QUALIDADE NA COLHEITA MECANIZADA DE MILHO SEMEADO EM DIFERENTES VELOCIDADES

2016 ◽  
Vol 15 (3) ◽  
pp. 582 ◽  
Author(s):  
ANTONIO TASSIO SANTANA ORMOND ◽  
MURILO APARECIDO VOLTARELLI ◽  
CARLA SEGATTO STRINI PAIXÃO ◽  
ALINE SPAGGIARI ALCÂNTARA1 ◽  
ELIZABETH HARUNA KAZAMA ◽  
...  
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Plant Density ◽  
Plant Population ◽  
Longitudinal Distribution ◽  
Statistical Control ◽  
Individual Values ◽  
Sowing Time ◽  
Statistical Process

RESUMO - As perdas na colheita podem estar relacionadas tanto a colhedora, como também a fatores ligados a cultura como: mau preparo do solo, densidade de plantas, inadequação da época de semeadura são alguns deles. O presente estudo objetivou determinar a influência da velocidade de semeadura no processo de colheita mecanizada de milho, por meio do controle de qualidade do processo. O experimento foi conduzido em Latossolo Vermelho, textura argilosa e relevo suave ondulado. O delineamento foi baseado na óptica do Controle Estatístico de ProcessoCEP, onde os dados foram coletados em pontos aleatórios em função do tempo. Os indicadores de qualidade avaliados foram divididos em parâmetros de semeadura (população de plantas e distribuição longitudinal de plântulas); e de colheita (Perdas de grãos e distribuição de palha) em função de seis velocidades de deslocamento (aproximadamente 2,0; 4,0; 6,0; 9,0; 10,0 e 12,0 Km.h-1). Os dados foram submetidos a análise descritiva para análise do comportamento. Como ferramentas do controle estatístico de processo utilizou-se, run charts ou gráfico sequencial e carta de controle de valores individuais para análise da qualidade do processo. A maior velocidade (V6) apresentou a maior variabilidade dos dados para todas as variáveis. A operação da colheita mecanizada de milho foi influenciada por fatores extrínsecos e intrínsecos a ela.Palavras-chave: Controle estatístico de processo, espaçamentos normais, perdas, população de plantas.QUALITY IN MECHANIZED HARVEST OF CORN SOWN IN DIFFERENT SPEEDSABSTRACT - The harvest losses may be associated to harvester as well as factors related to cultivation such as poor soil preparation, plant density, unsuitable sowing time. This study aimed to determine the effect of speed sowing in the mechanized harvest of corn, through the control of the quality of the process. The experiment was conducted in a clayey Oxisol and undulate relief. The design was based on the optics of the Statistical Process Control SPC, and the data were collected at random points in function of time. The quality indicators evaluated were divided into sowing parameters (plant population and longitudinal distribution of seedlings) and harvesting (loss of grain and straw distribution) in function of six displacement speeds (approximately 2.0, 4.0, 6.0, 9.0, 10.0 and 12.0 Km.h-1). The data were submitted to descriptive analysis for behavior analysis. As tools for the statistical control of the process, run charts or sequential graph were used, and control chart of individual values for analysis of the quality of the process. The highest speed (V6) showed the highest variability of the data for all variables. The operation of mechanized harvest of corn was influenced by extrinsic and intrinsic factors.Keywords: statistical process control, normal spacings, losses, plant population.

QUALIDADE NA COLHEITA MECANIZADA DE MILHO SEMEADO EM DIFERENTES VELOCIDADES

2017 ◽  
Vol 15 (3) ◽  
pp. 582
Author(s):  
ANTONIO TASSIO SANTANA ORMOND ◽  
MURILO APARECIDO VOLTARELLI ◽  
CARLA SEGATTO STRINI PAIXÃO ◽  
ALINE SPAGGIARI ALCÂNTARA1 ◽  
ELIZABETH HARUNA KAZAMA ◽  
...  
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Plant Density ◽  
Plant Population ◽  
Longitudinal Distribution ◽  
Statistical Control ◽  
Individual Values ◽  
Sowing Time ◽  
Statistical Process

