Process Capability Study Research Articles

Investigating the effect of moulding sand properties on investment castings: a process capability study

The purpose of the present investigation is to study the effect of molding sand properties on investment casting (IC) for process capability analysis. Starting from the identification of component/benchmark, ICs were produced with different grades of silica slurry layers (coarse, medium and fine) over the patterns (in the form of a tree). Measurements on a coordinate measuring machine (CMM) helped in calculating the dimensional tolerances of the castings produced. Some important mechanical properties were also compared to verify the suitability of the castings. The study suggested the long-term performance level of the IC process after it has been brought under statistical control as casting solution for plain carbon steel. The provided data from the experimentation highlights the ability of the IC process to produce a product that will consistently meet the design requirements and the customer expectation.

Sample size determination for estimating multivariate process capability indices based on lower confidence limits

With the advent of modern technology, manufacturing processes have become very sophisticated; a single quality characteristic can no longer reflect a product's quality. In order to establish performance measures for evaluating the capability of a multivariate manufacturing process, several new multivariate capability (NMC) indices, such as NMC p and NMC pm , have been developed over the past few years. However, the sample size determination for multivariate process capability indices has not been thoroughly considered in previous studies. Generally, the larger the sample size, the more accurate an estimation will be. However, too large a sample size may result in excessive costs. Hence, the trade-off between sample size and precision in estimation is a critical issue. In this paper, the lower confidence limits of NMC p and NMC pm indices are used to determine the appropriate sample size. Moreover, a procedure for conducting the multivariate process capability study is provided. Finally, two numerical examples are given to demonstrate that the proper determination of sample size for multivariate process indices can achieve a good balance between sampling costs and estimation precision.

Evaluation of turbidity measuring instruments using statistical process control

The quality management system (QMS) and environmental management system (EMS) are currently being widely developed in laboratories, and have contributed to significant improvements in measurement management processes and client opinion. The purpose of this work is to establish appropriate methodologies for assessing the turbidity measurement that guarantee the quality of the results concerning precision and accuracy and also to test the service performance of the Water Analysis Laboratory of the Engineering Department (LAADEG) of Federal University of Lavras (UFLA). The research assessed two bench turbidimeters "A and B" through a statistical process capability study, analyzing the potential capability indexes (Cp) and performance capability (Cpk). We identified special causes also present in the control through non predetermined random patterns. After the studies, it was found that turbidimeter "B" should be used for all turbidity measurements in LAADEG, since its precision was higher than turbidimeter "A".

Design, Yield and Process Capability Study of 8 kV 4H-SiC PIN Diodes

This paper presents a study of performance and scalability of 8kV SiC PIN diodes focusing on area-dependent yield and sensitivity to material properties variation. Successfully fabricated 18 and 36 mm2 SiC-PiN diodes exhibited avalanche breakdown above 8 kV and < 5V forward voltage drop at 100 A/cm2 current density. The fast switching operation of these diodes up to ~5 kHz frequency is evidenced by reverse recovery measurements with by double-pulse inductive switching tests. The devices exhibit 0.142 and 0.169 uC/cm2 stored charge at room temperature and 125oC, respectively, when turned-off from Jf = 100A/cm2 to Vr = 2.1 kV. The measured diode breakdown voltage exhibited location and size dependent yield, indicating the necessity of material quality improvements for production.

Process capability study of rapid silicon moulding for plastic components

The purpose of the present study is to investigate process capability of rapid silicon moulding for plastic components. Starting from the identification of component, prototypes with two different type of plastic material were produced under different vacuum moulding conditions. Measurements on the coordinate measuring machine helped in calculating the dimensional tolerances of the plastic components produced. Some important mechanical properties were also compared to verify the suitability of the components. Final components produced are acceptable as per ISO standard UNI EN 20286-I (1995) and DIN16901. The results of study suggest that rapid silicon moulding process lies in ± 4.5 sigma (σ) limit as regards the dimensional accuracy of plastic components is concerned. This process ensures rapid production of pre-series technological prototypes and proof of concept at less production cost and time.

