Author, institution: Justinas Tilindis, Kauno University of Technology
Scientific Supervisor: Prof. Dr. Vytautas KLEIZA (Kaunas University of Technology, Technological Sciences, Mechanical Engineering – 09T).
Science area, field: Technological Sciences, Mechanical Engineering – 09T
The Doctoral Dissertation is available at the library of Kaunas University of Technology (K. Donelaičio St. 20, Kaunas)
Dissertation Defense Board of Mechanical Engineering Science Field:
Assoc. Prof. Dr. Giedrius JANUŠAS (Kaunas University of Technology, Technological Sciences, Mechanical Engineering – 09T) – chairman,
Prof. Dr. Rimvydas GAIDYS (Kaunas University of Technology, Technological Sciences, Mechanical Engineering – 09T);
Prof. Dr. Volodymyr HUTSAYLYUK (Military University of Technology, Technological Sciences, Mechanical Engineering – 09T);
Prof. Dr. Artūras ŠTIKONAS (Vilnius University, Physical Sciences, Informatics – 09P).
Annotation:
Nowadays, the manual assembly of production items by humans continues, although this manufacturing technology is being widely replaced by robotic equipment; however, there are still many production fields where mechanical human work is inevitable due to a variety of reasons, such as labor or equipment cost, task complexity and etc. Moreover, long time trend in the manufacturing industry shows the fall of mass production and the spread of mass customization. Therefore, manufacturing companies are forced to reduce order quantities, increase product variety and shorten production lead times. In the manual assembly, when the order quantities are small or intermittent, there is no possibility of completing the learning phase, so the production is always at the beginning of the learning curve. However, in order to react to changing customer demand, there is a necessity to reduce the production time. In this research, the problem of direct production time is addressed and solved by learning time reduction. Also, the new learning curve models that satisfy the general properties of the learning curves and approximate learning processes more accurately are created and proved. In addition, it is also proved that the parameters of the learning curve can be estimated by using deterministic (non-statistical) methods. Finally, in this research it is proved that efficiency of the complex manual assembly increases when the process is split into a certain (optimal) number of simpler processes.
