TY - JOUR

T1 - Leveraging Iterative Plan Refinement for Reactive Smart Manufacturing Systems

AU - Wally, Bernhard

AU - Vyskocil, Jiri

AU - Novak, Petr

AU - Huemer, Christian

AU - Sindelar, Radek

AU - Kadera, Petr

AU - Mazak-Huemer, Alexandra

AU - Wimmer, Manuel

N1 - Publisher Copyright: © 2020 IEEE.

PY - 2021/1

Y1 - 2021/1

N2 - Industry 4.0 production systems must support flexibility in various dimensions, such as for the products to be produced, for the production processes to be applied, and for the available machinery. In this paper, we present a novel approach to design and control smart manufacturing systems. The approach (i) is reactive, i.e., responds to unplanned situations and (ii) implements an iterative refinement technique, i.e., optimizes itself during runtime in order to better accommodate production goals. For realizing these advances, we present a model-driven methodology and we provide a prototypical implementation of such a production system. In particular, we employ PDDL as our artificial intelligence environment for automated planning of production processes and combine it with one of the most prominent Industry 4.0 standards for the fundamental production system model: IEC 62264. We show how to plan the assembly of small trucks from available components and how to assign specific production operations to available production resources, including robotic manipulators and transportation system shuttles. Results of the evaluation indicate that the presented approach is feasible and that it is able to significantly strengthen the flexibility of production systems during runtime.

AB - Industry 4.0 production systems must support flexibility in various dimensions, such as for the products to be produced, for the production processes to be applied, and for the available machinery. In this paper, we present a novel approach to design and control smart manufacturing systems. The approach (i) is reactive, i.e., responds to unplanned situations and (ii) implements an iterative refinement technique, i.e., optimizes itself during runtime in order to better accommodate production goals. For realizing these advances, we present a model-driven methodology and we provide a prototypical implementation of such a production system. In particular, we employ PDDL as our artificial intelligence environment for automated planning of production processes and combine it with one of the most prominent Industry 4.0 standards for the fundamental production system model: IEC 62264. We show how to plan the assembly of small trucks from available components and how to assign specific production operations to available production resources, including robotic manipulators and transportation system shuttles. Results of the evaluation indicate that the presented approach is feasible and that it is able to significantly strengthen the flexibility of production systems during runtime.

UR - http://www.scopus.com/inward/record.url?scp=85099384059&partnerID=8YFLogxK

U2 - 10.1109/TASE.2020.3018402

DO - 10.1109/TASE.2020.3018402

M3 - Article

VL - 18.2021

SP - 230

EP - 243

JO - IEEE transactions on automation science and engineering

JF - IEEE transactions on automation science and engineering

SN - 1545-5955

IS - 1

ER -