machine speed
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2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Song Thanh Quynh Le ◽  
June Ho ◽  
Huong Mai Bui

Purpose This paper aims to develop a decision support system for predicting the knitting production’s efficiency based on the input parameters of an order. This tool supports the operations managers to make reliable decisions of estimated delivery time, which will result in reducing waste arising from late delivery, overtime and increased labor. Design/methodology/approach The decision tree method with a set of logical IF-THEN rules is used to determine the knitting production’s efficiency. Each path of the decision tree represents a rule of the following form: “IF <Condition> THEN <Efficiency label>.” Starting with identifying and categorizing input specifications, the model is then applied to the observed data to regenerate the results of efficiency into classification instances. Findings The production’s efficiency is the result of the interaction between input specifications such as yarn’s component, knitting fabric specifications and machine speed. The rule base is generated through a decision tree built to classify the efficiency into five levels, including very low, low, medium, high and very high. Based on this, production managers can determine the delivery time and schedule the manufacturing planning more accurately. In this research, the correct classification instances, which is simply a ratio of the correctly predicted observations to the total ones, reach 80.17%. Originality/Values This research proposes a new methodology for estimating the efficiency of weft knitting production based on a decision tree method with an application of real data. This model supports the decision-making process of the estimated delivery time.


2021 ◽  
pp. 213-224
Author(s):  
Zhilong Zhang ◽  
Jinlong Zheng ◽  
Aijun Geng ◽  
Ji Zhang ◽  
Abdalla N.O. Kheiry ◽  
...  

Applying different types of fertilizers to different depths of soil according to demand is advantageous in that it can optimize the distribution of nutrients in arable soil, adjust the nutrient supply of each growth stage of wheat, and increase grain yield. In the study, a layered fertilization opener that could realize the layered fertilization was developed. The interaction model between the opener, fertilizer and soil was established using EDEM simulation software. A response surface analysis was used to determine the optimal parameters of the opener. Specifically, the horizontal distance between the fertilizer drop openings was 140 mm, the machine speed was 1.05 m/s, and the angle of the opener was 37°. Furthermore, field experiments demonstrated that the average depth of upper layer was 8.39 cm, the average depth of middle layer was 16.465 cm, the average depth of lower layer was 24.025 cm, the average spacing of upper layer was 8.075 cm, and the average spacing of lower layer was 7.6 cm. The corresponding findings demonstrated that the layering effect of the opener met the requirements of the fertilization standard.


2021 ◽  
Author(s):  
Julian Kullick ◽  
Christoph Hackl

<div><div><div><div><p>A not yet available look-up table (LUT) based optimal feedforward torque control (OFTC) method for squirrel- cage induction machines (SCIMs) is presented. It is based on: (i) a generic transformer-like machine model in an arbitrarily rotating (d,q)-reference frame, considering nonlinear flux linkages and iron losses in the stator laminations; (ii) machine identification by evaluating steady-state measurements over a grid of (d,q) stator currents, producing frequency-dependent machine maps for e.g. flux linkages, torque, iron resistance and efficiency; and (iii) numerical optimization and extraction of OFTC look- up tables for optimal stator current references depending on reference torque and electrical frequency. In order to increase reproducibility, a feedback temperature controller is employed to keep the stator winding temperature constant. Moreover, throughout the identification, the electrical frequency is kept con- stant (per data set) by adapting the machine speed accordingly using a speed-controlled prime mover; this way the impact of iron losses becomes more balanced than for constant speed operation. The presented measurement results confirm that compared to constant flux operation or scalar V/Hz control, efficiency can be increased particularly in part-load operation by up to 7 %.</p></div></div></div></div>


2021 ◽  
Vol 2 (2) ◽  
pp. 413-424
Author(s):  
Adewale SEDARA ◽  
Emmanuel ODEDİRAN

The research was carried out to optimize parameters for evaluating an improved motorize maize sheller. Statistical analysis was performed using response surface methodology (RSM) with 3 by 3 factorial experiment with 3 replicates. The three parameters are speed (850 rpm, 950 rpm and 1100 rpm), moisture content (12, 15, and 17%) and feed rate (120 kg h-1, 130 kg h-1 and 140 kg h-1) used to illustrate the ability of the machine to shell maize (throughput capacity, shelling rate and machine efficiency). Results obtained showed that for optimum throughput capacity of 630.97 kg h-1; shelling rate 485.34 kg h-1 and machine efficiency 93.86% of the machine; is maximum for 129.6 kg h-1 feed rate and moisture content 16.49% and machine speed of 1026.9 rpm. The machine can be used on commercial farms with these operational results.