Process capability study of laser assisted micro milling of a hard-to-machine material

Laser assisted micro milling (LAMM) is capable of generating three-dimensional micro scale features in hard-to-machine materials. This paper compares the process capability of LAMM with conventional micro milling of a hardened tool steel. In particular, the potential advantages of LAMM over micro milling with respect to cutting forces, tool wear, material removal rate, burr formation and surface roughness are investigated when micro milling hardened A2 tool steel (62 HRC). The results show that LAMM has significant advantages over micro milling, especially in terms of cutting forces, material removal rate and tool wear. The average reduction in the resultant cutting force is found to be up to 69% with laser assist. In addition, tool wear is found to be substantially less with laser assist even when the material removal rates are increased by a factor of six over the tool manufacturer recommended cutting conditions.

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.

Process capability study of polyjet printing for plastic components

The purpose of the present study is to investigate process capability of polyjet printing (PP) for plastic components. Starting from the identification of component, prototypes with three different type of plastic material were prepared at different orientations. Measurements on the coordinate measuring machine helped in calculating the dimensional tolerances of the plastic components produced. Some important mechanical properties were also compared to verify the suitability of the components. Final components produced are acceptable as per ISO standard UNI EN 20286-I (1995) and DIN16901. The results of study suggest that PP process lies in ±4.5sigma (σ) limit as regard to dimensional accuracy of plastic component is concerned. This process ensures rapid production of pre-series technological prototypes and proof of concept at less production cost and time.

The Process Capability Study of Pressing Process for Force Closed

The Process Capability Study of Pressing Process for Force ClosedObjective of the statistical assessment process is to determine whether all major manageable causes of instability of the process have been removed. The basis for statistical regulation is management of production processes. If a process operates a large number of random effects, the resulting distribution has the character of a normal distribution. Processes are considered as eligible if the indexes Cp and Cpk are greater than 1.33.

Process capability study of ultrasonic machining for titanium alloys

The purpose of the present investigations is to study the process capability of ultrasonic machining (USM) for titanium and its alloys. For the analysis purpose, Taguchi model of material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR) for USM of titanium and its alloys has been used. Relationships between MRR, TWR, SR and other controllable machining parameters (power rating, tool type, slurry concentration, slurry type, slurry temperature and slurry size) have been deduced. The results of study suggest that the USM process lies in ±4.5 sigma (σ) limit as regard to MRR and TWR is concerned. For SR process capability is ±5.1 σ at the best settings of input parameters while machining of titanium alloys.

Classification of Problems Under Uncertainty, in FEM-Based Analysis and Design of Sheet Metal Forming Operations

During sheet metal forming operations, many different sources of non-controllable process variations usually display their effect, leading to a degree of uncertainty in the final parts’ quality. For this reason, statistical tools are increasingly used in combination with FEM numerical simulation. The focus is moving in recent times beyond mere feasibility to robustness of the product and process being engineered. Ensuring robustness is the next big challenge for the virtual tryout / simulation technology. In this paper, some of the numerous applications of statistical analysis combined with simulation of sheet forming processes are presented and discussed: 1) sensitivity analysis under uncertainty, 2) process capability studies, 3) reliability assessment, 4) reliability optimization, 5) process design optimization under uncertainty. Problems of type 2) and 5) will be particularly addressed, by giving the description of some innovative methods and applications.

Process Capability Analysis Made Simple Through Graphical Approach

The statistical techniques can be used to quantify the variability of the manufacturing process. The process capability analysis helps in quantifying the process variability and assists manufacturing by reducing the variability. Process capability of a manufacturing process can be assessed through the computations of various process capability ratios. Graphical approach can be extended and used to compute capability ratios. A graphical approach of the process capability study is a presentation of process capability parameters in the form of graphs and gives quick information for further computation and analysis. The process capability can be measured and analyzed through the computations of various process capability indices by analytical methods. This involves more computations and time consuming and not user-friendly. The mean and grand mean computations are replaced by median and grand median. Graphical method of computations of median, grand median and average range is developed. Graphical approach can be used for process capability measurement. The graphical approach of process capability study makes it easier to assess the variability of the manufacturing process with less computation and consumes less time and makes user-friendly analysis. The process capability results using median has less affected by extreme values. The median chart indicates on-line variability. If, the control limits are introduced to median chart it performs like average and range chart with fewer computations and consumes less time. This approach helps to worker in the shop floor to assess the process.

