Evaluate Process Capability Research Articles

Taguchi Approach to Defect Analysis on Electric Motor Conversion at PT. Electric Vehicle Trimotorindo

Maintaining product quality can reduce the risk of defective products. The survival of a company is heavily reliant on product quality. In order to improve performance and reduce defects in the product process, PT. Electric Vehicle Trimotorindo employs the Taguchi approach in conjunction with the QC system. The goal of this research is to determine the product process of PT. Trimotorindo Electric Vehicle, identify the highest rejection rate, evaluate process capability, and propose improvements using the Kaizen system. This study uses the Taguchi system to optimize the product process of PT. Trimotorindo Electric Vehicles. After collecting data from the taguchi system, it can be concluded that the highest position of blights in a conversion process is in the electric current string.

Statistical Process Control and Capability Six-pack for Conductivity Measurement in Medicinal Chemical Industry

Statistical Process Control (SPC) is indispensable in the medicinal chemical industry for maintaining pharmaceutical product quality and consistency. Background: With complex chemical processes and stringent regulatory standards, robust quality control measures are essential. SPC enables monitoring and control of key process parameters, identification and correction of process variations, and ensuring final product compliance with specifications. This study evaluates the quality and capability of a process measuring medicinal raw material quality from an international chemical manufacturing company. Methods: Data collected from the conductivity measurement process were analyzed using Minitab software. The process specification was fixed at 30 µS·cm-1 as a maximum permissible limit, with 33 subgroups of size one. The computation of the capability six-pack and analysis generated six plots providing insights into process stability, recent behavior, and capability against specifications, distribution verification and capability indices. Minitab® version 17.1.0 software was used to perform a process capability six-pack analysis, including various charts and statistics to assess process stability and capability. Results: The results indicate that while the process is stable, it lacks capability, with variations exceeding acceptable limit. The process does not meet the maximum allowable specification value, with deviations from normal distribution and following logistic dispersion pattern. Conclusion: The study's novelty lies in its comprehensive yet concise presentation of key metrics essential for evaluating process capability and performance, providing insights for process improvement and quality control. Further investigation and improvement actions are needed to address sources of variation and enhance process capability and performance.

A Non-Invasive Method to Evaluate Fuzzy Process Capability Indices via Coupled Applications of Artificial Neural Networks and the Placket–Burman DOE

The capability analysis of a process against requirements is often an instrument of change. The traditional and fuzzy process capability approaches are the most useful statistical techniques for determining the intrinsic spread of a controlled process for establishing realistic specifications and use for comparative processes. In the industry, the traditional approach is the most commonly used instrument to assess the impact of continuous improvement projects. However, these methods used to evaluate process capability indices could give misleading results because the dataset employed corresponds to the final product/service measures. This paper reviews an alternative procedure to assess the fuzzy process capability indices based on the statistical methodology involved in the modeling and design of experiments. Firstly, a model with reasonable accuracy is developed using a neural network approach. This model is embedded in a graphic user interface (GUI). Using the GUI, an experimental design is carried out, first to know the membership function of the process variability and then include this variability in the model. Again, an experimental design identifies the improved operating conditions for the significative independent variables. A new dataset is generated with these operating conditions, including the minimum error reached for each independent variable. Finally, the GUI is used to get a new prediction for the response variable. The fuzzy process capability indices are determined using the triangular membership function and the predicted response values. The feasibility of the proposed method was validated using a random data set corresponding to the basis weight of a papermaking process. The results indicate that the proposed method provides a better overview of the process performance, showing its true potential. The proposed method can be considered non-invasive.

Process quality indices: new metrics for process quality capability with zero-loss baseline

The traditional process capability indices Cp /Cpk for evaluating process capability used in the current industry are based on the probability model of a process distribution, which makes it difficult for many processes whose actual output with non-normal distribution to apply these indices. In this paper, new process quality indices (PQI) Qp /Qpk are proposed for measuring the quality capability of the process. The new PQI are derived from Taguchi's quality loss function model and do not need to estimate the probability distribution of the process output. The traditional process capability indices Cp /Cpk are zero-defect indices, it focus on meeting the process specification requirements, while the new PQI Qp /Qpk are zero-loss indices, it focus on approaching the process target value. The construction of the new PQI is logical, simple, and practical. The PQI approach is more suitable for quality management in the Era of big data.

