PROCESS PLATFORM FORMATION FOR PRODUCT FAMILIES

ABSTRACT In accordance with the product families, process platforms have been recognized as a promising tool for companies to configure optimal, yet similar, production processes for producing different products. This paper tackles process platform formation from large volumes of production data available in companies' production systems. A data mining methodology based on text mining and tree matching is developed for the formation of process platforms. A case study of high variety production of vibration motors for mobile phones is reported to prove the feasibility and potential of forming process platforms using text mining and tree matching

TEXTUAL DATA MINING FOR NEXT GENERATION INTELLIGENT DECISION MAKING IN INDUSTRIAL ENVIRONMENT: A SURVEY

This paper proposes textual data mining as a next generation intelligent decision making technology for sustainable knowledge management solutions in any industrial environment. A detailed survey of applications of Data Mining techniques for exploiting information from different data formats and transforming this information into knowledge is presented in the literature survey. The focus of the survey is to show the power of different data mining techniques for exploiting information from data. The literature surveyed in this paper shows that intelligent decision making is of great importance in many contexts within manufacturing, construction and business generally. Business intelligence tools, which can be interpreted as decision support tools, are of increasing importance to companies for their success within competitive global markets. However, these tools are dependent on the relevancy, accuracy and overall quality of the knowledge on which they are based and which they use. Thus the research work presented in the paper uncover the importance and power of different data mining techniques supported by text mining methods used to exploit information from semi-structured or un-structured data formats. A great source of information is available in these formats and when exploited by combined efforts of data and text mining tools help the decision maker to take effective decision for the enhancement of business of industry and discovery of useful knowledge is made for next generation of intelligent decision making. Thus the survey shows the power of textual data mining as the next generation technology for intelligent decision making in the industrial environment

Textual data mining applications for industrial knowledge management solutions

In recent years knowledge has become an important resource to enhance the business and many activities are required to manage these knowledge resources well and help companies to remain competitive within industrial environments. The data available in most industrial setups is complex in nature and multiple different data formats may be generated to track the progress of different projects either related to developing new products or providing better services to the customers. Knowledge Discovery from different databases requires considerable efforts and energies and data mining techniques serve the purpose through handling structured data formats. If however the data is semi-structured or unstructured the combined efforts of data and text mining technologies may be needed to bring fruitful results. This thesis focuses on issues related to discovery of knowledge from semi-structured or unstructured data formats through the applications of textual data mining techniques to automate the classification of textual information into two different categories or classes which can then be used to help manage the knowledge available in multiple data formats. Applications of different data mining techniques to discover valuable information and knowledge from manufacturing or construction industries have been explored as part of a literature review. The application of text mining techniques to handle semi-structured or unstructured data has been discussed in detail. A novel integration of different data and text mining tools has been proposed in the form of a framework in which knowledge discovery and its refinement processes are performed through the application of Clustering and Apriori Association Rule of Mining algorithms. Finally the hypothesis of acquiring better classification accuracies has been detailed through the application of the methodology on case study data available in the form of Post Project Reviews (PPRs) reports. The process of discovering useful knowledge, its interpretation and utilisation has been automated to classify the textual data into two classes.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Analysis of manufacturing operations using knowledge- Enriched aggregate process planning

Knowledge-Enriched Aggregate Process Planning is concerned with the problem of supporting agile design and manufacture by making process planning feedback integral to the design function. A novel Digital Enterprise Technology framework (Maropoulos 2003) provides the technical context and is the basis for the integration of the methods with existing technologies for enterprise-wide product development. The work is based upon the assertion that, to assure success when developing new products, the technical and qualitative evaluation of process plans must be carried out as early as possible. An intelligent exploration methodology is presented for the technical evaluation of the many alternative manufacturing options which are feasible during the conceptual and embodiment design phases. 'Data resistant' aggregate product, process and resource models are the foundation of these planning methods. From the low-level attributes of these models, aggregate methods to generate suitable alternative process plans and estimate Quality, Cost and Delivery (QCD) have been created. The reliance on QCD metrics in process planning neglects the importance of tacit knowledge that people use to make everyday decisions and express their professional judgement in design. Hence, the research also advances the core aggregate planning theories by developing knowledge-enrichment methods for measuring and analysing qualitative factors as an additional indicator of manufacturing performance, which can be used to compute the potential of a process plan. The application of these methods allows the designer to make a comparative estimation of manufacturability for design alternatives. Ultimately, this research should translate into significant reductions in both design costs and product development time and create synergy between the product design and the manufacturing system that will be used to make it. The efficacy of the methodology was proved through the development of an experimental computer system (called CAPABLE Space) which used real industrial data, from a leading UK satellite manufacturer to validate the industrial benefits and promote the commercial exploitation of the research

Knowledge Discovery Models for Product Design, Assembly Planning and Manufacturing System Synthesis

