Quality Engineering using Design of Experiments

Phadke, M. S.

doi:10.1007/978-1-4684-1472-1_3

Cited by 1,089 publications

(1,193 citation statements)

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“…In the Taguchi method, the signalto-noise ratios ( ratio) serve as the objective functions for optimization, help in data analysis, and predict the optimum results (Phadke, 2009). The  ratio is the ratio of the mean to the standard deviation, and is the measure of the deviation of the response (dependent parameter) from the desired value.…”

Section: Identification Of Optimum Levels Of Independent Parametersmentioning

confidence: 99%

Development of a Hopper-Type Planting Device for a Walk-Behind Hand-Tractor-Powered Vegetable Transplanter

Dihingia

Gautam

Sarma³

2016

Journal of Biosystems Engineering

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Purpose: In order to ensure that vegetable seedlings (with a soil block around their roots) are planted in an upright orientation after metering in a vegetable transplanter, they need to be dropped freely from a certain height. The walkbehind hand-tractor-powered machines do not have sufficient space to drop the seedlings from that height. In the present work, a hopper-type planting device was developed for the walk-behind hand-tractor-powered vegetable transplanter to ensure that the soil block seedlings are planted in an upright orientation. Methods: Various dimensionless terms were developed based on the dimensional analysis approach, and their effect on the planting of soil block seedlings in an upright orientation (planting efficiency) was studied. The optimum design dimensions of the hopper-type planting device were identified by the Taguchi method of optimization. Results: The ratio of the height of free fall to the sliding distance of the seedling on the surface of the hopper had the highest influence on planting efficiency. The planting efficiency was highest for plants with a height 15 ± 2 cm. The plant handling Froude number, in interaction with the design of the hopper-type planting device, also significantly affected the planting efficiency. Of the hopper design factors, the length of the slide of the seedlings on the surface of the hopper was most important, and induced sufficient velocity and rotation to cause the seedling to fall in an upright orientation. An evaluation of the performance of the planting device under actual field conditions revealed that the planting efficiency of the developed planting device was more than 97.5%. Conclusions: As the seedlings were fed to the metering device manually, an increase in planting rate increased missed plantings. The planting device can be adopted for any vegetable transplanter in which the seedlings are allowed to drop freely from the metering device.

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Section: Identification Of Optimum Levels Of Independent Parametersmentioning

confidence: 99%

Development of a Hopper-Type Planting Device for a Walk-Behind Hand-Tractor-Powered Vegetable Transplanter

Dihingia

Gautam

Sarma³

2016

Journal of Biosystems Engineering

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“…L 18 (2 1 ×3 7 ) orthogonal array [46] is used to implement the experiments according to Taguchi's parameter design principles (Figure 3). In Figure 3, columns 2-9 represent the eight mixture dosages (factors) and their levels.…”

Section: Signal To Noise Ratio Calculationsmentioning

confidence: 99%

Hybridizing a fuzzy multi-response Taguchi optimization algorithm with artificial neural networks to solve standard ready-mixed concrete optimization problems

Şimşek¹,

İç²,

Şimşek³

2016

IJCIS

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In this study, a fuzzy multi-response standard ready-mixed concrete (SRMC) optimization problem is addressed. This problem includes two conflicting quality optimization objectives. One of these objectives is to minimize the production cost. The other objective is to assign the optimal parameter set of SRMC's ingredient to each activity. To solve this problem, a hybrid fuzzy multi-response optimization and artificial neural network (ANN) algorithm is developed. The ANN algorithm is integrated into the multi-response SRMC optimization framework to predict and improve the quality of SRMC. The results show that fuzzy multi-response optimization model is more effective than crisp multi-response optimization model according to final production cost. However, the ANN model also gave more accurate results than the fuzzy model considering the regression analysis results.: Standard ready-mixed concrete; Multi-response optimization; Taguchi method; Fuzzy TOPSIS; Artificial neural networks.

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“…Where T is the nominal value of the productivity target specification; y is real field productivity; L is the loss associated to a particular difference between y and T; and k is the quality loss coefficient, whose value depends on the cost at any specified limits and on the width of such specification, e.g., T ± Δ, where Δ is the customer's tolerance to deviation of y from the target (Phadke, 1989). 2.00E+02 0.00E+00 1.16E+00 1.46E+01 0 0 0 y3 2.00E+02 0.00E+00 1.27E+00 1.76E+01 N-P-K: 15-15-15 = 2.50E+02 Paraquat = 3.75E+00 0 y4 3.00E+02 0.00E+00 1.16E+00 0.00E+00 0 Glyphosate = 1.17E+00 0 y5 4.67E+02 3.30E+02 5.80E-01 9.55E+00 0 Glyphosate = 1.20E+01 0 y6…”

Section: Tlf Economic Assessment Of the Cocoa Production Systemmentioning

confidence: 99%