach. The methods are developed specifically for use on the machine shop floor to take advantage of the possibility to update and extend knowledge models to reflect current fabrication capabilities and to adapt to changes in the environment and re-plan during operation. Finally, future directions, including integrating on-line perception and learning, are discussed, which are required to create a truly flexible and cognitive fabrication system.

An Upper Ontology for Manufacturing Service Description

In today’s volatile market, manufacturing companies are increasingly adopting agile manufacturing strategies. Virtual Enterprise (VE) is one of the key enablers of the agile philosophy. Timely deployment of VE requires efficient communication between the potential members of VE through a common language. This work introduces Manufacturing Service Description Language (MSDL) as an ontology for representation of manufacturing services. MSDL provides the primitive building blocks required for description of a wide spectrum of manufacturing services. Description Logic is used as the knowledge representation formalism of MSDL in order to make it amenable to automatic reasoning.© 2006 ASME

Semantic rule modelling for intelligent supplier discovery

Formal representation of manufacturing capabilities is a critical requirement for autonomous deployment of agile supply chains in distributed environments. In this context, manufacturing ontologies play a key role by providing the required means for explicit knowledge representation. Manufacturing Service Description Language (MSDL) is an OWL (Web Ontology Language)-based ontology developed with the purpose of modelling manufacturing capability in a service-oriented framework. This paper presents the rule-based extension of MSDL that enhances the ontology semantically and enables advanced ontological reasoning. The particular focus of this paper is on analysing the reasoning and inference patterns used by human experts during the supplier discovery process and formally representing them, to the possible extent, using Semantic Web Rule Language (SWRL). Two categories of rules, namely, property inference and classification rules are introduced and implemented in this research. In order to capture the rules and evaluate the effects of the encoded rules on the performance of the MSDL search engine, an experimental approach is followed in this work. The obtained results support the hypothesis that semantic enrichment of the ontology through introducing semantic SWRL rules enhances the performance of the semantic search process.

An Entropic Method for Sequencing Discrete Design Decisions

This paper presents a mathematical model for quantifying uncertainty of a discrete design solution and to monitor it through the design process. In the presented entropic view, uncertainty is highest at the beginning of the process as little information is known about the solution. As additional information is acquired or generated, the solution becomes increasingly well-defined and uncertainty reduces, finally diminishing to zero at the end of the process when the design is fully defined. In previous research, three components of design complexity—size, coupling, and solvability—were identified. In this research, these metrics are used to model solution uncertainty based on the search spaces of the variables (size) and the compatibility between variable values (coupling). Solvability of the variables is assumed uniform for simplicity. Design decisions are modeled as choosing a value, or a reduced set of values, from the existing search space of a variable, thus, reducing its uncertainty. Coupling is measured as the reduction of a variables search space as an effect of reducing the search space of another variable. This model is then used to monitor uncertainty reduction through a design process, leading to three strategies that prescribe deciding the variables in the order of their uncertainty, number of dependents, or the influence of on other variables. Comparison between these strategies shows how size and coupling of variables in a design can be used to determine task sequencing strategy for fast design convergence.

Systematic Decision Support for Engineering Change Management in PLM

Engineering Change Management (ECM) is an important component of PLM. ECM modules in current PLM solutions conform to the industry-standard CMII closed-loop change model. They provide customised forms and pre-defined workflows for creating and processing change requests, change orders, etc. Evaluating the effects of the proposed Engineering Change on manufacturing processes, BOM, lead times, inventory, etc., usually form tasks in this generic workflow. However, each change has different downstream effects, which themselves lead to further changes that may not be evident. Identifying these impacts requires considerable experience and expertise. This paper addresses the need for automated tools to assist this process. The approach involves dynamic creation of workflow tasks for evaluating cascaded effects of any change using a predefined industry specific knowledge base. The process is further enhanced by prioritising the evaluation of effects based on experience generated by past engineering changes.© 2005 ASME

Ontological Conceptualization Based on the SKOS

Ontological conceptualization refers to the process of creating an abstract view of the domain of interest through a set of interconnected concepts. In this paper, a thesaurus-based methodology is proposed for systematic ontological conceptualization in the manufacturing domain. The methodology has three main phases, namely, thesaurus development, thesaurus evaluation, and thesaurus conversion and it uses simple knowledge organization system (SKOS) as the thesaurus representation formalism. The concept-based nature of a SKOS thesaurus makes it suitable for identifying important concepts in the domain. To that end, novel thesaurus evaluation and thesaurus conversion metrics that exploit this characteristic are presented. The ontology conceptualization methodology is demonstrated through the development of a manufacturing thesaurus, referred to as ManuTerms. The concepts in ManuTerms can be converted into ontological classes. The whole conceptualization process is the stepping stone to developing more axiomatic ontologies. Although the proposed methodology is developed in the context of manufacturing ontology development, the underlying methods, tools, and metrics can be applied to development of any domain ontology. The developed thesaurus can serve as a standalone lightweight ontology and its concepts can be reused by other ontologies or thesauri.

A Text Mining Technique for Manufacturing Supplier Classification

The web presence of manufacturing suppliers is constantly increasing and so does the volume of textual data available online that pertains to the capabilities of manufacturing suppliers. To process this large volume of data and infer new knowledge about the capabilities of manufacturing suppliers, different text mining techniques such as association rule generation, classification, and clustering can be applied. This paper focuses on classification of manufacturing suppliers based on the textual description of their capabilities available in their online profiles. A probabilistic technique that adopts Naive Bayes method is adopted and implemented using R programming language. Casting and CNC machining are used as the examples classes of suppliers in this work. The performance of the proposed classifier is evaluated experimentally based on the standard metrics such as precision, recall, and F-measure. It was observed that in order to improve the precision of the classification process, a larger training dataset with more relevant terms must be used.Copyright