Statistical Process Control System Research Articles

Statistical Process Control (SPC) under the Quality Approach of Just In Time (JIT) Manufacturing Philosophie and an Application

Purpose –The objective of this study is to apply Statistical Process Control (SPC) system to the cog wheel production. Continuous quality improvement of manufactured products is a fundamental assumption of Just in Time (JIT) manufacturing. One of the tools to achieve JIT quality through the elimination of variability is Statistical Process Control (SPC). Statistical Process Control is a powerful collection of problem-solving tools in achieving quality about process stability and improving capability through the reduction of variability.Variation can be significantly reduced by the help of statistical process control. This study is aimed at examining the added value of Statistical Process Control (SPC) in terms of quality when applied in production lines. Design\methodology\approach –The scope of this study is to examine gear wheel production process in the X jeep factory. Before this study was carried out, there was no work on SPC in this factory. First of all the main problem in the gearboxes was understood and the ways for solving the main problem was explained.Findings –It was found out that SPC is not only a simple control method to determine defectiveproduction but also it is a method for blocking defective production. Discussion –Statistical Process Control (SPC) is the most widely used and effective tool that keeps under control of variability in the process.

Tool wear monitoring in roughing and finishing processes based on machine internal data

Data analytics plays a significant role in the realization of Industry 4.0. By generating context-related persistent datasets, every manufacturing process in real production becomes an experiment. The vision of Internet of Production (IoP) is to enable real-time diagnosis and prediction in smart productions by acquiring datasets seamlessly from different data silos. This requires interdisciplinary collaboration and domain-specific expertise. In this paper, we present a novel tool wear monitoring system for milling process developed in the context of IoP. This system is based on high-frequency data from the numerical control of the production machine without additional sensors. The novelty of this paper lies in the introduction of virtual workpiece quality and fusion of multiple build-in sensor signals and a force model as decision support. This bridges the time gap between quality inspection and production at the shop floor level, establishes an automated statistical process control system, and provides a more plausible prediction of tool lifetime. The monitoring of two different milling processes in a real production environment is exemplary demonstrated in this paper. The first case is a face roughing process with the aim of rapidly removing large amounts of material. The second case is a face finishing operation that follows roughing and aims to achieve the desired surface quality.

Unsupervised concept drift detection based on multi-scale slide windows

In the past few decades, research related to concept drift learning has been increasing, and many concept drift learning algorithms have also been developed and applied to actual data stream processing. In general, concept drift research involves the development of methodologies and techniques for drift detection, understanding and adaptation. This paper focuses on concept drift detection, and proposes an unsupervised concept drift detection algorithm based on multi-scale slide windows, where the total average distance is obtained through k-means clustering and multi-scale windows and is used as a detection index for concept drift, and then uses the statistical process control system to determine the range of index thresholds. Proved by experiments of detecting the gradual and abrupt concept drift with five datasets of different dimensions, including Sin, Circle, Gaussian, Radar and Motion Sense datasets, the algorithm has a good concept drift detection effect.

Statistical process control and CAQ systems as a tools assuring quality in the automotive industry

Abstract The aim of the article is to present the case study of implementation of the example CAQ system, which allows to meet the requirements of IATF 16949:2016 and the VDA 6.1 standards in the field of statistical process control (SPC). The foundations of the CAQ systems concept and their specific requirements, especially for companies operating in the automotive industry, for which modern CAQ tools are necessary, in the described case based on the LEAN-QS program, are presented. The article presents the observations and results of the analysis of the operation of the quality assurance system in a company that is a supplier of car parts. One of the modules of the LEAN-QS program was implemented there, which makes it possible to meet the requirements of a certified quality management system. The effectiveness of the presented tool was demonstrated, allowing to meet industry requirements while minimizing resources necessary for supervision and proper implementation of the quality management system process, which in this case is the SPC.

