Knowledge Base Development for Assembly Planning Using Evidence Theory.
More details
Hide details
Faculty of Machenical Engineering and Computer Science, University of Bielsko-Biala Willowa 2, 43-300 Bielsko-Biała, Poland, Poland
Izabela Kutschenreiter-Praszkiewicz   

Faculty of Machenical Engineering and Computer Science, University of Bielsko-Biala Willowa 2, 43-300 Bielsko-Biała, Poland, Poland
Submission date: 2021-12-27
Final revision date: 2022-03-06
Acceptance date: 2022-04-13
Online publication date: 2022-04-18
This paper presents an approach to assembly planning in the early phase of product development. The product specification, workstation, environment, equipment and tools are not fully known in the early stage of product development. When comparing product variants at this stage there is a lack of data that affects the efficiency of the manufacturing process. It is therefore necessary to apply methods useful in processing incomplete and uncertain data. The main indicator which helps in comparing different product variants is manufacturing time standard. This papier is focused on assembly tool selection which is one of important data influenced assembly time. Based on the proposed algorithm and case study, a tool selection method using a decision tree induced from a training set with reduced uncertainty is presented.
BONINO B., RAFFAELI R., MONTI M., GIANNINI F., 2021, A Heuristic Approach to Detect CAD Assembly Clusters, Procedia CIRP, 100, 463–468.
MODRAK V., MARTON D., BEDNAR S., 2015, The Influence of Mass Customization Strategy on Configuration Complexity of Assembly Systems, Procedia CIRP, 33, 538–543.
KERN W., RUSITSCHKA F., BAUERNHANSL T., 2016, Planning of Workstations in a Modular Automotive Assembly System, Procedia CIRP, 57, 327–332.
BAAS S., KWAKERNAAK H., 1977, Rating and Ranking of Multiple-Aspect Alternatives Using Fuzzy Sets, Automatica, 13, 47–58.
MÖHRING H., ESCHELBACHER S., GÜZEL K., KIMMELMANN M., SCHNEIDER M., ZIZELMANN C., HÄUSLER A., MENZE C., 2019, En Route to Intelligent Wood Machining – Current Situation and Future Perspectives, Journal of Machine Engineering, 19/4, 5–26.
GUERGOV S., 2018, A Review and Analysis of the Historical Development of Machine Tools into Complex Intelligent Mechatronic Systems, Journal of Machine Engineering, 18/1, 107–119.
DALVI S., 2016, Optimization of Assembly Sequence Plan Using Digital Prototyping and Neural Network, Procedia Technology, 23, 414–422.
YU H., CHEN L., YAO J., 2021, A Three-Way Density Peak Clustering Method Based on Evidence Theory, Knowledge-Based Systems, 211, 106532.
LIU H., CHEN Y., PENG X., XIE J., 2011, A Classification Method of Glass Defect Based on Multiresolution and Information Fusion, Int. J. Adv. Manuf. Technol., 56, 1079–1090.
QING Y., XIAOPING W., CHANGHONG Z., 2009, An Intrusion Detection System Based on Evidence Theory and Rough Set Theory, Journal of Electronics, 26, 6, 777–781.
DYMOVA L., SEVASTIANOV P., BARTOSIEWICZ P., 2010, A New Approach to the Rule-Base Evidential Reasoning: Stock Trading Expert System Application, Expert Systems with Applications, 37, 5564–5576.
CHEN B., FENG J., 2014, Multisensor Information Fusion of Pulsed GTAW Based on Improved D-S Evidence Theory, Int. J. Adv. Manuf. Technol., 71, 91–99.
WU X., ZHAO J., TONG Y., 2018, Big Data Analysis and Scheduling Optimization System Oriented Assembly Process for Complex Equipment, IEEE Access, 6, 36479–36486, DOI: 10.1109/ACCESS.2018.2852791.
LV Y., QUIN W., YANG J., ZHANG J., 2018, Adjustment Mode Decision Based on Support Vector Data Description and Evidence Theory for Assembly Lines, Industrial Management & Data Systems, 118, 8, 1711–1726, DOI: 10.1108/IMDS-01-2017-0014.
ANTONSSON E.K., OTTO K.N., 1995, Imprecision in Engineering Design, Journal of Mechanical Design, 117(B), 25–32, DOI: 10.1115/1.2836465.