The variety of products has been growing over the last few decades so that the challenges for designers and manufacturers to enhance their design and manufacturing capabilities, responsively and cost-effectively are greater than ever. The main objective of this research is to help designers and manufacturers cope with the increasing variety management challenges by exploiting the data records of existing or old products, along with appropriate Knowledge Discovery (KD) models, in order to extract the embedded knowledge in such data and use it to speed-up the development of new products. Four product development activities have been successfully addressed in this research: product design, product family formation, assembly sequencing and manufacturing system synthesis. The models and methods developed in this dissertation present a package of knowledge-based solutions that can greatly support product designers and manufacturers at various stages of the product development and manufacturing planning stages. For design retrieval; using efficient tree reconciliation algorithms found in Biological Sciences, a novel Bill of Materials (BOM) trees matching method was developed to retrieve the closest old design and discover components and structure shared with new product design. As a further application to BOM matching, an enhanced BOM matching method was also developed and used for product family formation. A new approach was introduced for assembly sequencing, based on the notion of consensus trees used in evolutionary studies, to overcome the critical limitation of individual assembly sequence retrieval methods that are not able to capture the assembly sequence data for a given new combination of components that never existed before in the same product variant. For manufacturing system synthesis; a novel Integer Programming model was developed to extract association rules between the product design domain and manufacturing domain to be used for synthesizing a manufacturing/assembly system for new products. Examples of real products were used to demonstrate and validate the developed models and comparisons with related existing methods were carried out to demonstrate the advantages of the developed models. The outcomes of this research provide efficient, and easy to implement knowledge-based solutions for facilitating cost-effective and rapid product development activities

Products and Services

Today’s global economy offers more opportunities, but is also more complex and competitive than ever before. This fact leads to a wide range of research activity in different fields of interest, especially in the so-called high-tech sectors. This book is a result of widespread research and development activity from many researchers worldwide, covering the aspects of development activities in general, as well as various aspects of the practical application of knowledge

Towards the development of a reliable reconfigurable real-time operating system on FPGAs

In the last two decades, Field Programmable Gate Arrays (FPGAs) have been rapidly developed from simple “glue-logic” to a powerful platform capable of implementing a System on Chip (SoC). Modern FPGAs achieve not only the high performance compared with General Purpose Processors (GPPs), thanks to hardware parallelism and dedication, but also better programming flexibility, in comparison to Application Specific Integrated Circuits (ASICs). Moreover, the hardware programming flexibility of FPGAs is further harnessed for both performance and manipulability, which makes Dynamic Partial Reconfiguration (DPR) possible. DPR allows a part or parts of a circuit to be reconfigured at run-time, without interrupting the rest of the chip’s operation. As a result, hardware resources can be more efficiently exploited since the chip resources can be reused by swapping in or out hardware tasks to or from the chip in a time-multiplexed fashion. In addition, DPR improves fault tolerance against transient errors and permanent damage, such as Single Event Upsets (SEUs) can be mitigated by reconfiguring the FPGA to avoid error accumulation. Furthermore, power and heat can be reduced by removing finished or idle tasks from the chip. For all these reasons above, DPR has significantly promoted Reconfigurable Computing (RC) and has become a very hot topic. However, since hardware integration is increasing at an exponential rate, and applications are becoming more complex with the growth of user demands, highlevel application design and low-level hardware implementation are increasingly separated and layered. As a consequence, users can obtain little advantage from DPR without the support of system-level middleware. To bridge the gap between the high-level application and the low-level hardware implementation, this thesis presents the important contributions towards a Reliable, Reconfigurable and Real-Time Operating System (R3TOS), which facilitates the user exploitation of DPR from the application level, by managing the complex hardware in the background. In R3TOS, hardware tasks behave just like software tasks, which can be created, scheduled, and mapped to different computing resources on the fly. The novel contributions of this work are: 1) a novel implementation of an efficient task scheduler and allocator; 2) implementation of a novel real-time scheduling algorithm (FAEDF) and two efficacious allocating algorithms (EAC and EVC), which schedule tasks in real-time and circumvent emerging faults while maintaining more compact empty areas. 3) Design and implementation of a faulttolerant microprocessor by harnessing the existing FPGA resources, such as Error Correction Code (ECC) and configuration primitives. 4) A novel symmetric multiprocessing (SMP)-based architectures that supports shared memory programing interface. 5) Two demonstrations of the integrated system, including a) the K-Nearest Neighbour classifier, which is a non-parametric classification algorithm widely used in various fields of data mining; and b) pairwise sequence alignment, namely the Smith Waterman algorithm, used for identifying similarities between two biological sequences. R3TOS gives considerably higher flexibility to support scalable multi-user, multitasking applications, whereby resources can be dynamically managed in respect of user requirements and hardware availability. Benefiting from this, not only the hardware resources can be more efficiently used, but also the system performance can be significantly increased. Results show that the scheduling and allocating efficiencies have been improved up to 2x, and the overall system performance is further improved by ~2.5x. Future work includes the development of Network on Chip (NoC), which is expected to further increase the communication throughput; as well as the standardization and automation of our system design, which will be carried out in line with the enablement of other high-level synthesis tools, to allow application developers to benefit from the system in a more efficient manner

Ensimmäinen ja toinen käsikirjoitusversio väitöskirjaa varten

This publication contains the first and the second manuscript version for LauriLahti’s doctoral dissertation in 2015 "Computer-assisted learning based on cumulative vocabularies, conceptual networks and Wikipedia linkage".Tämä julkaisu sisältää ensimmäisen ja toisen käsikirjoitusversion Lauri Lahden väitöskirjaan vuonna 2015 "Tietokoneavusteinen oppiminen perustuen karttuviin sanastoihin, käsiteverkostoihin ja Wikipedian linkitykseen".Not reviewe