PREMΛP: A Platform for the Realization of Engineered Materials and Products

Gautham, B. P.; Singh, Amarendra K.; Ghaisas, Smita; Reddy, Sreedhar; Mistree, Farrokh

doi:10.1007/978-81-322-1050-4_104

Cited by 19 publications

(4 citation statements)

References 7 publications

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“…It is essential to model this intent space, so as to be able to capture right kind of knowledge for the right purpose. 4. Knowledge is applicable in a certain context.…”

Section: Premλp Knowledge Engineering Frameworkmentioning

confidence: 99%

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A Knowledge Modeling Framework for Computational Materials Engineering

Yeddula

Vale

Reddy³

et al. 2016

International Conferences on Software Engineering and Knowledge Engineering

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Integrated computational materials engineering (ICME) is a new approach to the design and development of materials, manufacturing processes and products. The approach proposes using a combination of modeling and simulation, datadriven reasoning and knowledge-guided decision making. Industrialization of this approach requires strong automation support. Modeling and simulation is a highly knowledge-intensive activity and integrated design requires knowledge cutting across several design domains. For the industrialization vision to succeed, it is essential to capture this knowledge and make it available in a usable form for people not so skilled in these areas. With this motivation, we are building a comprehensive computational platform to support this emerging design paradigm. We present an overview of the platform and discuss its knowledge engineering requirements. We present a knowledge modeling framework to address these requirements.

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“…It is essential to model this intent space, so as to be able to capture right kind of knowledge for the right purpose. 4. Knowledge is applicable in a certain context.…”

Section: Premλp Knowledge Engineering Frameworkmentioning

confidence: 99%

“…It should use this knowledge to systematically guide a designer towards right solutions. With this motivation, we are building a comprehensive computational platform called PREMΛP [4] that supports concurrent design of materials, manufacturing processes and engineered components. Fig.…”

Section: Introductionmentioning

confidence: 99%

A Knowledge Modeling Framework for Computational Materials Engineering

Yeddula

Vale

Reddy³

et al. 2016

International Conferences on Software Engineering and Knowledge Engineering

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“…Initial Temperature , K X 2 Refining Time, min X 3 Holding Duration, min X 4 Teeming Duration, min X 5 Start time of purging, % X 6 End time of purging, %…”

Section: Nomenclature Xmentioning

confidence: 99%

“…The requirement thus is to ensure careful control of individual process parameters of each unit process involved in steel making [1]. Efforts are going on to develop a platform based on integrated systems engineering approach [2][3] which will make it possible to address such complex problems. The idea is to (a) reduce time, cost and effort incurred in development of new materials and their manufacturing processes and (b) facilitating attainment of suitable end properties and performance specification of the products (corrosion behavior, fatigue life, spring back etc.…”

Section: Frame Of Referencementioning

confidence: 99%

Exploring the Design Set Points of Refining Operation in Ladle for Cost Effective Desulfurization and Inclusion Removal

Shukla

Anapagaddi

Singh

et al. 2015

Volume 2B: 41st Design Automation Conference

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This paper is motivated by a need identified by steel makers, namely, the need to produce steel products with new and often more stringent set of specifications and enhanced performances (such as fatigue life and corrosion behavior) using existing equipment cost-effectively. Manufacturing a steel product involves series of unit operations, each having a significant bearing on the properties of the end product. This paper focuses on studying the effect of one such unit operation, namely, ladle refining. The performance like corrosion behavior and fatigue life and properties of advanced high strength steel are greatly influenced by its cleanliness and by maintaining composition within specified bounds. Cleanliness of steel is assessed in terms of the count and nature of inclusions present and the levels of tramp elements such as sulfur, phosphorus and total oxygen present in the liquid steel. The desired composition is maintained with respect to alloying elements (Ni, Cr, Mn, etc.) that are added to impart certain properties to the steel. The ladle furnace is one of the key unit operations for carrying out deoxidation and desulfurization to maintain the levels of oxygen and sulfur within a tolerable limit. Deoxidation reaction during refining lead to formation of a number of which are deleterious in nature and should be removed. The effectiveness of the ladle operation is thus influenced by conflicting goals such as inclusion removal efficiency, desulfurization and the cost of refining. George Box is reputed to have observed that all models are wrong and some are useful. In keeping with George Box’s observation we suggest that our challenge is to determine the set points for the ladle unit operation using computational models that at best capture the essence of reality but not reality itself. Therefore, the need is to find solutions that are relatively insensitive to the inherent uncertainties embodied in the computational model while satisficing the conflicting goals. In this paper we present a method for visualizing and exploring the solution space using the compromise Decision Support Problem (cDSP) as a decision model. We illustrate the efficacy of our method, for use by steel producers, by determining the set points for a ladle, in an industrial setting.

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