scholarly journals Analisis Masalah Kualitas pada M/C Crank Shaft M2 dengan Menggunakan Tool Capability Process di PT XYZ, Pegangsaan Dua, Jakarta

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Gidion Karo Karo ◽  
Jessie Deborah R. Makapedua
Keyword(s):  
Process Improvement ◽  
Control Process ◽  
Process Capability ◽  
Repair Process ◽  
Process Capability Analysis ◽  
Normality Test ◽  
Quality Control Process ◽  
Capability Indices ◽  
Capability Analysis

<p>Process Capability is a tool that is often used in the process of quality improvement, especially for process improvement. This study uses a process capability analysis on crank shaft production line 2 for motorcycles. By using normality test data and process capability indices for calculation of Cp/Cpk, shows that most of the data obtained are not normally distributed, so need to transform the data into normal, which can then be followed by the calculation of process capability. For the calculation of Cp/Cpk, it was found that there were some machines that still need to get tight control to meet the specification. It shows that mass production is still less stable. In order to meet the specifications, it is necessary to improve the quality of the repair process to reduce the variation in the process.</p><p>Keywords: Process Capability, Quality Control, Process Improvement</p>

scholarly journals CAPABILITY INDICES FOR CONTROL CHARTS BASED ON REGRESSION MODELS

2015 ◽  
Vol 12 (2) ◽  
pp. 234
Author(s):  
Fernanda Siqueira Souza ◽  
Danilo Cuzzuol Pedrini ◽  
Carla Schwengber Ten Caten
Keyword(s):  
Quality Improvement ◽  
Control Charts ◽  
Regression Models ◽  
Process Capability ◽  
Process Capability Analysis ◽  
Capability Indices ◽  
Capability Analysis ◽  
Simulated Process

Process capability analysis is extremely important for optimization and quality improvement. It verifies whether the process under analysis is capable of producing items within engineering and customers’ specifications. The use of capability indices when assumptions are not satisfied leads to erroneous conclusions, compromising the study and analysis of the process, jeopardizing the fulfillment of requirements from management or external customers. Aiming at filling a gap identified in the literature, the main contributions of this work are: (i) proposition of capability indices for processes monitored through control charts based on regression models, for symmetric and asymmetric specifications; and (ii) comparison of the proposed indices with traditional capability indices through a simulated process.

scholarly journals Analyzing of Process Capability Indices based on Neutrosophic Sets

2021 ◽  
Author(s):  
Selin Yalçın ◽  
Ihsan Kaya
Keyword(s):  
Automotive Industry ◽  
Process Capability ◽  
Process Capability Indices ◽  
Neutrosophic Sets ◽  
Modeling Uncertainty ◽  
Inaccurate Information ◽  
Process Capability Analysis ◽  
Capability Indices ◽  
Capability Analysis ◽  
Human Thinking

Abstract Process capability analysis (PCA) is an important statistical analysis approach for measuring and analyzing the ability of the process to meet specifications. This analysis has been applied by producing process capability indices (PCIs). \({C}_{p}\) and \({C}_{pk}\) are the most commonly used PCIs for this aim. Although they are completely effective statistics to analyze process’ capability, the complexity of the production processes based on uncertainty arising from human thinking, incomplete or vague information makes it difficult to analyze the process capability with precise values. When there is uncertain, complex, incomplete and inaccurate information, the capability of the process is successfully analyzed by using the fuzzy sets. Neutrosophic sets (NSs), one of the new fuzzy set extensions, have a significant role in modeling uncertainty, since they contain the membership functions of truth, indeterminacy, and falsity definitions rather than an only membership function. This feature provides a strong advantage for modeling uncertainty. In this paper, PCA has been performed based on NSs to overcome uncertainties of the process. For this purpose, specification limits (SLs) have been reconsidered by using NSs and two of the well-known process capability indices (PCIs) named \({C}_{p}\) and \({C}_{pk}\) have been reformulated. Finally, the neutrosophic process capability indices (NPCIs) named \({C}_{p}\) \(\left({\tilde{\stackrel{⃛}{C}}}_{p}\right)\) and \({C}_{pk}\) \(\left({\tilde{\stackrel{⃛}{C}}}_{pk}\right)\) have been derived for three cases that are created by defining SLs. Additionally, the obtained NPCIs have also been applied and confirmed on real case problems from automotive industry. The obtained results show that the NPCIs support the quality engineers to easily define SLs and obtain more flexible and realistic evaluations for PCA.

