scholarly journals Minimization of Casting Defects using SQC Tool

2021 ◽  
Vol 9 (8) ◽  
pp. 1627-1636
Author(s):  
Abhijit M. Mane
Keyword(s):  
Quality Control ◽  
Casting Process ◽  
Statistical Quality Control ◽  
Casting Defects ◽  
Casting Defect ◽  
Statistical Quality ◽  
Analysis Technique ◽  
Quality Control Tool ◽  
Control Tool

Abstract: Casting is most widely used manufacturing technique. During casting process, number of defects in the casting takes place. In this research, Statistical Quality Control tool is used to minimize the defects. Paretro analysis technique is used to find out the defects in the castings. Recommendations are implemented in the casting line. Improved quality of casting and reduction of defects are found after the implementation of SQC tool. Keywords: Casting, Defect, Why-Why analysis, Shift, Manifold

scholarly journals CONTROLE ESTATÍSTICO APLICADO A UNIFORMIDADE DE DISTRIBUIÇÃO EM UNIDADES GOTEJADORAS OPERANDO COM ÁGUA RESIDUÁRIA

Irriga ◽  
2020 ◽  
Vol 25 (4) ◽  
pp. 719-727
Author(s):  
ABEL HENRIQUE SANTOS GOMES ◽  
Mayra Gislayne Melo de Lima ◽  
DENISE DE JESUS LEMOS FERREIRA ◽  
GLEYKA NÓBREGA VASCONCELOS ◽  
JUAREZ PAZ PEDROZA ◽  
...  
Keyword(s):  
Quality Control ◽  
Water Reuse ◽  
Industrial Sector ◽  
Statistical Quality Control ◽  
Statistical Control ◽  
Statistical Quality ◽  
Quality Control Tool ◽  
E Mail ◽  
Control Limits ◽  
Control Tool

