TY - GEN

T1 - Probabilistic Simulation and Determination of Sojourn Time Distribution in Manufacturing Processes

AU - Zumsande, Johannes

AU - Kortmann, Karl-Philipp

AU - Wielitzka, Mark

AU - Ortmaier, Tobias

N1 - Funding information: Supported by the Fundamental Research Fundation for Universities of Heilongjiang Province (LGYC2018JC011). This work has been supported by the European Regional Development Fund (ERDF) grant ZW 6-85018381.

PY - 2019/8

Y1 - 2019/8

N2 - The ongoing transformation from automated to smart manufacturing offers new capabilities to fulfill the multidimensional challenges and demands of a globalized market. Smart in this context covers methods of data mining and machine learning to generate high-value process information. As modern manufacturing industry is characterized by a big amount of heterogeneous data from different sources, the temporal relations between the data is generally unknown. Token-based solutions (such as RFID chips) offer a highly reliable tracking but are not suitable in every scenario; possible reasons are environmental conditions or fluid products. In this paper, we introduce a directed graph model of a stochastic manufacturing process. This can be used for process simulation to generate data from different plant topologies and transition conditions. It is validated by means of a plant model representing an automated handling process containing three industrial robots. As the sojourn time distributions of the work pieces are not only dependent on the work times but also on the transition conditions like capacity and workload of the workstations, it has to be identified considering both. The sojourn time distributions can be transferred to a new process graph model, suitable for work piece tracking.

AB - The ongoing transformation from automated to smart manufacturing offers new capabilities to fulfill the multidimensional challenges and demands of a globalized market. Smart in this context covers methods of data mining and machine learning to generate high-value process information. As modern manufacturing industry is characterized by a big amount of heterogeneous data from different sources, the temporal relations between the data is generally unknown. Token-based solutions (such as RFID chips) offer a highly reliable tracking but are not suitable in every scenario; possible reasons are environmental conditions or fluid products. In this paper, we introduce a directed graph model of a stochastic manufacturing process. This can be used for process simulation to generate data from different plant topologies and transition conditions. It is validated by means of a plant model representing an automated handling process containing three industrial robots. As the sojourn time distributions of the work pieces are not only dependent on the work times but also on the transition conditions like capacity and workload of the workstations, it has to be identified considering both. The sojourn time distributions can be transferred to a new process graph model, suitable for work piece tracking.

KW - directed graphs

KW - Process modeling

KW - sojourn time determination

UR - http://www.scopus.com/inward/record.url?scp=85072400393&partnerID=8YFLogxK

U2 - 10.1109/icma.2019.8816629

DO - 10.1109/icma.2019.8816629

M3 - Conference contribution

AN - SCOPUS:85072400393

SN - 978-1-7281-1698-3

SN - 978-1-7281-1700-3

T3 - International Conference on Industrial Mechatronics and Automation

SP - 2094

EP - 2099

BT - 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 16th IEEE International Conference on Mechatronics and Automation, ICMA 2019

Y2 - 4 August 2019 through 7 August 2019

ER -