2021 ◽  
Vol 2 (2) ◽  
pp. 413-424
Author(s):  
Adewale SEDARA ◽  
Emmanuel ODEDİRAN

The research was carried out to optimize parameters for evaluating an improved motorize maize sheller. Statistical analysis was performed using response surface methodology (RSM) with 3 by 3 factorial experiment with 3 replicates. The three parameters are speed (850 rpm, 950 rpm and 1100 rpm), moisture content (12, 15, and 17%) and feed rate (120 kg h-1, 130 kg h-1 and 140 kg h-1) used to illustrate the ability of the machine to shell maize (throughput capacity, shelling rate and machine efficiency). Results obtained showed that for optimum throughput capacity of 630.97 kg h-1; shelling rate 485.34 kg h-1 and machine efficiency 93.86% of the machine; is maximum for 129.6 kg h-1 feed rate and moisture content 16.49% and machine speed of 1026.9 rpm. The machine can be used on commercial farms with these operational results.


Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6790
Author(s):  
Daniel Wachowiak

Properties of state observers depend on proper gains selection. Each method of state estimation may require the implementation of specific techniques of finding those gains. The aim of this study is to propose a universal method of automatic gains selection and perform its verification on an induction machine speed observer. The method utilizes a genetic algorithm with fitness function which is directly based on the impulse response of the observer. System identification using least-squares estimation is implemented to determine the dynamic properties of the observer based on the estimation error signal. The influence of sampling time as well as signal length on the system identification has been studied. The results of gains selection using the proposed method have been compared with results obtained using the approach based on the placement of the poles of linearized estimation error equations. The introduced method delivers results comparable with analytical methods and does not require prior preparation specific to the implemented speed observer, such as linearization.


2021 ◽  
Author(s):  
Abdi Dera

<p>An embedded system is a microcontroller or microprocessor-based system which is designed to perform a specific task by collecting, processing and communicating information. While focusing on specific task, it is also desired to make such system for better and efficient result. In due course, one of the challenges is contextualizing the collected information to predict the output and making smart decision to produce the output. The learning system that can contextualize the surrounding environment should have a capability of automatic mechanism of inferring information like humans do. This calls for neural networks that provide an embedded intelligence for smart systems to make decisions at machine speed. The main challenge to develop such system is the constraints in memory size, computational power and other characteristics of embedded system that can significantly restrict developers from implementing learning algorithms to solve the problem. This paper resents lightweight neural networks so as to show a method for implementing context-aware embedded system in environment where there is resource limitation. A testbed is setup for collecting the data, training and evaluation. The algorithms are simulated using C on Arduino. A good result was obtained after deploying the algorithm and knowledgebase on Arduino board for sensor reading.</p>


2021 ◽  
Author(s):  
Abdi Dera

<p>An embedded system is a microcontroller or microprocessor-based system which is designed to perform a specific task by collecting, processing and communicating information. While focusing on specific task, it is also desired to make such system for better and efficient result. In due course, one of the challenges is contextualizing the collected information to predict the output and making smart decision to produce the output. The learning system that can contextualize the surrounding environment should have a capability of automatic mechanism of inferring information like humans do. This calls for neural networks that provide an embedded intelligence for smart systems to make decisions at machine speed. The main challenge to develop such system is the constraints in memory size, computational power and other characteristics of embedded system that can significantly restrict developers from implementing learning algorithms to solve the problem. This paper resents lightweight neural networks so as to show a method for implementing context-aware embedded system in environment where there is resource limitation. A testbed is setup for collecting the data, training and evaluation. The algorithms are simulated using C on Arduino. A good result was obtained after deploying the algorithm and knowledgebase on Arduino board for sensor reading.</p>


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