CONTROLE ESTATÍSTICO APLICADO A UNIFORMIDADE DE DISTRIBUIÇÃO EM UNIDADES GOTEJADORAS OPERANDO COM ÁGUA RESIDUÁRIA     ABEL HENRIQUE SANTOS GOMES1; MAYRA GISLAYNE MELO DE LIMA2; DENISE DE JESUS LEMOS FERREIRA3; GLEYKA NÓBREGA VASCONCELOS4; JUAREZ PAZ PEDROZA5 E VERA LÚCIA ANTUNES DE LIMA6   1 Doutor em Engenharia Agrícola: Departamento de Engenharia Agrícola, Programa de Pós-Graduação em Engenharia Agrícola, Universidade Federal de Campina Grande, Rua Aprígio Veloso, 882, Bairro Universitário, 58428-830, Campina Grande, Paraíba, Brasil, e-mail: [email protected]. 2 Doutoranda em Engenharia Agrícola: Departamento de Engenharia Agrícola, Programa de Pós-Graduação em Engenharia Agrícola, Universidade Federal de Campina Grande, Rua Aprígio Veloso, 882, Bairro Universitário, 58428-830, Campina Grande, Paraíba, Brasil, e-mail: [email protected]. 3 Professora doutora EBTT na área de Engenharia Agrícola: Instituto Federal de Educação, Ciência e Tecnologia Baiano – Campus Xique-Xique, Rodovia BA 052, Km 468, s/n – Zona Rural, CEP: 47.400-000, Xique-Xique, Bahia, Brasil, e-mail: [email protected]. 4 Mestranda em Engenharia Agrícola: Departamento de Engenharia Agrícola, Programa de Pós-Graduação em Engenharia Agrícola, Universidade Federal de Campina Grande, Rua Aprígio Veloso, 882, Bairro Universitário, 58428-830, Campina Grande, Paraíba, Brasil, e-mail: [email protected]. 5 Professor doutor: Departamento de Engenharia Agrícola, Programa de Pós-Graduação em Engenharia Agrícola, Universidade Federal de Campina Grande, Rua Aprígio Veloso, 882, Bairro Universitário, 58428-830, Campina Grande, Paraíba, Brasil, e-mail: [email protected]. 6 Professora doutora: Departamento de Engenharia Agrícola, Programa de Pós-Graduação em Engenharia Agrícola, Universidade Federal de Campina Grande, Rua Aprígio Veloso, 882, Bairro Universitário, 58428-830, Campina Grande, Paraíba, Brasil, e-mail: [email protected].     1 RESUMO   A ferramenta de controle estatístico de qualidade, desenvolvida para o setor industrial, ganhou espaço na agricultura como auxilio ao manejo adequado da irrigação. Objetivou-se com este projeto, através do auxílio do controle estatístico de qualidade, avaliar a uniformidade de distribuição de água em unidades de irrigação por gotejamento abastecidas por águas residuárias. O experimento foi conduzido em ambiente controlado pertencente ao Laboratório de Irrigação e Drenagem, da Unidade Acadêmica de Engenharia Agrícola, Universidade Federal de Campina Grande. Foram avaliados dois tipos de linhas laterais constituídas de gotejadores específicos, submetidos a fontes de água distintas, totalizando 31 ensaios. A maioria dos valores por ensaio situou-se dentro dos limites de controle, entretanto os valores de vazão não atingiram o valor de catálogo do fabricante para ambos modelos de fitas gotejadoras. Os dois modelos distintos de emissores funcionaram durante grande parte do período de estudo dentro dos limites de controle. A ferramenta de controle estatístico de qualidade se mostrou viável e imprescindível, possibilitando o uso eficiente das unidades de irrigação.   Palavras-chave: Irrigação, controle de qualidade, reúso de água.     GOMES, A. H. S.; LIMA, M. G. M. DE; FERREIRA, D. DE J. L.; VASCONCELOS, G. N.; PEDROZA, J. P.; LIMA, V. L. A. DE STATISTICAL CONTROL APPLIED TO DISTRIBUTION UNIFORMITY IN DRIPPING UNITS OPERATING WITH WASTE WATER 2 ABSTRACT   The statistical quality control tool, developed for the industrial sector, gained space in agriculture as an aid in the proper management of irrigation. The aim of the project was to assess, through the aid of statistical quality control, the uniformity of water distribution in drip irrigation units supplied by wastewater. The experiment was conducted in a controlled environment belonging to the Irrigation and Drainage Laboratory, of the Agricultural Engineering Academic Unit, Federal University of Campina Grande. Two types of lateral lines constituted of specific grippers were evaluated, submitted to different water sources, totaling 31 tests. Most of the values per test were within the control limits, however the flow values did not reach the manufacturer's catalog value for both drip strip models. The two models of emitters functioned during much of the study period within the control limits, the statistical quality control tool proved to be viable and essential, enabling the efficient use of the irrigation units.   Keywords: Irrigation, quality control, water reuse.

scholarly journals PENERAPAN STATISTICAL QUALITY CONTROL (SQC) PADA PENGENDALIAN MUTU MINYAK TELON (STUDI KASUS DI PT.X)

AGROINTEK ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 72
Author(s):  
Andan Linggar Rucitra ◽  
S Fadiah
Keyword(s):  
Quality Control ◽  
Raw Materials ◽  
Control Process ◽  
Statistical Quality Control ◽  
Statistical Quality ◽  
Quality Control Process ◽  
Fishbone Diagram ◽  
Potential Factor ◽  
Measurement Of Success

<p><em>Telon oil is</em><em> one of </em><em> </em><em>the </em><em>traditional medicine in the form of </em><em> </em><em>liquid preparations that serves to provide a sense of warmth to the wearer. PT</em><em>.X</em><em> is one of the companies that produce</em><em> </em><em>telon</em><em> oil</em><em>.</em><em> To maintain</em><em> the quality of telon oil from PT.X</em><em> product</em><em>, required overall quality control that is starting from the quality control of raw materials, quality control process to the quality control of the final product. The purpose of this research is to know the application of Statistical Quality Control (SQC) in controlling the quality of telon oil in PT X. </em><em>F</em><em>inal product</em><em> quality</em><em> become one of the measurement of success of a process, so it needs a good quality control. SQC method used in this research is Pareto Diagram and Cause and Effect Diagram. Pareto diagram is a bar graph </em><em>that </em><em>show the problem based on the order of the number of occurrences of the most number of problems until the least happened. A causal diagram is often called a fishbone diagram, a tool for identifying potential causes of an effect or problem. The result of applying the method indicates that 80% defect is caused by unsuitable volume and on the incompatibility of Expired Date (ED) code. The damage is caused by several factors namely the method, labor, and machine while the most potential factor is the volume conformity to reduce the number of defect products.</em></p>

scholarly journals Statistical Quality Control for Human Computation and Crowdsourcing