Computer Vision based Object Detection System for Statistical Process Control

Computer Vision based Object Detection System for Statistical Process Control

Statistical Process Control (SPC) and Quality Management Systems as a Specialty of Quality Management and Case Turkey

Concept of quality, quality assurance, quality policy and quality management have long since become indispensable in economic aspects. Quality management systems and their meaning are no longer discussed every now and then they have become reality and economic necessity. Also, in public administration, in politics, in education and in the non-profit sector, declarations of quality and their steering and securing are of fundamental importance. The general trend is higher expectations as to the quality of the most diverse products - whether physical products or services - is accompanied by the growing awareness that only through continuous and consistent improvements. A high product quality adds to satisfy the needs of the population. Therefore, the extraction of high-quality information based on the measurement technology. The measurement technology is integrated into the production process maximally strongly.

Improvement of Water Distillation Plant by Using Statistical Process Control System

Improvement of Water Distillation Plant by Using Statistical Process Control System

Design and implementation of SPC systems in defense industry manufacturing process

In this paper Statistical Process Control (SPC) was proposed for manufacturing process of a defense product manufacturing company, with the aim of improvement product quality. The production process was monitored from material input to the final output. The processes were mapped and SPC system was proposed. The system was tested by collecting data at the collecting points and analyzed by appropriate SPC tool such as: X R chart,( X) S chart and the p-chart. The results show that the processes in the some parts in production are in the under control, processes are capable, but not centered, also some sub-processes of production are not in the control but also not capable.

Silko-Scalese Machining Corporation

Alan Silko must decide whether to invest in seven statistical-process-control (SPC) stations in order to increase his chances of becoming a select supplier for a large computer company. The student must do a discounted-cash-flow/decision-tree analysis of the option. The student is also given the opportunity to construct x-bar and range charts and to do an SPC analysis. Excerpt UVA-OM-0661 SILKO-SCALESE MACHINING CORPORATION Alan Silko was perplexed. For over an hour, he had been pleading his case with Harris Computer's purchasing managers to no avail. The discussion revolved around a rejection of 440 parts that were produced by Silko's company, Silko-Scalese Machining Corporation. According to Harris Computer's incoming inspection report, the entire lot of parts had been rejected because one dimension had been out of tolerance on 13 of the 440 parts. The basis of Silko's argument was that he knew the particular application for this part, and the out-of-tolerance condition had no bearing on the functionality of the part. Furthermore, Silko claimed that Harris Computer's engineers had clearly overtoleranced the parts, and there was no way traditional manufacturing processes could consistently yield a part that would meet this unreasonable criterion. Throughout the discussion, Harris Computer managers stressed that it was Silko-Scalese's responsibility to meet the requirements of the drawing, and those requirements were clearly not met. Therefore, there was no reason to discuss the problem any further. As for Silko's claim of the dimension being overtoleranced, Harris Computer managers said that, without some type of data to support this claim, their hands were tied. Their engineers believed that meeting the requirement was feasible, and a supplier's complaint without supporting data did not warrant a change of this requirement. When Silko inquired what kind of information would be required to prove his claim was correct, and what the repercussion of the rejection would be on his company, Harris Computer managers referred to a copy of a letter they had sent to Silko-Scalese Machining and the rest of their major suppliers requesting that each firm implement a statistical process control (SPC) system at its facilities. . . .

Implementation of statistical process control methods as a way to reduce production costs and improve product quality

The article analyzes various situations of building a quality assurance system at an enterprise. The article includes the analysis of influence of various factors on the decision-making on the principles of building a quality assurance system, the conclusion about the need to build the quality assurance system aimed not at preventing the assembly of parts with deviations but on creating a process producing parts without deviations. The stages of implementation of the statistical process control system are considered. Moreover, the article analyzes the characteristics of measuring methods and tools that can be included into the quality assurance system, and examines the various process states allowing making a conclusion about the possibility of their controllability. The example of a comprehensive solution of the statistical process control system is given.