Six-Sigma implementation through Competency Based Perspective (CBP)

The popularity of six-sigma has given rise to the question: can any company adopt this rigid data driven approach to achieve higher levels of quality? This research suggests that a company must develop a unique combination of resources and competencies to realize the benefits of six-sigma. According to the competency based perspective (CBP), a company first needs to have the assets, skills, and resources to launch the six-sigma program, and second, it needs to have the expertise to integrate these assets to orchestrate a cohesive implementation of this program. The paper makes the case that most successful six-sigma adopters had initially implemented Total Quality Management (TQM), Baldridge, or some other quality initiative, preparing them to launch this data driven, process oriented approach. For companies that have emphasized quality function deployment (QFD), innovation and problem solving, statistical process control, process capability studies, etc. a transition to six-sigma would be a natural course of action. For others where these practices are at an incipient stage, six-sigma progress can be extremely slow and frustrating. The paper validates these comments with the help of a case study. It was easy for the study company to make the transition to six-sigma because it had captured enough process performance data, customer requirements, infrastructure and leadership development through its long history of quality initiatives.

A Bayesian estimator of process capability index

Process capability and process performance index studies are to assess a process relative to specification criteria. Quantification of this measurement is often reported using various indices. One such index namely Cp has been considered in this paper. The equations for process capability indices are basically very simple; however, they are very sensitive to the input value for standard deviation, as it is not known. Unfortunately there can be difference of opinion on how to determine standard deviation for a given situation. Thus the basic problem is to estimate the standard deviation and thereby estimating the process capability index. The paper provides a Bayes estimator for process capability index Cp when a priori or guessed interval of standard deviation is available. To express the belief of the experimenter, an improper prior distribution is considered. The squared error loss function has been used to assess goodness of the suggested estimator. The Bayes estimator thus obtained has been compared theoretically and empirically with the minimum mean squared error estimator.

Artificial intelligence approach to support statistical quality control teaching

Statistical quality control – SQC (consisting of Statistical Process Control, Process Capability Studies, Acceptance Sampling and Design of Experiments) is a very important tool to obtain, maintain and improve the Quality level of goods and services produced by an organization. Despite its importance, and the fact that it is taught in technical and college courses, as well as in companies’ training sectors, SQC has been largely misused. An inappropriate teaching approach may be the cause of such problem; therefore it has motivated the development of a model for SQC teaching, allowing its learners to correctly apply SQC techniques. After a survey regarding the concept needed to correctly apply SQC, its use and teaching/training methods, the model’s contents and methodology were defined. We also realized the opportunity of incorporating a computer environment for the model, permitting the practice of the needed SQC concepts and skills. An Artificial Intelligence approach was used to develop the computer environment, resulting in an Intelligent Tutoring System, the STCEQ. The paper discusses the main characteristics of the system, its functioning, benefits of using such a system and the results we obtained while using this system.

Characterization of wheat-based biscuit cooking process by statistical process control techniques

A commercial wheat-based biscuit cooking process was characterized using statistical process control (SPC) techniques. These techniques included histograms, analysis of means (ANOM), control charts, process capability studies and Gauge Repeatability and Reproducibility (Gauge R&R). The production variables studied included raw material variables (wheat protein and moisture content) and product variables (biscuit breaking strength, moisture content and thickness of biscuit). The plant studied has three independent production lines, so product variables were analysed separately for each line. ANOM indicated that several process means were significantly different ( α=0.05) from the grand mean (breaking strength line 3, product moisture content line 3 and thickness of biscuit in all lines). Control charts indicated that some observed variables were not in statistical control (wheat protein and moisture content, breaking strength line 3 and thickness of biscuit for lines 1–2). Results from capability studies showed that all observed variables except wheat moisture content were not capable. Notably, there was a large variation in the thickness of biscuit. Gauge R&R suggested that the measurement system used was inadequate, contributing to the variation found in the process characterization. Initial investigation of the relationship between raw material variables and product variables in each line found no correlation.