R&D 분야의 목표 시그마 수준 설정과 설계 공차의 강건 한계 결정에 대한 연구

Abstract The Sigma Level, proposed by Motorola Inc., is one of the many Process Capability Index (PCI)’s that havebeen presented since the 1970’s. It is used to evaluate process capability and unlike other PCI’s, it has anadvantage in that it uses population probability distribution. However, it is originally designed for massproduction and is inadequate to evaluate prototypes or early products in the R&D stages. For use in suchcases, we propose an R&D target Sigma Level, derived by considering 1.5 sigma shifts in traditional sigmalevel from a statistical point of view. We also explain the way to nd robust limits for design tolerancebecause the sigma level or defect probability is useful to establish economical tolerance limits at the R&Dstage and mass production.Keywords: Process Capability Index(PCI), Sigma Level, R&D Sigma Level, 1.5 sigma shift, robust limits,design tolerance 1. 서론 20세기 이후 제품/서비스에 대한 품질 향상의노력은체계적인품질 관리 기법과 품질 개선이론을발전시켜 왔다 (Donaldson, 2004). 특히 1970년대

Advanced gripper development and tests for automated photovoltaic wafer handling

Purpose – The purpose of this paper is to provide a technical review of a new Bernoulli gripper development using computed fluid dynamics (CFD) modeling, and also to outline an appropriate independent testing method for validating and evaluating process capability in terms of automated thin wafer handling. The investigation has been carried out by a collaborative way of Festo and Fraunhofer IPA as a connecting link between applied research and industrial needs. Design/methodology/approach – Following an introduction, the paper first describes the basic development and fundamental principles of a gripper based on Bernoulli's law. The gripper was dimensioned and designed with the aid of CFD methods. The performance of the hardware was tested using extreme parameter settings while gripping thin, fragile workpieces. The performance of the gripper was tested from the aspects of shortest cycle times, positioning accuracy and air consumption and followed a manufacturer-independent design of experiments. A charac...

Evaluation of Process Capability Using Fuzzy Inference System

In many industrial instances product quality depends on a multitude of dependent characteristics and as a consequence, attention on capability indices shifts from univariate domain to multivariate domain. In this research fuzzy inference system is used to determine the process capability index. Fuzzy sets can represent imprecise quantities as well as linguistic terms. Fuzzy inference system (FIS) is a method, based on the fuzzy theory, which maps the input values to the output values. The mapping mechanism is based on some set of rules, a list of if-then statements. In this research Mamdani fuzzy inference system is used to derive the overall output process capability when subjected to six crisp input and one output. This paper deals with a novel approach to evaluating process capability based on readily available information using fuzzy inference system.

Measuring PPM Non‐conformities for Processes with Asymmetric Tolerances

Process yield has been the most basic and common criterion used in the manufacturing industry for evaluating process capability. The Cpk index has been used widely in the manufacturing industry. In this note, we considered a generalization of Cpk index which handles processes involving a target T with asymmetric tolerances. Particularly, we established a formula for measuring the PPM non‐conformities for given ratios of the two‐side tolerances. We proved the validity of the established formula and tabulated the upper bounds on PPM non‐conformities for various given Cpk index values and ratios of the two‐side tolerances. Copyright © 2012 John Wiley & Sons, Ltd.

Three New Multivariate Process Capability Indices

Different multivariate process capability indices are developed by researchers to evaluate process capability when vectors of quality characteristics are considered in a study. This article presents three indices referred to as NCpM, MCpM, and NMC PM in order to evaluate process capability in multivariate environment. The performance of the proposed indices is investigated numerically. Simulation results indicate that the proposed indices have descended estimation error and improved performance compared to the existing ones. These results can be important to researchers and practitioners who are interested in evaluating process capability in multivariate domain.

Process Capability Evaluation for Square Bumps with Mean Shift

Abstract Process capability indices have been widely used in manufacturing industries to provide numerical measures of process potential and performance. In in-plant applications, some inevitable process mean shift may be undetected when the statistical process control charts are applied, particularly, in the bumping process for square bumps. Bothe in 2002 provided a statistical reason for considering such a shift in the process mean to accommodate the undetected mean shifts for normally distributed processes when evaluating process capability. In this paper, we consider a bumping process for square bumps in which the data can be formulated as a non-central chi-square distribution, a class of non-normal distributions. To evaluate the popular yield-based process capability Cpk more accurately, we accommodate the magnitudes of undetected mean shifts using the modified capability evaluation formula and tabulate the detection powers under various subgroup sizes and non-central chi-square parameters. Based on the modified process capability evaluation, we can provide a more reliable process capability evaluation of capability index Cpk for square bumps and make more correct decisions. For illustration purpose, a real application in a bumping factory which is located on the Science-based Industrial Park in Hsinchu, Taiwan, is presented.