Towards quantified measures of Agility for Production Line Information Systems (PLIS)

The agility in enterprise information (EIS) and business intelligence (BI) systems is summarized as content and structural agility due to changing business requirements. We argue that production lines are key enablers in organizational agility and any hostile and drifting behaviour could turn potential opportunities into losses besides the fact that an agile EIS is well integrated. There are many ISs developed to control and monitor the drifting behaviour of production lines like fault detection and classification (FDC), statistical process control (SPC) and automation systems. These offer excellent detection mechanisms against known drifts; however, real challenge lies in carrying out root-cause analysis for unknown drifts. Therefore, in this paper we propose a new category of IS for production lines as agile PLIS, capable to control, detect, assess and predict the potential drifts for proactive measures. The definition of an agile IS for production line is proposed with quality attributes selected from literature, ISO 91261 software quality standard and specificities of the production line. Further, generic measures of production line agility as local (LAI) and global (GAI) agility indices are proposed to measure the agility drifts for a production line. The agility in an IS is built and not designed; therefore, we also present a compliance matrix of most widely used information system architectures (ISA) against proposed agile PLIS with quality attributes. This paper concludes by validating the proposed quantified measures of agility for agile PLIS using data from reputed semiconductor manufacturer.

Development of fuzzy logic-based statistical process control chart pattern recognition system

This study developed a fuzzy logic system for online variation detection by recognizing patterns in statistical process control (SPC) charts. The system enables quality engineers to take corrective actions for an out-of-control manufacturing process. The fuzzy logic SPC system has three subsystems and is able to detect most common four patterns. Data go through subsystems in the absence of detective patterns. Otherwise, data will be eliminated when a particular pattern is recognized on the SPC chart. The user-friendly output interface reduces human analysis errors. The performance of the developed fuzzy logic system is tested by a simulated process.

Application of statistical process control to qualitative molecular diagnostic assays.

Modern pathology laboratories and in particular high throughput laboratories such as clinical chemistry have developed a reliable system for statistical process control (SPC). Such a system is absent from the majority of molecular laboratories and where present is confined to quantitative assays. As the inability to apply SPC to an assay is an obvious disadvantage this study aimed to solve this problem by using a frequency estimate coupled with a confidence interval calculation to detect deviations from an expected mutation frequency. The results of this study demonstrate the strengths and weaknesses of this approach and highlight minimum sample number requirements. Notably, assays with low mutation frequencies and detection of small deviations from an expected value require greater sample numbers to mitigate a protracted time to detection. Modeled laboratory data was also used to highlight how this approach might be applied in a routine molecular laboratory. This article is the first to describe the application of SPC to qualitative laboratory data.

A development of a web-based and user-centered process analysis system for quality improvement

In order to manage and control quality of the products in a real-time manner, statistical process control methodology, one of the most popular quality control activities, is utilized as a software service in many manufacturing industries. However, these quality control systems are not affordable to small and medium sized industries due to their expense in installation and difficulty in the systems. To reduce installation cost and difficulty of the systems, this research paper suggests web-based statistical process control system with user-centered design. Although number of non-traditional and web-based SPC systems exists, they tend to focus their designs to either beginners or experts of SPC system. The suggested system, PAS, aims to develop the system to be compatible with frequently used browsers and acceptable in both beginners and experts. Lastly, it accepts diverse forms of data so that it attempts to support data from industry to be used directly to the system.

Research on Statistical Process Control for Aluminium Electrolysis Reduction

In order to lower the measurement system errors in aluminum electrolysis reduction, and find out the abnormity of process parameters, this paper considered the process parameters in the production process, used mean-range control chart in statistical process control to analyse aluminium level data during aluminium electrolysis reduction, designed and implemented the statistical process control system for aluminum electrolysis reduction data, presented process improvement method for aluminium level data. Results for the 300KA aluminium electrolysis cell show the statistical process control system can ensure the stability of aluminium electrolysis pot process parameters, find out specific reasons of abnormal data, keep the material balance and energy balance, improve current efficiency, and reduce the energy consumption.

