173 resultados para Industrial and Manufacturing Engineering


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Natural fibers can be used in rotational molding process to obtain parts with improved mechanical properties. Different approaches have been followed in order to produce formulations containing banana or abaca fiber at 5% weight, in two- and three-layer constructions. Chemically treated abaca fiber has also been studied, causing some problems in processability. Fibers used have been characterized by Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), optical microscopy, and single-fiber mechanical tests. Rotomolded parts have been tested for tensile, flexural, and impact properties, demonstrating that important increases in elastic modulus are achieved with these fibers, although impact properties are reduced. © 2013 Copyright Taylor and Francis Group, LLC.

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The article presents cost modeling results from the application of the Genetic-Causal cost modeling principle. Industrial results from redesign are also presented to verify the opportunity for early concept cost optimization by using Genetic-Causal cost drivers to guide the conceptual design process for structural assemblies. The acquisition cost is considered through the modeling of the recurring unit cost and non-recurring design cost. The operational cost is modeled relative to acquisition cost and fuel burn for predominately metal or composites designs. The main contribution of this study is the application of the Genetic-Causal principle to the modeling of cost, helping to understand how conceptual design parameters impact on cost, and linking that to customer requirements and life cycle cost.

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This is an invited contribution in a special issue of the Journal of Cement and Concrete Composites

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This paper points out a serious flaw in dynamic multivariate statistical process control (MSPC). The principal component analysis of a linear time series model that is employed to capture auto- and cross-correlation in recorded data may produce a considerable number of variables to be analysed. To give a dynamic representation of the data (based on variable correlation) and circumvent the production of a large time-series structure, a linear state space model is used here instead. The paper demonstrates that incorporating a state space model, the number of variables to be analysed dynamically can be considerably reduced, compared to conventional dynamic MSPC techniques.

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This is the first paper that shows and theoretically analyses that the presence of auto-correlation can produce considerable alterations in the Type I and Type II errors in univariate and multivariate statistical control charts. To remove this undesired effect, linear inverse ARMA filter are employed and the application studies in this paper show that false alarms (increased Type I errors) and an insensitive monitoring statistics (increased Type II errors) were eliminated.

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With the intention of introducing unique and value-added products to the market, organizations have become more conscious of how to best create knowledge as reported by Ganesh Bhatt in 2000 in 'Information dynamics, learning and knowledge creation in organizations'. Knowledge creation is recognized as having an important role in generating and sustaining a competitive advantage as well as in meeting organizational goals, as reported by Aleda Roth and her colleagues in 1994 in 'The knowledge factory for accelerated learning practices.' One of the successful ingredients of value management (VM) is its utilization of diverse knowledge resources, drawing upon different organizational functions, professional disciplines, and stakeholders, in a facilitated team process. Multidisciplinary VM study teams are viewed as having high potential to innovate due to their heterogeneous nature. This paper looks at one of the VM workshop's major benefits, namely, knowledge creation. A case study approach was used to explore the nature, processes, and issues associated with fostering a dynamic knowledge creation capability within VM teams. The results indicate that the dynamic knowledge creating process is embedded in and influenced by managing team constellation, creating shared awareness, developing shared understanding, and producing aligned action. The catalysts that can speed up the processes are open dialogue and discussion among participants. This process is enhanced by the use of facilitators, skilled at extracting knowledge.

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In this paper the parameters of cement grout affecting rheological behaviour and compressive strength are investigated. Factorial experimental design was adopted in this investigation to assess the combined effects of the following factors on fluidity, rheological properties, induced bleeding and compressive strength: water/binder ratio (W/B), dosage of superplasticiser (SP), dosage of viscosity agent (VA), and proportion of limestone powder as replacement of cement (LSP). Mini-slump test, Marsh cone, Lombardi plate cohesion meter, induced bleeding test, coaxial rotating cylinder viscometer were used to evaluate the rheology of the cement grout and the compressive strengths at 7 and 28 days were measured. A two-level fractional factorial statistical model was used to model the influence of key parameters on properties affecting the fluidity, the rheology and compressive strength. The models are valid for mixes with 0.35-0.42 W/B, 0.3-1.2% SP, 0.02-0.7% VA (percentage of binder) and 12-45% LSP as replacement of cement. The influences of W/B, SP, VA and LSP were characterised and analysed using polynomial regression which can identify the primary factors and their interactions on the measured properties. Mathematical polynomials were developed for mini-slump, plate cohesion meter, inducing bleeding, yield value, plastic viscosity and compressive strength as function of W/B, SP, VA and proportion of LSP. The statistical approach used highlighted the limestone powder effect and the dosage of SP and VA on the various rheological characteristics of cement grout