RESUMO - As perdas na colheita podem estar relacionadas tanto a colhedora, como também a fatores ligados a culturacomo: mau preparo do solo, densidade de plantas, inadequação da época de semeadura são alguns deles. O presenteestudo objetivou determinar a influência da velocidade de semeadura no processo de colheita mecanizada de milho,por meio do controle de qualidade do processo. O experimento foi conduzido em Latossolo Vermelho, textura argilosae relevo suave ondulado. O delineamento foi baseado na óptica do Controle Estatístico de ProcessoCEP, onde os dadosforam coletados em pontos aleatórios em função do tempo. Os indicadores de qualidade avaliados foram divididos emparâmetros de semeadura (população de plantas e distribuição longitudinal de plântulas); e de colheita (Perdas de grãose distribuição de palha) em função de seis velocidades de deslocamento (aproximadamente 2,0; 4,0; 6,0; 9,0; 10,0 e12,0 Km.h-1). Os dados foram submetidos a análise descritiva para análise do comportamento. Como ferramentas docontrole estatístico de processo utilizou-se, run charts ou gráfico sequencial e carta de controle de valores individuaispara análise da qualidade do processo. A maior velocidade (V6) apresentou a maior variabilidade dos dados para todasas variáveis. A operação da colheita mecanizada de milho foi influenciada por fatores extrínsecos e intrínsecos a ela.Palavras-chave: Controle estatístico de processo, espaçamentos normais, perdas, população de plantas.QUALITY IN MECHANIZED HARVEST OF CORN SOWN IN DIFFERENT SPEEDSABSTRACT - The harvest losses may be associated to harvester as well as factors related to cultivation such as poorsoil preparation, plant density, unsuitable sowing time. This study aimed to determine the effect of speed sowing inthe mechanized harvest of corn, through the control of the quality of the process. The experiment was conductedin a clayey Oxisol and undulate relief. The design was based on the optics of the Statistical Process Control SPC,and the data were collected at random points in function of time. The quality indicators evaluated were divided intosowing parameters (plant population and longitudinal distribution of seedlings) and harvesting (loss of grain and strawdistribution) in function of six displacement speeds (approximately 2.0, 4.0, 6.0, 9.0, 10.0 and 12.0 Km.h-1). The datawere submitted to descriptive analysis for behavior analysis. As tools for the statistical control of the process, run chartsor sequential graph were used, and control chart of individual values for analysis of the quality of the process. Thehighest speed (V6) showed the highest variability of the data for all variables. The operation of mechanized harvest ofcorn was influenced by extrinsic and intrinsic factors.Keywords: statistical process control, normal spacings, losses, plant population.

scholarly journals Monitoring of mechanical sugarcane harvesting through control charts

2015 ◽  
Vol 35 (6) ◽  
pp. 1079-1092 ◽  
Author(s):  
Murilo A. Voltarelli ◽  
Rouverson P. da Silva ◽  
Cristiano Zerbato ◽  
Carla S. S. Paixão ◽  
Tiago de O. Tavares
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Grinding Wheel ◽  
Individual Values ◽  
Statistical Process ◽  
Minas Gerais State ◽  
Weighted Moving Average

ABSTRACT Statistical process control in mechanized farming is a new way to assess operation quality. In this sense, we aimed to compare three statistical process control tools applied to losses in sugarcane mechanical harvesting to determine the best control chart template for this quality indicator. Losses were daily monitored in farms located within Triângulo Mineiro region, in Minas Gerais state, Brazil. They were carried over a period of 70 days in the 2014 harvest. At the end of the evaluation period, 194 samples were collected in total for each type of loss. The control charts used were individual values chart, moving average and exponentially weighted moving average. The quality indicators assessed during sugarcane harvest were the following loss types: full grinding wheel, stumps, fixed piece, whole cane, chips, loose piece and total losses. The control chart of individual values is the best option for monitoring losses in sugarcane mechanical harvesting, as it is of easier result interpretation, in comparison to the others.

scholarly journals Optimum Manpower Allocation in Health Care Using Statistical Process Control

2019 ◽  
pp. 1-9
Author(s):  
William E. Odinikuku ◽  
Jephtah A. Ikimi ◽  
Ikechukwu P. Onwuamaeze
Keyword(s):  
Health Care ◽  
Process Control ◽  
Statistical Process Control ◽  
Optimal Allocation ◽  
Manpower Planning ◽  
Production Costs ◽  
Statistical Control ◽  
Statistical Process ◽  
Planning Models ◽  
Allocation Process

In many countries manpower problems in the field of health care are regular items on the agenda of policy makers. To avoid mismatches between demand of care and supply of care on national and regional levels, manpower planning models and methods are used to determine adequate numbers of medical specialists to fulfill the future demand of care. Inadequate or inefficient allocation of manpower to various departments in an organization or workplace can lead to undesired outcomes which may include: down time, reduced productivity, workers fatigue, increased production costs, etc. As a result of the above stated problem, there is need to devise a statistical model that will ensure optimal allocation of manpower. In this study, the optimum allocation of two hundred and fifty two general nurses to fifteen wards at a hospital code named WCH located in South-South geopolitical zone, Nigeria was achieved using statistical process control. The study involved the analysis of data obtained from our hub of study for a period of two months. The C-chart was used to check if the process of allocation was in control or not. The result obtained from the study showed that the manpower allocation process was out of statistical control as the allocation of the children emergency ward was outside the upper control limit of the c-chart plot.

scholarly journals Fertilizer deposition as a function of angular velocity and inclination of the helical dosing mechanism

2022 ◽  
Vol 26 (3) ◽  
pp. 226-235
Author(s):  
Gabriel G. Zimmermann ◽  
Samir P. Jasper ◽  
Daniel Savi ◽  
Leonardo L. Kmiecik ◽  
Lauro Strapasson Neto ◽  
...  
Keyword(s):  
Angular Velocity ◽  
Process Control ◽  
Statistical Process Control ◽  
Industrial Process ◽  
Descriptive Statistics ◽  
Flow Data ◽  
Statistical Process ◽  
Flow Rates ◽  
Angular Velocities

