Alojzij Sluga

A quality management model based on the “deep quality concept”

Purpose – According to many authors, differences in firm performances are increasingly attributed to tacit knowledge that cannot easily be transmitted or imitated. On the other hand, current quality management models knowledge typically relates only to people. Situations, in which knowledge that is related to people is not available, sufficient, reliable or lucrative for application, are not considered. This paper aims to investigate how to overcome this gap.Design/methodology/approach – Based on the adopted classification, types of knowledge typically present in an organisation are identified, and are discussed. Techniques for acquiring and formalising tacit knowledge are explored, and related criteria are defined. Particular attention is shown to knowledge management and artificial intelligence techniques.Findings – A new approach to quality management called deep quality concept (DQC) is conceptualised, and mechanisms and concepts needed to acquire and integrate formalised knowledge into quality system...

A multi-agent approach to process planning and fabrication in distributed manufacturing

In the paper a multi-agent approach to the development of a distributed manufacturing architecture is presented. An essential building block introduced here is the virtual work system (VWS) which represents a manufacturing work system in the information space. The VWS is structured as an autonomous agent and is a constituent entity of an agent network. In the network dynamic clusters of cooperating agents are solving manufacturing tasks. A machining work system and its VWS is demonstrated in a case study. Its role in the agent communication network is discussed in a process planning and fabrication domain.

A Conceptual Framework for Collaborative Design and Operations of Manufacturing Work Systems

The paper addresses a conceptual framework for collaborative product design and related manufacturing system development, operations and maintenance. The framework consists of (1) a conceptual model for collaboration among autonomous manufacturing work systems and (2) an ICT platform which supports collaborative operations over the web. The framework improves visibility, understanding and control of the processes involved and on a practical level provides a platform on which geographically distributed operations can be conducted effectively. The system is developed as an integrated prototype web application. The case study presents the implementation of the concept in the development, operations and maintenance of manufacturing cells for die-casting of aluminum and magnesium components for automotive industry.

Autonomous work systems in manufacturing networks

Manufacturing networks open new possibilities and potentials in design, development and production of complex high-tech products while they combine good characteristics of large companies with advantages of SMEs. In order to manage the structural complexity of a manufacturing network, the paper proposes the business-to-manufacturing network B2MN approach based on the market mechanism. Next, the network nodes in terms of the autonomous work system (AWS) are conceptualized. AWS encapsulates functionalities and competencies related to its management and manufacturing operations, as well as its autonomous information system, which supports autonomous decision-making and cooperation in the network. The case study illustrates the implementation of the AWS concept in industry.

Dynamic structuring of distributed manufacturing systems

Abstract The paper addresses the problem of dynamic structuring of manufacturing systems. The approach presented in this paper is based on the decomposition of manufacturing objectives and the allocation of tasks to autonomous building blocks, i.e. work systems, in a dynamic environment. The allocation is based on a market mechanism that enables the self-structuring and optimization of a manufacturing system by evaluation and selection among competing work systems. The mechanism presented is based on logic relations and constraints. It enables the building of task-oriented manufacturing structures of work systems acting in series and/or in parallel. The approach is discussed in an example in the part fabrication domain.

A Conceptual Framework for the Collaborative Modeling of Networked Manufacturing Systems

A manufacturing system is a product, and has to be designed as any other product. Therefore, a need for adequate methodological support and tools for modeling, structuring, and control of the next generation manufacturing systems is recognized. In this study, the adaptive distributed modeling framework for collaborative design and operations of network manufacturing systems is presented. In manufacturing networks, several autonomous partners participate in dynamic design of a manufacturing system, its implementation, and adaptation. In this context collaborative modeling, structuring, and control in distributed manufacturing environment play a vital role. The proposed modeling framework introduces the common modeling space and enables a collaborative definition of modeling building blocks, model design, simulation, and operations support of distributed manufacturing systems in a dynamic environment — which is the realistic nature of the global manufacturing. The prototype of the framework is elaborated in a case study.

Self-organization in a distributed manufacturing system based on constraint logic programming

Abstract The paper addresses the problem of self-organization of manufacturing systems. The objective is to overcome the rigidity of conventional hierarchical structures and to introduce structures that are able to adapt to a dynamic environment. The presented approach is based on the concept of Complex Adaptive Manufacturing Systems. It is characterized by a decomposition of manufacturing objectives and allocation of tasks to work systems as autonomous building blocks in a dynamic environment. The allocation is based on a market mechanism that enables self-organization and optimization of a manufacturing system by evaluation and selection among competing work systems. The approach is implemented in the Constraint Logic Programming environment Eclipse and validated in a simulation experiment.

Machine learning applied to quality management-A study in ship repair domain

The awareness about the importance of knowledge within the quality management community is increasing. For example, the Malcolm Baldrige Criteria for Performance Excellence recently included knowledge management into one of its categories. However, the emphasis in research related to knowledge management is mostly on knowledge creation and dissemination, and not knowledge formalisation process. On the other hand, identifying the expert knowledge and experience as crucial for the output quality, especially in dynamic industries with high share of incomplete and unreliable information such as ship repair, this paper argues how important it is to have such knowledge formalised. The paper demonstrates by example of delivery time estimate how for that purpose the deep quality concept (DQC)-a novel knowledge-focused quality management framework, and machine learning methodology could be effectively used. In the concluding part of the paper, the accuracy of the obtained prediction models is analysed, and the chosen model is discussed. The research indicates that standardisation of problem domain notions and expertly designed databases with possible interface to machine learning algorithms need to be considered as an integral part of any quality management system in the future, in addition to conventional quality management concepts.

Machine learning approach to machinability analysis

Optimisation and automation of determination of cutting conditions in operation planning depend significantly on availability of reliable machinability data and knowledge. In order to improve and automate the tool selection and determination of cutting parameters in operation planning we have to re-formulate and generalise the existing machinability knowledge. In the paper the existent machinability data base was analysed by the use of machine learning methodology. A multi-stage experiment has been carried out, comprising (1) preparatory phase in which manual construction of higher level attributes and grouping of similar learning examples to obtain more consistent decision trees was performed, and (2) learning relations between workpiece materials to be machined, cutting tool features and cutting conditions. Within the learning process several decision trees have been synthesised predicting tool features, cutting geometry and cutting parameters from a set of attribute values. The investigation has revealed the extended insight into the machinability domain, as well as the possible knowledge synthesis regarding workpiece material to be machined and cutting tool as a bottom-line in operation planning for NC-programming and automated process planning.

Condition monitoring and fault diagnostics for hydropower plants

Abstract This paper presents a condition monitoring and fault diagnostics (CMFD) system for hydropower plants (HPP). CMFD is based on the concept of industrial product-service systems (IPS2), in which the customer, turbine supplier, and maintenance service provider are the IPS2 stakeholders. The proposed CMFD consists of signal acquisition, data transfer to the virtual diagnostics center (VDC) and fault diagnostics. A support vector machine (SVM) classifier has been used for fault diagnostics. CMFD has been implemented on an HPP with three Kaplan units. A signal acquisition system for CMFD consists of data acquisition from a unit control system and a supplementary system for high-frequency data acquisition. The implemented SVM method exhibits high training accuracy and thus enables adequate fault diagnostics. The data are analyzed in the VDC, which allows all stakeholders access to diagnostic information from anywhere at any time. Based on this information, the service providers can establish condition-based maintenance and offer operational support. Furthermore, through the VDC, cooperation between the stakeholders can be achieved; thus, better maintenance scheduling is possible, which will be reflected in higher system availability.