Conventional Quality Control Methods Research Articles

Quality 4.0: Learning quality control, the evolution of SQC/SPC

This article marks a significant advancement in the field of quality management, specifically focusing on the evolution from traditional Statistical Quality Control (SQC) and Statistical Process Control (SPC) methods to a more advanced Learning Quality Control (LQC) approach. The research introduces Quality 4.0 (Q4.0) as a novel paradigm that fuses the technologies of the fourth industrial revolution, Manufacturing Big Data (MBD), Industrial Internet of Things (IIoT), Cloud Storage and Computing (CSC) and Artificial Intelligence (AI), with traditional quality management practices. The central theme of this study is exploring the limitations inherent in conventional quality control methods when faced with the complexities of modern manufacturing environments. The authors propose LQC systems as a solution, employing binary classification algorithms to predict and detect defects in manufacturing processes. This represents a shift from reactive to proactive quality measures enabled by AI’s real-time data processing capabilities. The document delves into the evolution of manufacturing data across industrial revolutions, highlighting the exponential growth of unstructured data and its challenges. Through case studies, the authors illustrate the practical applications of LQC systems, demonstrating their ability to learn complex patterns in hyperdimensional spaces and automate tasks traditionally performed visually.

Surface quality prediction for FDM-printed parts using in-process material flow data

Efficient and sustainable manufacturing practices rely on the early detection and removal of faulty components in production processes. In the context of Fused Deposition Modeling, this means that identifying defective parts during the production cycle can help to minimize waste and optimize resource utilization. However, conventional quality control methods, which involve post-process inspection, can be time-consuming and inefficient, particularly if nonconforming parts are detected after the production cycle is complete. To address this issue, a real-time quality prediction system has been developed that utilizes in-process flow sensor data to detect and identify nonconforming parts as they are being produced. The system was tested on cuboid test specimens, which were deliberately modified to include defects on the surface of the part. By analyzing the sensor data in real-time, the system was able to identify the defective parts and provide corrective actions to minimize waste and optimize resource utilization. By implementing this approach, manufacturing processes can be streamlined and resource utilization can be optimized while minimizing the production cycle time. This approach represents a significant advance over traditional quality control methods, which rely on post-process inspection and human factors.

Quality 4.0 – an evolution of Six Sigma DMAIC

Purpose Manufacturing companies can competitively be recognized among the most advanced and influential companies in the world by successfully implementing Quality 4.0. However, its successful implementation poses one of the most relevant challenges to the Industry 4.0. According to recent surveys, 80%–87% of data science projects never make it to production. Regardless of the low deployment success rate, more than 75% of investors are maintaining or increasing their investments in artificial intelligence (AI). To help quality decision-makers improve the current situation, this paper aims to review Process Monitoring for Quality (PMQ), a Quality 4.0 initiative, along with its practical and managerial implications. Furthermore, a real case study is presented to demonstrate its application. Design/methodology/approach The proposed Quality 4.0 initiative improves conventional quality control methods by monitoring a process and detecting defective items in real time. Defect detection is formulated as a binary classification problem. Using the same path of Six Sigma define, measure, analyze, improve, control, Quality 4.0-based innovation is guided by Identify, Acsensorize, Discover, Learn, Predict, Redesign and Relearn (IADLPR2) – an ad hoc seven-step problem-solving approach. Findings The IADLPR2 approach has the ability to identify and solve engineering intractable problems using AI. This is especially intriguing because numerous quality-driven manufacturing decision-makers consistently cite difficulties in developing a business vision for this technology. Practical implications From the proposed method, quality-driven decision-makers will learn how to launch a Quality 4.0 initiative, while quality-driven engineers will learn how to systematically solve intractable problems through AI. Originality/value An anthology of the own projects enables the presentation of a comprehensive Quality 4.0 initiative and reports the approach’s first case study IADLPR2. Each of the steps is used to solve a real General Motors’ case study.

Improving quality control of yucca extracts used as food additives by screening antimicrobial activity using NMR metabolomics.

Yucca schidigera is mainly distributed in southwestern US and the northern desert of Mexico. Its extract is widely used as a food additive for its antimicrobial activity. However, this antimicrobial activity is subject to significant variability across production lots. Yucca extracts are natural products and their composition is affected by their cultivation area and weather. Manufacturer deal with natural products such as food additives pay particularly close attention to quality control. In the present study, NMR metabolomics methods were used to screen the antimicrobial activity of yucca extracts. Yucca extracts were subjected to principal component analysis (PCA) and categorized on a score plot of their 1H NMR spectral data according to their antimicrobial activity. Furthermore, hierarchical cluster analysis (HCA) was also used to classify yucca extracts based on their antimicrobial activity. Classification using PCA and HCA was dependent upon saponin content, particularly that of schidigera-saponin A1 and D1, which was further confirmed by HPLC analysis of the yucca extracts. We demonstrated that NMR-based metabolomics is a potentially useful tool to use in combination with conventional quality control methods for yucca extracts used as food additives. We envisage this method as tool for initially screening the extracts prior to carrying out the officially recommended quality control tests.

