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Research Papers

State of the Art Review on Process, System, and Operations Control in Modern Manufacturing

[+] Author and Article Information
Dragan Djurdjanovic

Department of Mechanical Engineering,
University of Texas,
Austin, TX 78712
e-mail: dragand@utexas.edu

Laine Mears

Department of Automotive Engineering,
Clemson University,
Greenville, SC 29607
e-mail: mears@clemson.edu

Farbod Akhavan Niaki

Department of Automotive Engineering,
Clemson University,
Greenville, SC 29607
e-mail: fakhava@g.clemson.edu

Asad Ul Haq

Department of Mechanical Engineering,
University of Texas,
Austin, TX 78712
e-mail: asadulhaq@utexas.edu

Lin Li

University of Illinois,
Chicago, IL 60607
e-mail: linli@uic.edu

Manuscript received July 19, 2017; final manuscript received August 15, 2017; published online March 30, 2018. Editor: Y. Lawrence Yao.

J. Manuf. Sci. Eng 140(6), 061010 (Mar 30, 2018) (18 pages) Paper No: MANU-17-1461; doi: 10.1115/1.4038074 History: Received July 19, 2017; Revised August 15, 2017

Dramatic advancements and adoption of computing capabilities, communication technologies, and advanced, pervasive sensing have impacted every aspect of modern manufacturing. Furthermore, as society explores the Fourth Industrial Revolution characterized by access to and leveraging of knowledge in the manufacturing enterprise, the very character of manufacturing is rapidly evolving, with new, more complex processes, and radically, new products appearing in both the industries and academe. As for traditional manufacturing processes, they are also undergoing transformations in the sense that they face ever-increasing requirements in terms of quality, reliability, and productivity, needs that are being addressed in the knowledge domain. Finally, across all manufacturing we see the need to understand and control interactions between various stages of any given process, as well as interactions between multiple products produced in a manufacturing system. All these factors have motivated tremendous advancements in methodologies and applications of control theory in all aspects of manufacturing: at process and equipment level, manufacturing systems level, and operations level. Motivated by these factors, the purpose of this paper is to give a high-level overview of latest progress in process and operations control in modern manufacturing. Such a review of relevant work at various scales of manufacturing is aimed not only to offer interested readers information about state-of-the art in control methods and applications in manufacturing, but also to give researchers and practitioners a vision about where the direction of future research may be, especially in light of opportunities that lay as one concurrently looks at the process, system and operation levels of manufacturing.

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Figures

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Fig. 1

Adaptive control. Operational strategy(ies) and process performance assessment are used directly to modify the machining path plan in real time (Ref. [2]).

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Fig. 2

MPC. The MPC controller uses a model of the process to predict behavior, then optimizes control action for the next time-step. Penalty is placed on large changes to the input(s).

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Fig. 3

Smith predictor. To address time delay, such as introduced by model calculations, an additional inner loop is included to predict the behavior in between actual feedback signals to the controller using a generalized system model.

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Fig. 4

Illustration of a multistage manufacturing process

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Fig. 5

Illustration of R2R control

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Fig. 6

Illustration of a mixed product manufacturing environment

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Fig. 7

Manufacturing operation decision-making outline

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Fig. 8

Hybrid model with dynamic reliability threshold [111]

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Fig. 9

On-line bottleneck control framework [254]

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Fig. 10

Illustration of APB programming framework [271]

Tables

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