Statistical Process Control Data Research Articles

A Data-Based Tool Failure Prevention Approach in Progressive Die Stamping

The research on methods for monitoring sheet metal stamping is benefiting from the increased availability of enabling technologies such as sensors, data mining software, cloud computing, and artificial intelligence. The predictive maintenance policies of tools (punches and dies) can be targeted at monitoring progressive wear or at the detection of sudden failures or anomalies. Early detection of tool failure is the method preferred by the recent literature on data management in sheet metal stamping. However, the stamping of small parts poses challenges due to multiple tools and signals and limited visibility of die wear, requiring management of multiple sensors and data sources. This paper proposes a failure prevention approach for progressive die stamping using global and local force sensors with upper bounds for maximum values to indicate unhealthy conditions. The methodology was tested on millions of small washers made of carbon steel. The stamping process was implemented using a servo-press with a high rate. The press was equipped with eight in-process sensors, including strain gauges, thin foil force sensors, and acoustic sensors. The data of material properties, maintenance reports, statistical process control data, and in-process sensors were collected and stored in a data lake. By combining the in-process sensor acquisition with the corresponding log events and maintenance data in the same time span, it is possible to look for correlations among the variables and build an effective tool health prevention policy.

An Interactive Visualization Web Application for Industrial-Focused Statistical Process Control Analysis

Statistical process control (SPC) implementation plays a major role in quality assurance during the manufacturing process. Nevertheless, the adoption rate of SPC commercial software solutions is unsatisfactory in most Malaysian manufacturing companies due to high software subscription costs and difficulties in applying the software without proper know-how, guidance, and training. This study proposes the development of a purpose-built interactive data visualization web application for rapid SPC analysis in the manufacturing industry using open-sourced software packages. An agile software development model is applied as the software development methodology. In the requirement phase, an interview session was conducted to identify project requirements among stakeholders, i.e. industrial practitioners that are involved with SPC analysis. Based on the feedback and expectations from stakeholders, a design of a web application for SPC analysis that incorporates interactive parameter settings and automated reporting was proposed. The web application was developed using the R programming language and the Shiny package library, and deployed at ShinyApps.io, a web service provider. For evaluation, a usability testing procedure was designed and conducted with five industrial SPC practitioners to determine the usefulness of the web application. The outcome of the usability testing indicated positive results and feedback from evaluators. In conclusion, the developed web-app can assist users, particularly from the manufacturing industry sectors, to perform fast SPC data analytics, visualization, and reporting with ease.

Stability of diagnostic rate in a cohort of 38,813 colorectal polyp specimens and implications for histomorphology and statistical process control

This work sought to quantify pathologists’ diagnostic bias over time in their evaluation of colorectal polyps to assess how this may impact the utility of statistical process control (SPC). All colorectal polyp specimens(CRPS) for 2011–2017 in a region were categorized using a validated free text string matching algorithm. Pathologist diagnostic rates (PDRs) for high grade dysplasia (HGD), tubular adenoma (TA_ad), villous morphology (TVA + VA), sessile serrated adenoma (SSA) and hyperplastic polyp (HP), were assessed (1) for each pathologist in yearly intervals with control charts (CCs), and (2) with a generalized linear model (GLM). The study included 64,115 CRPS. Fifteen pathologists each interpreted > 150 CRPS/year in all years and together diagnosed 38,813. The number of pathologists (of 15) with zero or one (p < 0.05) outlier in seven years, compared to their overall PDR, was 13, 9, 9, 5 and 9 for HGD, TVA + VA, TA_ad, HP and SSA respectively. The GLM confirmed, for the subset where pathologists/endoscopists saw > 600 CRPS each(total 52,760 CRPS), that pathologist, endoscopist, anatomical location and year were all strongly correlated (all p < 0.0001) with the diagnosis. The moderate PDR stability over time supports the hypothesis that diagnostic rates are amendable to calibration via SPC and outcome data.

Identifying Unnatural Variation in Precision Rotational Part Manufacturing

In the manufacturing industry, it is well known that in-process variation is a major contributor to poor quality products. In order to fabricate a precise part, the source of unnatural variation (UV) needed to be properly identified, monitored and controlled while the process is running. In relation to this issue, this study aims to identify the error root causes of UV in bivariate process associated with statistical process control (SPC) chart patterns. In research methodology, in-process variation in manufacturing roller head component was discussed systematically based on real product of roller head, computer aided design (CAD) and statistical process control (SPC) chart patterns. Initially, the CAD software was used to model a precise rotational part, and to analyse the cause of UV. Then, the programming software was used to generate the artificial SPC data streams based on an established mathematical model. Data generation also involved linear correlation between two dependent variables (bivariate). The outcome of this study would be helpful for industrial practitioners as a database when applying SPC for monitoring bivariate process.

The Fuzzy Process Quality Evaluation Model for the STB Quality Characteristic of Machining

Whether it is important components of a machine tool itself or various important components processed by the machine tool, many vital quality characteristics mostly belong to the smaller-the-better type. When the process quality levels of these quality characteristics do not attain to the criteria, friction loss may increase during the machine operation, affecting not only the process precision and accuracy but also the lifetime of the product. Therefore, this study applied a smaller-the-better six-sigma quality index simultaneously demonstrating process quality level and process yield. Besides, in coping with statistical process control data, a one-tail confidence-interval-based fuzzy testing method was developed to evaluate process quality. Because this approach is built on the basis of confidence intervals, it can reduce the possibility of misjudgment resulting from sampling errors as well as integrate past experience to enhance the accuracy and precision of the assessment, and then it can grasp the timeliness of improvement.

Statistical process control and interrupted time series: a golden opportunity for impact evaluation in quality improvement

Time series plots are widely used, across sectors and media, probably because many find them easy to understand. Figure 1 is a time series plot of how the readmission rate...