A Quality Control Method of Ground-Based Weather Radar Data Based on Statistics

The detection of meteorological targets using ground-based weather radars usually suffers from ground clutter and beam blockage. These nonmeteorological or weakened signals should be identified so quality control should be implemented before weather radar data can be used. Conventional quality control methods aim at differentiating between echo structures of ground clutter and meteorological targets, and use terrain information to calculate beam blockage regions based upon standard atmospheric refraction. However, it is difficult to achieve the goal for long-term large data sets by conventional methods due to the complexity and diversity of weather radar echoes. In this paper, regions of ground clutter and beam blockage are first identified through the statistics on spatial distribution of reflectivity and fuzzy logic classification, and then they are used as masks to remove data from the scan. The new method is applied to data of the Nanjing weather radar in China. By the aid of a proposed evaluation scheme and the visual recognition, quality control results of the new method are compared with those of the conventional methods. It is found that the new method can provide better identification of ground clutter or beam blockage and thus better quality control results. The new scheme has a good prospect in operational service for its principle advantages, easy applicable conditions, and better performance compared with conventional methods.

Quality systems for unit-use testing devices

Unit-use testing or single-test-system analysis has existed for many years. Quality-control and quality-assurance procedures have generally used conventional methods and lyophilized or aqueous control materials. Because these materials were readily available and generally accepted, they became part of the quality-assurance program for many early unit-use test systems such as the DuPont aca. Over the years, these control products became standard and are now required as part of good laboratory practice. Technically speaking, however, conventional quality-control methods and materials cannot completely control the test system when used in a unit-use or single-test-system device. When conventional control material is run on a unit-use single-test system, only that testing unit is checked. One cannot test every unit with control material because by definition these are single-test systems: Once the control has been run, the patient's sample cannot be run. Conventional quality-assurance and quality-control methods do not, of themselves, assure quality. A one-size-fits-all, or "two levels per day of use" as outlined in the CLIA '88 regulations, is not appropriate. The divergence between HCFA-approved practices and those of the deemed agencies, coupled with the financial aspects of this quality-control method, led to the formation of the Subcommittee on Unit Use Testing of the National Committee on Clinical Laboratory Standards to develop guidelines for manufacturers, users, and regulators to use in developing new quality systems.

Process decision program chart: from operations research to total quality control

In conventional quality control methods, where consequences are analyzed to determine actions, often provide the solution too late. Even if the process is initiated as soon as problems occur, small environmental changes might force the entire process to be redesigned. Without the ability to deal promptly with such situations, it is frequently too late to do anything. The Process Decision Program Chart (PDPC) is one of the methods used to solve problems in Operations Research during the design stage in Research and Development. PDPC is an effective means of moving promptly toward one's goal at the very stage of the process where we encounter an unexpected problem and define the solution process when we are dealing with problems that have more than one possible outcome. In this paper, we discuss the application of PDPC for Total Quality Control in the area of process planning. Object-Oriented Programming Technique is proposed in order to implement this method effectively.

Design metrics in practice

The paper describes a method of software quality control based on the use of software metrics. The method is applied to software design metrics to illustrate how design metrics can be used constructively during the software production process. The various types of design metrics and how they can be used to support module (procedure) quality-control are discussed. This involves adapting conventional quality control methods such as control charts to the realities of software, by using: ‘robust’ summary statistics to construct ranges of acceptable metric values; scatterplots to detect modules with unusual combinations of metric values; and different types of metrics to help identify the underlying reasons for a module having unacceptable (anomalous) metric values. The approach is illustrated with examples of metrics from a number of existing software products.

Distribution of patients' test values and applicability of "average of normals" method to quality-control of radioimmunoassays.

Applicability of the Hoffmann's average of normals (Mn) method was evaluated in quality control (QC) of radioimmunoassays (RIA) for thyroxine, 3,5,3'-tri-iodothyronine, thyrotropin, and insulin--assays that are performed routinely in the authors' laboratory. In the first three RIAs, the patterns of the distributions were almost constant and Mn showed significant correlations with values of QC sera and intercepts of the dose-response curve. In insulin RIA, the patterns varied appreciably and Mn showed correlations with parameters that reflect a disturbance in the distribution. Exclusion of assays with abnormal distributions, however, resulted in better correlations of Mn with other QC parameters. These results suggest that average of normals method can be a very useful adjunct to conventional QC methods for RIA. The possibility that the method may be affected by a disturbance in the distribution can be monitored by computation of parameters reflecting such disturbance.

Interferometer for measuring power distribution of ophthalmic lenses

The use of a lateral shear interferometer in measuring the power variation of ophthalmic lenses is described and demonstrated. It is shown that an appropriate lateral shear interferometer directly measures the power variation of an ophthalmic lens. If the ophthalmic lens has a toric surface, the power for each axis can be measured separately. Individual surfaces can be tested, as well as the whole lens or the different segments of a multifocal lens. The sensitivity of the test can be selected by varying the amount of lateral shear. Because of the demonstrated simple relationship between fringe spacing and dioptric power, qualitative examination of the fringes has proved a useful adjunct to conventional quality control methods.