Design of an expert system for the optimization of steel compositions and process route

Manohar, P.; Shivathaya, S.S.; Ferry, Michael

doi:10.1016/s0957-4174(99)00030-5

Cited by 14 publications

(4 citation statements)

References 12 publications

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“…The yield strength is the key parameter for engineers when establishing failure criteria in ductile materials, since once exceeded the material begins to be considered plastically deformed and therefore does not retain the ideal properties in the performance of its engineering function. The optimization of steel composition has been, and is, widely studied [29,30], however neural network studies rarely focus on it. In this connection, many recent studies on steels try to explain the connection between composition and mechanical properties by using machine learning or neural networks, among others [11,[31][32][33].…”

Section: Related Workmentioning

confidence: 99%

Influential Yield Strength of Steel Materials with Return Random Walk Gravity Centrality

Rodríguez,

Curado,

Rodríguez

et al. 2024

Mathematics

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In complex networks, important nodes have a significant impact, both functional and structural. From the perspective of data flow pattern detection, the evaluation of the importance of a node in a network, taking into account the role it plays as a transition element in random paths between two other nodes, has important applications in many areas. Advances in complex networks and improved data generation are very important for the growth of computational materials science. The search for patterns of behavior of the elements that make up steels through complex networks can be very useful in understanding their mechanical properties. This work aims to study the influence of the connections between the elements of steel and the impact of these connections on their mechanical properties, more specifically on the yield strength. The patterns found in the results show the significance of the proposed approach for the development of new steel compositions.

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Section: Related Workmentioning

confidence: 99%

Influential Yield Strength of Steel Materials with Return Random Walk Gravity Centrality

Rodríguez,

Curado,

Rodríguez

et al. 2024

Mathematics

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“…• Development of KBES is a complex task because the metal forming knowledge is often ill-structured, implicit, difficult to systematise and involves a large number of rules ( [90], [99]).…”

Section: Q T and Q L Of Integrated Strategies In Metal Forming: Observationsmentioning

confidence: 99%

A review of rolling system design optimisation

Oduguwa

Roy

2006

International Journal of Machine Tools and Manufacture

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Rapid product development and efficient use of existing resources are key competitive drivers in the steel industry and it is imperative that solution strategies are capable of delivering high quality solutions at low cost. However, traditional search techniques for rolling system design (RSD) are ad hoc and users of them find it very difficult in satisfying the required commercial imperatives. This paper presents a comprehensive review of approaches for dealing with RSD problems over the years in terms of modelling and optimisation of both quantitative and qualitative aspects of the process. It critically analyses how such strategies contribute to developing timely low cost optimal solutions for the steel industry. The paper also explores the soft computing based technique as an emerging technology for a more structured RSD optimisation. The study has identified challenges posed by RSD for an algorithmic optimisation approach, especially for evolutionary computing based techniques.

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“…More recent research focus has been on the application of Artificial Intelligence (AI) to the hot rolling of the steel [3,4] . The primary objective of steel rolling to obtain the desired shape has been augmented by the need to produce steel products with a range of desired properties such as strength, toughness, weldability, and formability at low overall cost.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Manufacturing Process Education via Computer Simulation and Visualization

Manohar¹,

Acharya²,

Wu³

2014

JEL

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Acharya joined RMU in Spring 2005 after serving 15 years in the Software Industry. With US Airways, Acharya was responsible for creating a conceptual design for a Data Warehouse which would integrate the different data servers the company used. With i2 Technologies he led the work on i2's Data Mining product "Knowledge Discover Framework" and at CEERD (Thailand) he was the product manager of three energy software products (MEDEE-S/ENV, EFOM/ENV and DBA-VOID) which were/are used in Asian and European countries by both governmental and non-governmental organizations. Acharya has a M.Eng. Enhancing Manufacturing Process Education via Computer Simulation and Visualization AbstractIndustrially significant metal manufacturing processes such as melting, casting, rolling, forging, machining, and forming are multi-stage, complex processes that are labor, time, and capital intensive. Mathematical modeling of these processes provides a theoretical framework for understanding the process variables and their effects on productivity and quality. However it is usually difficult to provide the students with hands-on experience of experimentation with process parameters to arrive at optical process design. In order to solve this problem, interdisciplinary student projects were undertaken at author's institution to develop computer simulation tools that would facilitate process visualization, experimentation, exploration, design and optimization.The first system described in this paper is the visualization of metal ingot production schedule in an industrial setting that provides a basis for interactive decisions. A metal ingot casting is simulated by designing the abstract machine models to visualize capacity and loading of the production schedule. The graphical user interface is created to visualize the schedule according to the specific characteristics of the machines. The application of computer tools may also be extended to support other important functionalities such as tracking availability of raw materials, projection of inventory due to production overage, as well as critical business analysis. The students thus are exposed to the comprehensive process analysis. Another example of process simulation presented in this paper is the design and analysis of flexible rolling technology in industrial processing of C -Mn and Low C -microalloyed steels. Process simulation tools designed by the students allow new process sequences to be generated by breaking down existing process routes in to key elements and then by recombining them to generate novel alternative and more efficient hot processing sequences. This enables the identification of an optimal process sequence for specified steel compositions that also satisfies simultaneous design criteria such as process feasibility and property maximization.It is proposed that incorporation of such computer simulation tools in the pedagogy would be highly effective to enhancing and enriching undergraduate manufacturing education.

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Design of an expert system for the optimization of steel compositions and process route

Cited by 14 publications

References 12 publications

Influential Yield Strength of Steel Materials with Return Random Walk Gravity Centrality

Influential Yield Strength of Steel Materials with Return Random Walk Gravity Centrality

A review of rolling system design optimisation

Enhancing Manufacturing Process Education via Computer Simulation and Visualization

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