2018 ◽  
Author(s):  
Yukino Baba
Keyword(s):  
Quality Control ◽  
Critical Issue ◽  
Human Computation ◽  
Statistical Quality Control ◽  
Wisdom Of Crowds ◽  
Statistical Quality ◽  
Statistical Approaches ◽  
The Wisdom Of Crowds

Human computation is a method for solving difficult problems by combining humans and computers. Quality control is a critical issue in human computation because it relies on a large number of participants (i.e., crowds) and there is an uncertainty about their reliability. A solution for this issue is to leverage the power of the "wisdom of crowds"; for example, we can aggregate the outputs of multiple participants or ask a participant to check the output of another participant to improve its quality. In this paper, we review several statistical approaches for controlling the quality of outputs from crowds.

scholarly journals SequelTools: a suite of tools for working with PacBio Sequel raw sequence data

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
David E. Hufnagel ◽  
Matthew B. Hufford ◽  
Arun S. Seetharam
Keyword(s):  
Quality Control ◽  
Sequence Data ◽  
Read Length ◽  
Pacbio Sequencing ◽  
Sequencing Method ◽  
Quality Control Tool ◽  
Long Read ◽  
Control Tool ◽  
Publication Quality

Abstract Background PacBio sequencing is an incredibly valuable third-generation DNA sequencing method due to very long read lengths, ability to detect methylated bases, and its real-time sequencing methodology. Yet, hitherto no tool was available for analyzing the quality of, subsampling, and filtering PacBio data. Results Here we present SequelTools, a command-line program containing three tools: Quality Control, Read Subsampling, and Read Filtering. The Quality Control tool quickly processes PacBio Sequel raw sequence data from multiple SMRTcells producing multiple statistics and publication-quality plots describing the quality of the data including N50, read length and count statistics, PSR, and ZOR. The Read Subsampling tool allows the user to subsample reads by one or more of the following criteria: longest subreads per CLR or random CLR selection. The Read Filtering tool provides options for normalizing data by filtering out certain low-quality scraps reads and/or by minimum CLR length. SequelTools is implemented in bash, R, and Python using only standard libraries and packages and is platform independent. Conclusions SequelTools is a program that provides the only free, fast, and easy-to-use quality control tool, and the only program providing this kind of read subsampling and read filtering for PacBio Sequel raw sequence data, and is available at https://github.com/ISUgenomics/SequelTools.

scholarly journals SequelTools: A Suite of Tools for Working with PacBio Sequel Raw Sequence Data

2019 ◽  
Author(s):  
David E. Hufnagel ◽  
Matthew B. Hufford ◽  
Arun S. Seetharam
Keyword(s):  
Quality Control ◽  
Sequence Data ◽  
Read Length ◽  
Pacbio Sequencing ◽  
Sequencing Method ◽  
Quality Control Tool ◽  
Long Read ◽  
Control Tool ◽  
Publication Quality

AbstractBackgroundPacBio sequencing is an incredibly valuable third-generation DNA sequencing method due to very long read lengths, ability to detect methylated bases, and its real-time sequencing methodology. Yet, hitherto no tool was available for analyzing the quality of, subsampling, and filtering PacBio data.ResultsHere we present SequelTools, a command-line program containing three tools: Quality Control, Read Subsampling, and Read Filtering. The Quality Control tool quickly processes PacBio Sequel raw sequence data from multiple SMRTcells producing multiple statistics and publication-quality plots describing the quality of the data including N50, read length and count statistics, PSR, and ZOR. The Read Subsampling tool allows the user to subsample reads by one or more of the following criteria: longest subreads per CLR or random CLR selection. The Read Filtering tool provides options for normalizing data by filtering out certain low-quality scraps reads and/or by minimum CLR length. SequelTools is implemented in bash, R, and Python using only standard libraries and packages and is platform independent.ConclusionsSequelTools is a program that provides the only free, fast, and easy-to-use quality control tool, and the only program providing this kind of read sumbsampling and read filtering for PacBio Sequel raw sequence data, and is available at https://github.com/ISUgenomics/SequelTools

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