Comparative Study of Moore and Mealy Machine Models Adaptation in Black Soap Production

Akinboro, Solomon and Omotosho, Adebayo and Adeyiga, Johnson and Effiom, Eme (2017) Comparative Study of Moore and Mealy Machine Models Adaptation in Black Soap Production. Nigerian Journal of Technology (NIJOTECH), 36 (2). pp. 603-610. ISSN 2467-8821

Text Adebayo Omotosho COMPARATIVE STUDY OF MOORE AND MEALY MACHINE MODELS ADAPTATION IN BLACK SOAP PRODUCTION.pdf Download (1MB)

Abstract

Information and Communications Technology has influenced the need for automated machines that can carry out important production procedures and, automata models are among the computational models used in design and construction of industrial processes. The production process of the popular African Black Soap (ABS), which is widely used for the alleviation of various skin ailments among other uses, is still mostly done manually. In this paper, an automata model was developed for ABS manufacturing process using Moore and Mealy Finite State Machines. Simulation of standard input of raw materials into the machine was achieved using Input-Output methodology with pseudo random number generated input data. The output of this methodology served as the actual fractional contents of the raw ingredients fed into the Moore and Mealy machine. The automata models were simulated for ten runs using application developed with Microsoft Visual C#. The performance of each machine was assessed in order to determine, the most efficient of the machines using execution time as parameter. The simulation results showed that the Mealy Machine is faster than the Moore Machine as evident by the time overhead of over 3hrs in the production time process of ABS.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > TS Manufactures
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	Dr Adebayo Omotosho
Date Deposited:	30 Nov 2018 10:57
Last Modified:	30 Nov 2018 10:57
URI:	https://eprints.lmu.edu.ng/id/eprint/1456

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