Browsing by Author "Jen, T. C."

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Evaluation of palm kernel oil as cutting lubricant in turning AISI 1039 steel using taguchi-grey relational analysis optimization technique
(Elsevier, 2023) Alaba, E. S.; Kazeem, R. A.; Adebayo, A. S.; Petinrin, M. O.; Ikumapayi, O. M.; Jen, T. C.; Akinlabi, E. T.
Cutting fluids have a known negative impact on productivity, human health, and the environment in the manufacturing sector. A suitable method for reducing the effect of cutting fluids on human health and the environment is minimum quantity lubrication (MQL). In this experiment, AISI 1039 steel was machined using vegetable oil lubricant and MQL. A chemical method was used to extract vegetable oil from palm kernel seeds. Then, using established techniques, the physicochemical and lubricity properties of palm kernel oil (PKO) were ascertained. The Taguchi L9 (33) orthogonal array served as the basis for the planning of the experimental design. Process parameters such as surface roughness, chip thickness ratio, cutting temperature, and material removal rate were measured during the turning operations. The multi-response outputs from TGRA were considered to simultaneously optimize the cutting parameters namely depth of cut, feed rate, and spindle speed. At a temperature of 55◦C, 180 min, and particle sizes of 0.2–0.5 mm, an oil yield of 55% by weight was obtained. The viscosity at 40◦C, specific gravity, pour, fire, cloud, and flash points of the raw PKO were 117.6 mm2/s, 0.8940 mg/ml, 21◦C, 231◦C, 22.3 ◦C and 227◦C, respectively. The surface roughness and cutting temperature of PKO improved by 44% and 12%, respectively, when compared with mineral oil. The findings of this research confirmed the effectiveness of the integrated Taguchi-grey relational analysis (TGRA) optimization method and established an experimental foundation for the use of PKO minimum quantity lubrication turning.
Forecast of the trend in sales data of a confectionery baking industry using exponential smoothing and moving average models
(2023-02) Kazeem, R. A.; Petinrin, M. O.; Akhigbe, P. O.; Jen, T. C.; Akinlabi, E. T.; Akinlabi, S. A.; Ikumapayi, O. M.
Starch-containing foods such as bread, pastries, and cakes are usually baked at a moderately high temperature in an oven. When these products are later exposed to room temperature, the associated gelatinized starch begins to harden which causes retrogradation and molecular realignment. Due to this circumstance, manufacturers need to have a fairly accurate estimate of products demand in order to determine the precise amount of baking powder and additives for use in their production so as not to incur losses in their business arising from the stale and consequentially unsalable products. This research was therefore focused on selecting the best forecasting model using a prominent confectionery firm in Abeokuta, Ogun State, Nigeria as a case study. The study was based on 24-week operational period sales data collected from the company. The moving average model and the exponential smoothing model were the two forecasting models considered in this research. The data obtained was thoroughly reviewed and the results of the forecasting models were compared. The most effective model was the exponential smoothing model as it produced the lowest mean absolute percentage error on the average of 3.7347 for the cumulative days of sales under review as against the 15.1713 for the moving average model. However, the exponential smoothing model was considered the best forecasting model for minimizing forecasting error in this study.