WE‐C‐214‐08: Benefit of an Industrial Engineering Tool (Statistical Process Control) in Radiation Oncology: Clinical Experience

Purpose:1. Develop a tool for data collection, retrieval, visualization, real‐time feedback and advanced statistical analysis of IMRT QA process.2. Evaluate the need for site‐specific tolerances for IMRT QA.3. Timely identify systematic drifts in the IMRT QA measurements.Methods: Statistical process control (SPC) is a statistical tool that can be used to study process variations and evaluate process capability. This tool is applicable to IMRT QA processes. There are two causes of variation present in a process — common cause, which is the reason for random variation in the process, and special (assignable) cause, which is the reason for identifiable variation in the process. Process capability and control charts (S chart for process variability and EWMA chart to study the process mean) are two of the several tools available to study the process of interest. We use SPC and paperless environment for collection and processing of clinical IMRT QA data to create a tool for improved management of IMRT QA. Results: Automatic data collection, data validation and visualization are facilitated with the help of the developed tool. Data visualization and control charts are used to study the process variation and identify drifts and outliers. Process capability studies identify systematic variations present in the process and evaluate the need for site‐specific tolerances. For instance, process capability studies of the QA results for treatment plans using 6 MV X‐rays revealed a 1.1% variation in calibration and 0.5% variation in beam model and MLC leaf model. Conclusion: We have built a tool that collects data, analyzes them, plots S chart for process variability and EWMA charts. Process capability studies show that using a global tolerance for all different sites is not sufficient. Hence, we propose the use of 2 standard deviations for a 95% confidence level in the IMRT QA measurements.

A measuring model of process capability to consider sampling error

Most products have multiple characteristics, and customers accept products whenever all process capabilities of each characteristic satisfy preset specifications. Obviously, univariate process capability indices cannot meet the requirements stated as above. And Chen et al. (2001) used point estimation to evaluate process capability, even though the method agrees with 100% tests. If the check is sampling, the process capability must consider sampling error. So the research constructs a measuring model of process capability to consider sampling error to evaluate process capability for a multi-process produce based on Cpk which was proposed by Kane (1986).

Multi-process capability plot and fuzzy inference evaluation

Process capability indices C p, C pk, C pm and C pp fitting for nominal-the-best type quality characteristics, are effective tools to assess process capability since these indices can reflect a centering process capability and process yield adequately. The index C pp introduced by Greenwith and Jahr-Schaffrath [Greenwith, M., Jahr-Schaffrath, B.L., 1995. A process incapability index. International Journal of Quality and Reliability Management 12, 58–71] provides additional and individual information concerning the process accuracy and the process precision. Although C pp is useful to evaluate process capability for a single product in common situation, C pp cannot be applied to evaluate the multi-process capability. Referring to Vännman and Deleryd's ( C dr, C dp)-plot, a fuzzy inference approach is proposed in our study to evaluate the multi-process capability based on distance values of a confidence box. This method takes the advantages of fuzzy systems such that a grade instead of sharp evaluation result can be obtained. An illustrated example of color STN display demonstrates that the presented method is effective for assessment of multi-process capability.

Applied Product Capability Analysis Chart in Measure Step of Six Sigma

This paper will apply product capability analysis chart PCAC in Measure step of Six Sigma. Because the test model is sampling, sampling error must be consider. Thus minimum value will be used to evaluate process capability. Besides the paper used minimum value to evaluate process capability, we also applied the concept of Six Sigma in PCAC and construct PCAC of Six Sigma standard. Finally, PCAC of Six Sigma standard will apply in Six Sigma project of Offset Ratchet Wrenches which manufactured by Y company.

Process performance estimation on the quality characteristics of auto engines

The engine is a vital element in auto parts and is composed of numerous quality characteristics. This paper will systematically classify these quality characteristics, quality dimensions and quality configurations based on the hierarchy. The quality characteristics of an auto can be classified by three levels, the basic, safe and honorable needs. When the basic needs, horsepower, torque and fuel-consumption can’t meet the consumer’s requirement, then even if the product possesses a high level of safe and honorable needs, it is meaningless. Therefore, this paper will develop a process capability index to assess the process performance for auto engines. There are many types of engines and every type is composed of numerous quality characteristics. Numerous types of products make up a product family, so a process capability index for a process with multi-characteristics is used to assess the process performance. However, the index is still an inadequate measure in evaluating process capability for products with numerous unilateral specifications. Therefore, this paper will develop a novel process capability index to assess the process performance for engines, each type with numerous quality characteristics. Furthermore, this paper is also based on the process capability index for every type of engine, and constructs a process capability index to assess the process capability for the entire product family. Managers in industry can apply this method to analyze the process capability of a product family. The answer to the requirements can thus be obtained rapidly, and then the process capability of each abnormal product can be analyzed. In this way the best improvements can be achieved with the least amount of time and effort.