Characterization of wheat-based biscuit cooking process by statistical process control techniques

A commercial wheat-based biscuit cooking process was characterized using statistical process control (SPC) techniques. These techniques included histograms, analysis of means (ANOM), control charts, process capability studies and Gauge Repeatability and Reproducibility (Gauge R&R). The production variables studied included raw material variables (wheat protein and moisture content) and product variables (biscuit breaking strength, moisture content and thickness of biscuit). The plant studied has three independent production lines, so product variables were analysed separately for each line. ANOM indicated that several process means were significantly different (α=0.05) from the grand mean (breaking strength line 3, product moisture content line 3 and thickness of biscuit in all lines). Control charts indicated that some observed variables were not in statistical control (wheat protein and moisture content, breaking strength line 3 and thickness of biscuit for lines 1–2). Results from capability studies showed that all observed variables except wheat moisture content were not capable. Notably, there was a large variation in the thickness of biscuit. Gauge R&R suggested that the measurement system used was inadequate, contributing to the variation found in the process characterization. Initial investigation of the relationship between raw material variables and product variables in each line found no correlation.

A DMAIC approach to printed circuit board quality improvement

Since the early 1990s, six sigma has been sweeping the business world, driving greater manufacturing and service quality than has ever been seen before. Statistical quality techniques are one of the decisive factors contributing to the success of the six-sigma implementation. Applying statistical quality techniques is especially important in the manufacture of surface-mounted printed circuit boards (PCB). As any defect in the solder joint can lead to circuit failure, the screening process is regarded as the most critical process in PCB manufacturing. According to the current process capability study of a PCB company, the capability of the screening process is under 1.33—the company requirement. That is to say, the current printing process cannot reach a four-sigma level. Therefore, the objective of this study is to improve the sigma level of the screening process through the define-measure-analyse-improve-control (DMAIC) approach. At the early stages, process capability analysis (PCA) and statistical process control (SPC) were used to measure and analyse the current printing performance of the screening machines. During later stages, design of experiment (DOE) was conducted to determine the optimal settings of the critical-to-quality factors in the screening process. By using these optimal settings, six-sigma performance can be achieved.

Process Stability and Capability Studies of Polymer Micro Moulding Process

Process Stability and Capability Studies of Polymer Micro Moulding Process

The use of statistical process control (SPC) to enhance the validation of critical control points (CCPs) in shell egg washing

This study illustrates the application of validation principles to evaluate the scientific and technical content of the critical control point (CCP) in a hazard analysis and critical control point (HACCP) system. A commercial shell egg washing step was selected as a demonstrated process. Validation procedures involved in compliance of the obtained commercially operated data with the related literature and applying the appropriate statistical process control (SPC) tools. The results showed that the critical limits of all control measures defined for this CCP in the observed plant, pH of wash water (11–13), temperature of wash water (32–44 °C), temperature of rinse water (41–49 °C) and chlorine level (100–200 ppm) are in accordance with those generally recommended by many related literature to ensure safe food production. Recommendations suggested by related literature were that pH of wash water should be 10–11, however, >11 was tolerable, temperature of wash water should be at least 12 °C higher than eggs and not in excess of 50 °C, temperature of rinse water should be slightly higher (3–4 °C) than wash water temperature and chlorine (sanitiser) level should be between 100 and 200 ppm. Hence, this CCP was judged to be valid. The evaluation by applying the trial control charts to all collected data over the period showed that the process was not in control in all control measures (presence of tests of assignable causes). The process variability (control limit) was assumed as a “current actual performance” and compared with the recommendations. Most control measures are satisfactory in terms of safe food productions. Some special considerations were made on particular control measures such as wash water temperature and chlorine level. The process capability studies were conducted for treated data and the results showed that all control measures are capable to the designed critical limits except chlorine level whose process spread (6 sigmas) falls outside the width of critical limits.