scholarly journals PENERAPAN IMPROVED GENERALIZED VARIANCE PADA PENGENDALIAN KUALITAS PAVING BLOCK SEGIENAM

2020 ◽  
Vol 9 (1) ◽  
pp. 87-97
Author(s):  
Nathasa Erdya Kristy ◽  
Mustafid Mustafid ◽  
Sudarno Sudarno
Keyword(s):  
Quality Control ◽  
Production Process ◽  
Control Chart ◽  
Process Capability ◽  
Quality Characteristics ◽  
Quality Characteristic ◽  
Generalized Variance ◽  
Process Capability Analysis ◽  
Capability Analysis

In quality assurance of hexagonal paving block products, quality control is needed so the products that produced are in accordance with the specified standards. Quality control carried out involves two interconnected quality characteristics, that is thickness and weight of hexagonal paving blocks, so multivariate control chart is used. Improved Generalized Variance control chart is a tool used to control process variability in multivariate manner. Variability needs to be controlled because of in a production process, sometimes there are variabilities that caused by engine problems, operator errors, and deffect in raw materials that affect the process. The purpose of this study is to apply Improved Generalized Variance control chart in controlling the quality of hexagonal paving block products and calculating the capability of production process to meet the standards. Based on the assumption of multivariate normal distribution test, it can be seen that the data of quality characteristics of hexagonal paving blocks have multivariate distribution. While based on the correlation test between variables it can be concluded that the characteristics of the quality of thickness and weight correlate with each other. The result of the control using these control chart shows that the process is statistically in control. The results of process capability analysis show that the production process has been running according to the standard because the process capability index value is generated using a weighting of 0.5 for each quality characteristic that is 1.01517. Keywords: Paving Block, Quality Control, Variability, Improved Generalized Variance, Process Capability Analysis

Kernel Density Estimation and Metropolis-Hastings Sampling in Process Capability Analysis of Unknown Distributions

2012 ◽  
Author(s):  
Wenzhen Huang ◽  
Ankit Pahwa ◽  
Zhenyu Kong
Keyword(s):  
Density Estimation ◽  
Kernel Density ◽  
Process Capability ◽  
Kernel Functions ◽  
Process Capability Analysis ◽  
Normality Assumption ◽  
Capability Indices ◽  
Capability Analysis ◽  
Future Extension ◽  
Strong Normality

Strong normality assumption is associated with widely used process capability indices such as cp, cpk. Violation of the assumption will mislead the interpretation in applications. A nonparametric method is proposed for density estimation of any unknown distribution. Kernels are used for density estimation and metropolis-hastings (M-H) algorithm is adopted to generate samples from the density. M-H sampling provides a tool to accommodate different kernel functions and flexibility of future extension to multivariate cases. Conformity (yield) based indices (yp, y) are adopted to replace cp, cpk. These indices can be conveniently assessed by the proposed kernel density based M-H algorithm (K-M-H). The method is validated by several simulation case studies.

Process design and capability analysis using penthagorean fuzzy sets: surgical mask production machines comparison

2021 ◽  
pp. 1-13
Author(s):  
Elif Haktanır ◽  
Cengiz Kahraman
Keyword(s):  
Fuzzy Sets ◽  
Process Capability ◽  
Intuitionistic Fuzzy Sets ◽  
Process Capability Analysis ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Capability Indices ◽  
Capability Analysis ◽  
Tremendous Importance ◽  
Picture Fuzzy Sets

Process capability analysis (PCA) is a tool for measuring a process’s ability to meet specification limits (SLs), which the customers define. Process capability indices (PCIs) are used for establishing a relationship between SLs and the considered process’s ability to meet these limits as an index. PCA compares the output of a process with the SLs through these capability indices. If the customers’ needs contain vague or imprecise terms, the classical methods are inadequate to solve the problem. In such cases, the information can be processed by the fuzzy set theory. Recently, ordinary fuzzy sets have been extended to several new types of fuzzy sets such as intuitionistic fuzzy sets, Pythagorean fuzzy sets, picture fuzzy sets, and spherical fuzzy sets. In this paper, a new extension of intuitionistic fuzzy sets, which is called penthagorean fuzzy sets, is proposed, and penthagorean fuzzy PCIs are developed. The design of production processes for COVID-19 has gained tremendous importance today. Surgical mask production and design have been chosen as the application area of the penthagorean fuzzy PCIs developed in this paper. PCA of the two machines used in surgical mask production has been handled under the penthagorean fuzzy environment.

scholarly journals Process Capability Analysis in the Manufacturing of Pet Bottles

2010 ◽  
Vol 3 (S1) ◽  
pp. 531-534
Author(s):  
Maja Rujnić-Sokele ◽  
Mladen Šercer ◽  
Damir Godec
Keyword(s):  
Process Capability ◽  
Process Capability Analysis ◽  
Pet Bottles ◽  
Capability Analysis
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