Making decision to evaluate process capability index C p with fuzzy numbers

The process capability index C p has wide applications in the manufacturing industry. This paper extends those applications to a fuzzy environment, with a methodology for testing the index C p of fuzzy numbers. A pair of nonlinear functions is formulated to find the α-cut of index \(\widetilde{C}_{p} \). From various values of α, the membership function of index \(\widetilde{C}_{p} \) is constructed, and the probability of rejecting the null hypothesis is calculated based on this membership function. Different from classical tests, the statistical decision proposed in this paper shows a grade of acceptability of the null hypothesis and the alternative hypothesis, respectively. With crisp values, the developed approach not only can boil down to the classical formula for calculating Ĉ p , but also lead to a binary decision: to reject or to accept the null hypothesis. An example is used to illustrate the performance of the proposed approach.

Process capability analysis for a multi-process product

In this paper, we propose a flow path to evaluate the process capability of an entire product composed of multiple process characteristics. There are six steps in the flow path. Whether process data comply with a normal distribution or a non-normal distribution, the flow path can be applied. Based on Cpu, Cpl, and Cpn, the research aims to develop a multi-process capability analysis chart (MPCAC) model to evaluate process capability in a normal distribution. Similarly, the research aims to define non-normal multi-process capability analysis chart (NMPCAC) to evaluate process capability in a non-normal distribution based on Npu, Npl, and Npn.

Application of Neuro-fuzzy inference for process capability evaluation

Process capability index Cpm is an effective tool to evaluate process capability since the index can reflect a centering process capability adequately. An neuro-fuzzy inference method is proposed in this study to evaluate the process capability based on an estimated process capability index of Cpm calculated from sampled data. This method combines the advantages of fuzzy systems and neuro-networks such that a grade instead of sharp evaluation result can be obtained. Inference is based on a fuzzy statement associated with a statistical testing hypothesis formed with α -risk and p -value th t is related to the estimated process capability index and a product designer's concerns. Furthermore, a neuro-network is used to refine the membership function and to generate a Sugeno if-then rule in a systematic way. Result of the inference is described as a score value of the process capability index and is used to represent the grade of the process capability. An illustrated example of ball-point pens demonstrates that the presented method is effective for evaluation of centering process capability.

Statistical analysis of process capability indices with measurement errors

AbstractProcess capability indices (PCIs) have been widely used in manufacturing industries to provide a quantitative measure of process potential and performance. While some efforts have been dedicated in the literature to the statistical properties of PCIs estimators, scarce attention has been given to the evaluation of these properties when sample data are affected by measurement errors. In this work we deal with the problem of measurement‐error effects on the performance of PCIs. The analysis is illustrated with reference to $C_p$ and $C_{pk}$, i.e. the two most common measures suggested to evaluate process capability. Copyright © 2002 John Wiley & Sons, Ltd.

Process Quality Analysis of Products

Process capability indices are widely regarded as a flexible means of evaluating the quality and performance of a process. Accreditation by the Quality System Requirement QS9000 requires an agent to conduct a global evaluation on the process capability over the entire product family from a plant. Generally, the process capability of the whole product family is an important reference for evaluation when producing a new product or designing a new process. Therefore developing a scheme to evaluate the process capability of the whole product family or series is crucial for industry. This paper takes the production of capacitors as an example of a new method for evaluating the process capability of a whole product family. As the quality characteristic for producing capacitors is “the smaller the better”, the upper specification limits of the capacitors of various specifications should first be established. The process capability index Cpu for products for which “smaller is better” is then used as an evaluation tool to calculate the process capability indices for individual specified products. The relationship between the process capability indices and the yield will be studied. A process capability index is proposed to reflect the global yield of the whole product family and to serve as a reference for evaluating process capability for designing new processes or developing new products. The process capabilities for all products can thus be evaluated and the performance of each process can be evaluated according to the colour management method. Managers can determine easily whether products meet their specifications and take action to improve them if necessary, according to the colour management method.