MICHNIEWICZ J., REINHART G., BOSCHERT S., 2016, CAD-Based Automated Assembly Planning for Variable Products in Modular Production Systems, 6th CIRP Conference on Assembly Technologies and Systems (CATS), Procedia CIRP, 44, 44–49.
SINHA S., FRANCIOSA P., CEGLAREK D., 2021, Object Shape Error Response Using Bayesian 3-D Convolutional Neural Networks for Assembly Systems With Compliant Parts, IEEE Transactions on Industrial Informatics, 17, 10, 6676-6686.
ONG S.K., PANG Y., NEE A.Y.C., 2007, Augmented Reality Aided Assembly Design and Planning, Annals of the CIRP, 56/1, 49–52.
FRANCIOSA P., CEGLAREK D., 2015, Hierarchical Synthesis of Multi-Level Design Parameters in Assembly System, CIRP Annals – Manufacturing Technology, 64, 149–152.
MAROPOULOS P.G., VICHARE P., MARTIN O., MUELANER J., SUMMERS M.D., KAYANI A., 2011, Early Design Verification of Complex Assembly Variability Using a Hybrid – Model Based and Physical Testing – Methodology. CIRP Annals – Manufacturing Technology, 60, 207–210.
PARALIKAS J., FYSIKOPOULOS A., PANDREMENOS J., CHRYSSOLOURIS G., 2011, Product Modularity and Assembly Systems: An Automotive Case Study, CIRP Annals – Manufacturing Technology 60, 165–168.
KRÜGER J., BERNHARDT R., SURDILOVIC D., 2006, Intelligent Assist Systems for Flexible Assembly, CIRP Annals, 55/1, 29–32.
BOERL C.R., PEDRAZZOLI P., SACCOL M., RINALDI R., PASCALE G., AVAI A., 2001, Integrated Computer Aided Design for Assembly Systems, CIRP Annals, 50/1, 17–20.
LANGE S., SCHMIDT H., SELIGER G., 2000, Product and Assembly Design for a Fibre Reinforced Plastic Track Wheel, CIRP Annals, 49/1, 105–108.
BLEY H., FRANKE C., 2004, Integration of Product Design and Assembly Planning in the Digital Factory, CIRP Annals, 53/1, 25–30.
BATTAIA O., DOLGUI A., HERAGU S.S., MEERKOV S.M., TIWARI M.K., 2019, Design for Manufacturing and Assembly/Disassembly: Joint Design of Products and Production Systems, International Journal of Production Research, 56/24, 7181–7189, DOI: 10.1080/00207543.2018.1549795.
PUTZ M., LANGER T., 2012, Determination of Extended Availability and Productivity for Assembly Systems using existing data base, CIRP Annals – Manufacturing Technology, 61, 17–20.
LI X., ZHANG S., HUANG R., HUANG B., XU C.,4 ZHANG Y., 2018, A Survey of Knowledge Representation Methods and Applications in Machining Process Planning, The International Journal of Advanced Manufacturing Technology, 98, 3041–3059.
KUSIAK A., HERAGU S.S., 1988, KBSES: A Knowledge-Based System for Equipment Selection, The International Journal of Advanced Manufacturing Technology, 3/3, 97–109.
GEISKOPF F., KIEFER F., CAILLAUD E., 2007, Consistency in Tool Selection for Cast Iron Milling, Int. J. Adv. Manuf. Technol., 34, 9–20.
ZARANDI M., MANSOUR S., HOSSEINIJOU S., AVAZBEIGI M., 2011, A Material Selection Methodology and Expert System for Sustainable Product Design, Int. J. Adv. Manuf. Technol., 57, 885–903.
ZHANG Y., LUO X., ZHANG H., SUTHERLAND J., 2014, A Knowledge Representation for Unit Manufacturing Processes, Int. J. Adv. Manuf. Technol., 73, 1011–1031.
KUTSCHENREITER-PRASZKIEWICZ I., 2021, Standard Assembly Time Setting in an Early Stage Of Product Development, Journal of the University of Applied Sciences Mittweida Scientific Reports, 2, 81–92, DOI: 10.48446/opus-12311.
KUTSCHENREITER-PRASZKIEWICZ I., 2020, Neural Network Application for Time Standards Setting in Assembly and Disassembly, Journal of Machine Engineering, 20/3, 106–116.
Collective work, 2009, Rules for mounting and dismounting bearings, Inżynieria & Utrzymanie Ruchu, (in Polish),