ABSTRACT The establishment of grain crops in Brazil is an important industrial process in the agricultural chain, requiring the correct deposition of granular fertilizer over the sowing furrow and more efficient, precise, and sustainable assessments in the operation, which can be achieved with the statistical process control. This study aimed to assess the effect of the angular velocity on different inclinations of the helical metering mechanism on the granular fertilizer deposition. An automated electronic bench was used to assess the deposition quality of granular fertilizers considering different angular velocities (1.11, 1.94, and 2.77 m s-1) and longitudinal and transverse inclinations (+15, +7.5, 0, −7.5, and −15°), with the helical doser by overflow. Flow data were collected and submitted to descriptive statistics and statistical process control. The metering mechanism showed expected variations, with acceptable performance under process control. The values of the flow rates of the granular fertilizer increased as velocity increased, standing out longitudinal inclinations of +7.5 and +15°, providing higher fertilizer depositions.

scholarly journals KONSEP DAN TEKNIK MANAIEMEN MUTU TERPADU

2017 ◽  
pp. 94-101
Author(s):  
Saut Pruba
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Continuous Improvement ◽  
Six Sigma ◽  
Employee Empowerment ◽  
Just In Time ◽  
Statistical Process

Quality is a term that has diflferent meanings to different people. Quality set out in this paper are the features and characteristics of the total of a product or service associated with its ability to satisfij the needs of the visible or disguised. The quality of the environment requires an establishment of TQM because quality can not be examined only in a product. In this paper also discussed the concept of the seven TQM: continuous improvement, Six Sigma, employee empowerment, benchmarking, just in time, the concept of Taguchi TQM techniques and knowledge. TQM is the seventh technique check sheet, scatter diagrams, cause-effect diagrams, Pareto charts, flow charts, histograms, and statistical process control.

Standardization of CMM Fitting Algorithms and Development of Inspection Maps for Use in Statistical Process Control

2011 ◽  
Author(s):  
Neelakantan Mani ◽  
Jami J. Shah ◽  
Joseph K. Davidson
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Principal Component ◽  
Statistical Control ◽  
Statistical Process ◽  
Fitting Algorithm ◽  
Inspection Process ◽  
Feature Fitting ◽  
The Individual ◽  
Intelligent Methods

The choice of fitting algorithm in CMM metrology has often been based on mathematical convenience rather than the fundamental GD&T principles dictated by the ASME Y14.5 standard. Algorithms based on the least squares technique are mostly used for GD&T inspection and this wrong choice of fitting algorithm results in errors that are often overlooked and leads to deficiency in the inspection process. The efforts by organizations such as NIST and NPL and many other researchers to evaluate commercial CMM software were concerned with the mathematical correctness of the algorithms and developing efficient and intelligent methods to overcome the inherent difficulties associated with the mathematics of these algorithms. None of these works evaluate the ramifications of the choice of a particular fitting algorithm for a particular tolerance type. To illustrate the errors that can arise out of a wrong choice of fitting algorithm, a case study was done on a simple prismatic part with intentional variations and the algorithms that were employed in the software were reverse engineered. Based on the results of the experiments, a standardization of fitting algorithms is proposed in light of the definition provided in the standard and an interpretation of manual inspection methods. The standardized fitting algorithms developed for substitute feature fitting are then used to develop Inspection maps (i-Maps) for size, orientation and form tolerances that apply to planar feature types. A methodology for Statistical Process Control (SPC) using these i-Maps is developed by fitting the i-Maps for a batch of parts into the parent Tolerance Maps (T-Maps). Different methods of computing the i-Maps for a batch are explored such as the mean, standard deviations, computing the convex hull and doing a principal component analysis of the distribution of the individual parts. The control limits for the process and the SPC and process capability metrics are computed from inspection samples and the resulting i-Maps. Thus, a framework for statistical control of the manufacturing process is developed.

Variability Analysis by Statistical Control Process and Functional Data Analysis — Case of Study Applied to Power System Harmonics Assessment

2014 ◽  
Vol 615 ◽  
pp. 118-123 ◽  
Author(s):  
Joaquín Sancho ◽  
Jorge Pastor ◽  
Javier Martínez ◽  
Miguel Angel García
Keyword(s):  
Data Analysis ◽  
Process Control ◽  
Power Systems ◽  
Statistical Process Control ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Control Process ◽  
Industrial Applications ◽  
Statistical Control ◽  
Statistical Process

Functional data appear in a multitude of industrial applications and processes. However, in many cases at present, such data continue to be studied from the conventional standpoint based on Statistical Process Control (SPC), losing the capacity of analyzing different aspects over the time. In this study is presented a Statistical Control Process based on functional data analysis to identify outliers or special causes of variability of harmonics appearing in power systems which can negatively impact on quality of electricity supply. The results obtained from the functional approach are compared with those obtained with conventional Statistical Process Control that has been done firstly.

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