Squeegee bump technology

An innovative solder bumping technology, termed squeegee bumping, has been developed at Motorola's Interconnect Systems Laboratory that uses baked photoresist as a mask for solder printing to deposit fine pitch solder bumps on wafers. This process provides much better alignment accuracy and is capable of bumping finer pitch devices than stencil printing technology. Solder paste printing uses a screen printer similar to that used for stencil printing. Greater versatility of solder materials can be obtained through solder paste than the electroplating. Cost modeling shows that the squeegee bump technology has a significant cost benefit over controlled collapse chip connection (C4) technology. This is because the C4 process has very low efficiency in labor and materials usage. Statistical process control data show an average bump height of 118 /spl plusmn/ 3.5 /spl mu/m, and a maximum-to-minimum bump height range of 17 /spl mu/m over a 150 mm-diameter wafer and have been produced repeatedly on test wafers with 210 /spl mu/m peripheral pitch. A 109.6 /spl plusmn/ 1.3 /spl mu/m bump height on orthogonal array with 250 /spl mu/m pitch has been successfully demonstrated with greater than 90% die yield. Bump reliability has been studied using both multiple reflows and extended thermal/humidity storage procedures. No degradation of shear strength was observed after up to 10 /spl times/ reflows and 1008 h of a thermal/humidity stress environment. Bump reliability was also evaluated by assembling squeegee bumped dice on a plastic chip scale package (CSP). Liquid-to-liquid thermal shock cycling at a temperature range of -55/spl deg/C to +125/spl deg/C had a characteristic life of 2764 cycles with a 1st failure at 1050 cycles. No failures were observed after 432 h of autoclave stress at 121/spl deg/C, 100% RH, 15 psig test condition.

Transforming Non-Normal Data to Normality in Statistical Process Control

Quality characteristics analyzed in statistical process control (SPC) often are required to be normally distributed. This is true in many types of control charts and acceptance sampling plans, as well as in process capability studies. If a characteristic is not normally distributed, but normal-based techniques are used, serious errors can result. One approach to solving this problem is to transform the non-normal data to normality using the Johnson system of distributions. In this paper, we use the sample quantile ratio, in conjunction with the Shapiro-Wilk test of normality, to find a suitable transformation for non-normal data. Examples of fitting non-normal SPC data are presented and discussed. The effect of the Johnson transformation on an SPC procedure involving an estimator for the population standard deviation is studied using non-normal data and Johnson-transformed data.

Effectiveness of a physician education program in reducing consumption of hospital resources in elective total hip replacement.

A physician education program altered the consumption of hospital resources by orthopedic surgeons doing total hip replacement. They were presented with verbal and written physician-specific materials during a 7-week period. Xbar and R charts were used to display measurements of resource consumption before and after the educational program. Physicians were provided with surgeon-specific data profiling their own practice and that of their peers. Inferential statistics were used to validate the effects of education. Length of hospital stay for patients receiving total hip replacement dropped from 13.7 days to 9.9 days. Adjusted total charges dropped from $22,103 to $18,607. The variance for length of stay and total charges dropped by one half or more. Physician education programs are effective in reducing consumption of hospital resources. Statistical process control data formatting appears to make the data more usable to these physicians.

A HYBRID METHOD FOR FITTING DISTRIBUTIONS TO DATA AND ITS USE IN COMPUTING PROCESS CAPABILITY INDICES

A common assumption in capability studies for statistical process control data is that the individuals follow a normal distribution. If not, the conclusions of the study are likely to be invalid. To solve this problem, we propose a method to first help ..

Multivariate Profile Charts for Statistical Process Control

The multivariate profile (MP) chart is a new control chart for simultaneous display of univariate and multivariate statistics. It is designed to analyze and display extended structures of statistical process control data for various cases of grouping, reference distribution, and use of nominal specifications. For each group of observations, the scaled deviations from reference values are portrayed together as a modified profile plot symbol. The vertical location of the symbol is determined by the multivariate distance of the vector of means from the reference values. The graphical display in the MP chart enjoys improved visual characteristics as compared with previously suggested methods. Moreover, the perceptual tasks required by the use of the MP chart provide higher accuracy in retrieving the quantitative information. This graphical display is used to display other combined univariate and multivariate statistics, such as measures of dispersion, principal components, and cumulative sums

Multivariate Profile Charts for Statistical Process Control

The multivariate profile (MP) chart is a new control chart for simultaneous display of univariate and multivariate statistics. It is designed to analyze and display extended structures of statistical process control data for various cases of grouping, reference distribution, and use of nominal specifications. For each group of observations, the scaled deviations from reference values are portrayed together as a modified profile plot symbol. The vertical location of the symbol is determined by the multivariate distance of the vector of means from the reference values. The graphical display in the MP chart enjoys improved visual characteristics as compared with previously suggested methods. Moreover, the perceptual tasks required by the use of the MP chart provide higher accuracy in retrieving the quantitative information. This graphical display is used to display other combined univariate and multivariate statistics, such as measures of dispersion, principal components, and cumulative sums

The Effects of Performance Feedback on the Implementation of a Statistical Process Control (SPC) Program

The present study applied a performance feedback strategy to improve implementation of a Statistical Process Control (SPC) quality control program in a small machine shop. Behaviors involved in collecting and recording SPC data were targed, and feedback was provided by the shop foreman in face-to-face interactions with workers. The effect of the feedback on implementation was assessed in a mixed A-B-A reversal-multiple baseline across subjects design. The results showed that the percentage of components of the SPC process completed by three machine operators was initially low (baseline) and that implementation improved dramatically when the feedback program was added (intervention). Improved performance remained at high levels following removal of the feedback program for two of three workers. No consistent effects of the program on quality of products manufactured was observed.