The paper reviews applications of data mining in manufacturing engineering, in particular production processes, operations, fault detection, maintenance, decision support, and product quality improvement. Customer relationship management, information integration aspects, and standardization are also briefly discussed. This review is focused on demonstrating the relevancy of data mining to manufacturing industry, rather than discussing the data mining domain in general. The volume of general data mining literature makes it difficult to gain a precise view of a target area such as manufacturing engineering, which has its own particular needs and requirements for mining applications. This review reveals progressive applications in addition to existing gaps and less considered areas such as manufacturing planning and shop floor control.

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In this paper we have proposed a new family of distributions; the Topp-Leone family of distributions. We have given general expression for density and distribution function of the new family. Expression for moments and hazard rate has also been given. We have also given an example of the proposed family.

In this article we have proposed a general transmuted family of distributions with emphasis on the cubic transmuted (CT) family of distributions. This new class of distributions provide additional flexibility in modeling the bi-modal data. The proposed cubic transmuted family of distributions has been linked with the T − X family of distributions proposed by Alzaatreh et al. (2013). Some members of the proposed family of distributions have been discussed. The cubic transmuted exponential distribution has been discussed in detail and various statistical properties of the distribution have been explored. The maximum likelihood estimation for parameters of cubic transmuted exponential distribution has also been discussed alongside Monte Carlo simulation study to assess the performance of the estimation procedure. Finally, the cubic transmuted exponential distribution has been fitted to real datasets to investigate it's applicability.

Abstract:Communication, knowledge sharing and awareness of available expertise are complex issues for any multi-discipline team. Complexity increases substantially in extended enterprise environments. The concepts of an MSE moderator have previously been considered in environments with shared information models and vocabularies. These concepts are now translated to the realm of extended enterprises where inevitably individual partners will have their own terminology and information sources. An MSE ontology is proposed to enable the operation of an extended enterprise MSE Moderator, to provide common understanding of manufacturingrelated terms, and therefore to enhance the semantic interoperability and reuse of knowledge resources within globally extended manufacturing teams.