The Intelligent Quality Control Technology System Based on the Integration Methods of SPC and EPC

SPC (statistical process control) and EPC (engineering process control) is the scientific methods of quality quality control and quality improvement. It is the difficult problems of quality control process for network manufacturing enterprise how to effectively solve the dynamic quality fluctuation monitoring and the fluctuation abnormal diagnostic analysis and timely process adjustments, this paper designs the intelligent quality control mode and function system architecture, the modes expatiates quality management network based on quality control network of the workshop level and enterprise class network, and researches the integration applications of statistical process control and enterprise ERP quality system, and applies J2EE technology to achieve the system organic combination of design and development.

Specificity of SPC Procedures Application in Foundry in Aspect of Data Acquisition and Data Exploration

Abstract The paper presents an analysis of SPC (Statistical Process Control) procedures usability in foundry engineering. The authors pay particular attention to the processes complexity and necessity of correct preparation of data acquisition procedures. Integration of SPC systems with existing IT solutions in area of aiding and assistance during the manufacturing process is important. For each particular foundry, methodology of selective SPC application needs to prepare for supervision and control of stability of manufacturing conditions, regarding specificity of data in particular “branches” of foundry production (Sands, Pouring, Metallurgy, Quality).

Application of a Computer Based System for Statistical Process Control in a Semiconductor Company : A Case Study

The use of Statistical Process Control (SPC) in the manufacturing process has been historically proven to increase the quality of the product. Recent trends show that companies are becoming increasingly reliant on computer based-SPC because it can save a significant amount of time compared with traditional SPC. In addition, labor-intensive tasks, such as manual data collection and entry, can be eliminated, thus reducing human error. This paper aims to prove the benefits of computer based system for SPC known as e-SPC in a semiconductor manufacturing environment. Specifically, this paper will present the case study‟s finding that show how one semiconductor manufacturing company‟s use of e-SPC can detect a process abnormality at an early stage and in real time compared with manual SPC. The case study involves interviews with the company representatives and observations on the manufacturing environment. This paper will also show how e-SPC can be used to control and then to stabilize the manufacturing operation. In conclusion, this paper demonstrates that e-SPC can significantly improve the performance of a manufacturing environment. Moreover, this paper can also be used as a reference for the implementation of e-SPC in any company.

A case-based reasoning system development for statistical process control: Case representation and retrieval

Statistical process control (SPC) is a sub-area of statistical quality control. Considering the successful results of the SPC applications in various manufacturing and service industries, this field has attracted a large number of experts. Despite the development of knowledge in this field, it is hard to find a comprehensive perspective or model covering such a broad area and most studies related to SPC have focused only on a limited part of this knowledge area. According to many implemented cases in statistical process control, case-based reasoning (CBR) systems have been used in this study for developing of a knowledge-based system (KBS) for SPC to organize this knowledge area. Case representation and retrieval play an important role to implement a CBR system. Thus, a format for representing cases of SPC and the similarity measures for case retrieval are proposed in this paper.

Wpływ jakości systemu pomiarowego na ocenę współczynników zdolności procesu

Among statistical instruments of quality Statistical Process Control (SPC) and Measurement Systems Analysis (MSA) take particular place. The proper quality of measurement data - verification and assurance of this quality are the task of MSA - guarantees an opportunity to carry out a solid SPC analysis supplying information about a process behavior. One of the principal tasks of SPC is estimation of capability of a process i.e. the assessment of process potential as for variability regarding expectations defined by specification limits. Data for analysis of a process capability are the results of measurements made by means of a determined measurement system. The variability of this measurement system must influence the results of capability assessment, however the measurement system adequate to control the process should guarantee that the impact is little enough to be omitted. The subject of the work is the analysis of influence of measurement system variability on evaluation of process capability indices. The mutual relations between process variability and measurement system variability present analytical relationships and experimental research given in the work. The aim of the tests was an assessment of a capability of the process of steel drop forgings' heat treatment relating to a hardness. The methodology demonstrated in the work is valid quality standards compatible (ISO 9000, ISO/TS 16949 etc.).