Res. Agr. Eng., 2021, 67(3):123-130 | DOI: 10.17221/79/2020-RAE
Influence of the moisture content and speed on the cutting force and energy of tannia cormelsOriginal Paper
- 1 Department of Agricultural and Environmental Engineering, University of Ibadan, Ibadan, Nigeria
- 2 Department of Agricultural and Bio-Environmental Engineering, Oyo State College of Agriculture and Technology, Igboora, Nigeria
- 3 Department of Agricultural Engineering, Adeleke University, Ede, Osun State, Nigeria
- 4 Department of Agricultural Engineering, Federal College of Forestry Mechanization, Afaka, Kaduna
- 5 National Centre for Agricultural Mechanization, Idofian, Kwara State, Nigeria
This study investigated the influence of the moisture content and speed on the cutting force and energy of tannia cormels using the response surface methodology (RSM). The moisture content and cutting speed were varied over five levels each [95.79, 113.68, 136.68, 168.42, 242.11% moisture content (dry basis) and 10, 15, 20, 25, 30 mm×min-1, respectively]. The highest and lowest cutting forces were 114.09 and 63.99 N at the corresponding moisture contents of 168.42 and 113.68% and at cutting speeds of 10 and 20 mm×min-1, respectively. The highest and lowest cutting energies of 0.92 and 0.49 J were both obtained at a 136.68% moisture content, at the 10 and 20 mm×min-1 cutting speeds, respectively. The regression models for predicting the cutting force and energy as a function of the cutting speed and moisture content showed that there was no linear relationship between the investigated properties and the independent variables considered which could be attributed to the non-homogeneous nature of tannia cormels. The optimum cutting force and energy were 72.89 N and 0.60 J, respectively, at a 95.79% moisture content and a 22.33 mm×min-1 speed with a desirability of 0.80. These findings could serve as a guide for the development of chipping and cutting machines for tannia cormels.
Keywords: cocoyam; cutting resistance; cutting speed; moisture-dependence; regression models
Published: September 30, 2021 Show citation
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