Research Papers: Design Automation

A Goal-Oriented, Sequential, Inverse Design Method for the Horizontal Integration of a Multistage Hot Rod Rolling System

[+] Author and Article Information
Anand Balu Nellippallil

School of Aerospace and Mechanical Engineering,
University of Oklahoma,
202 W. Boyd Street,
Suite. 218,
Norman, OK 73019
e-mail: anand.balu@ou.edu

Kevin N. Song

School of Aerospace and Mechanical Engineering,
University of Oklahoma,
202 W. Boyd Street,
Suite. 219,
Norman, OK 73019
e-mail: kevin.song@ou.edu

Chung-Hyun Goh

Department of Mechanical Engineering,
University of Texas at Tyler,
3900 University Blvd.,
RBN 1012,
Tyler, TX 75799
e-mail: cgoh@uttyler.edu

Pramod Zagade

Tata Research Development and
Design Centre,
54-B, Hadapsar Industrial Estate,
Pune 411013, Maharashtra, India
e-mail: pramod.zagade@tcs.com

B. P. Gautham

Tata Research Development and
Design Centre,
54-B, Hadapsar Industrial Estate,
Pune 411013, Maharashtra, India
e-mail: bp.gautham@tcs.com

Janet K. Allen

Fellow ASME
John and Mary Moore Chair and Professor,
School of Industrial and Systems Engineering,
University of Oklahoma,
202 W. Boyd Street,
Suite 116,
Norman, OK 73019
e-mail: janet.allen@ou.edu

Farrokh Mistree

Fellow ASME
L.A. Comp Chair and Professor,
School of Aerospace and Mechanical Engineering,
University of Oklahoma,
865 Asp Avenue,
Felgar Hall, Rm. 306,
Norman, OK 73019
e-mail: farrokh.mistree@ou.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 17, 2016; final manuscript received December 12, 2016; published online January 19, 2017. Assoc. Editor: Kazuhiro Saitou.

J. Mech. Des 139(3), 031403 (Jan 19, 2017) (16 pages) Paper No: MD-16-1221; doi: 10.1115/1.4035555 History: Received March 17, 2016; Revised December 12, 2016

The steel manufacturing process is characterized by the requirement of expeditious development of high quality products at low cost through the effective use of available resources. Identifying solutions that meet the conflicting commercially imperative goals of such process chains is hard using traditional search techniques. The complexity in such a problem increases due to the presence of a large number of design variables, constraints and bounds, conflicting goals and the complex sequential relationships of the different stages of manufacturing. A classic example of such a manufacturing problem is the design of a rolling system for manufacturing a steel rod. This is a sequential process in which information flows from first rolling stage/pass to the last rolling pass and the decisions made at first pass influence the decisions that are made at the later passes. In this context, we define horizontal integration as the facilitation of information flow from one stage to another thereby establishing the integration of manufacturing stages to realize the end product. In this paper, we present an inverse design method based on well-established empirical models and response surface models developed through simulation experiments (finite-element based) along with the compromise decision support problem (cDSP) construct to support integrated information flow across different stages of a multistage hot rod rolling system. The method is goal-oriented because the design decisions are first made based on the end requirements identified for the process at the last rolling pass and these decisions are then passed to the preceding rolling passes following the sequential order in an inverse manner to design the entire rolling process chain to achieve the horizontal integration of stages. We illustrate the efficacy of the method by carrying out the design of a multistage rolling system. We formulate the cDSP for the second and fourth pass of a four pass rolling chain. The stages are designed by sequentially passing the design information obtained after exercising the cDSP for the last pass for different scenarios and identifying the best combination of design variables that satisfies the conflicting goals. The cDSP for the second pass helps in integrated information flow from fourth to first pass and in meeting specified goals imposed by the fourth and third passes. The end goals identified for this problem for the fourth pass are minimization of ovality (quality) of rod, maximization of throughput (productivity), and minimization of rolling load (performance and cost). The method can be instantiated for other multistage manufacturing processes such as the steel making process chain having several unit operations.

Copyright © 2017 by ASME
Topics: Stress , Design
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Fig. 1

The cDSP formulation [3]

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Fig. 2

cDSP-based method to predict set points [22,23]

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Fig. 3

Goal-oriented, inverse design method for manufacturing stages having sequential flow of information

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Fig. 4

(a) and (b) Oval and round passed with key dimensions

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Fig. 5

Geometry and mesh of the FE model developed for rod rolling

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Fig. 6

Cross section of rod produced using FE simulation showing the stress contours and the geometrical variables measured for calculating rod ovality

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Fig. 7

(a) and (b) Ovality responses for different variables considered

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Fig. 8

Ternary plot for Goal 1—ovality

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Fig. 9

Ternary plot for Goal 2—throughput

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Fig. 10

Ternary plot for Goal 3—Rolling load

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Fig. 11

Superimposed ternary space for all goals

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Fig. 12

Ternary plot for Goal 2—throughput with relaxed requirements

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Fig. 13

Ternary plot for Goal 3—Rolling load with relaxed requirements

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Fig. 14

Superimposed ternary spaces for all goals after changes in design preferences

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Fig. 15

Pass 1 dimensions designed

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Fig. 16

Pass 2 dimensions designed

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Fig. 17

Pass 3 dimensions designed

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Fig. 18

Pass 4 dimensions designed




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