Quality Control Model for Manufacturing Process Based on GQMM

2011 ◽  
Vol 214 ◽  
pp. 612-617 ◽  
Author(s):  
Zhong Hua Yu ◽  
Juan Zhou
Keyword(s):  
Quality Control ◽  
Manufacturing Process ◽  
Manufacturing System ◽  
Control Model ◽  
Formal Description ◽  
Improve Quality ◽  
Knowledge Based ◽  
Structured Analysis ◽  
Goal Question Metric

A structured quality control model was proposed. It was a multilevel and knowledge-based quality control model which took Goal, Question, Metric, Measure and other fundamental elements as the main line, structured principle as guidance, manufacturing process as carrier, and improve quality of manufacturing system as destination. Firstly, a structured analysis principle was introduced. And then a structured quality control model was proposed combining bearing manufacturing process. A formal description of mappings between Goal-Question-Metric-Measure (GQMM) was also discussed. Finally; the architecture of the model was presented.

scholarly journals Six Sigma Analysis as an Approach to Improve Quality of Corrugated Zinc Sheet Product

2021 ◽  
Vol 6 (2) ◽  
pp. 75-89
Author(s):  
Handy Nur Cahya
Keyword(s):  
Quality Control ◽  
Six Sigma ◽  
Analysis Data ◽  
Improve Quality ◽  
Limit Value ◽  
Five Factors ◽  
Study Results ◽  
Zinc Sheet ◽  
Product Defect

Every manufacturing company will strive to create the best quality products. This is also done by PT. CMSM, a steel company, is producing corrugated zinc products. In several production periods, the defective product values were still found that exceeded the tolerance limit value set at 0.5% in corrugated zinc products. These problems are, of course, very detrimental to the company, so it’s essential to control the quality of corrugated zinc products. This study aimed to determine the factors that cause product defects, propose plans to improve product quality, and provide managerial recommendations based on Six Sigma analysis. Data collection techniques are carried out through observation, interviews, and documentation. Based on the study results, it is known that DPO is 0.000885 and DPMO is 884.6028. At this time, the company is at the level of 4.64 sigma with CTQ (Critical to Quality) folded zinc sheet, cut size is not according to standard, thickness is not according to standard, uneven coating layer and perforated zinc sheet. Based on the analysis, five factors cause product defects: humans, machines, methods, materials, and the environment. To improve quality, the company should improve their quality control through 5W + 1H analysis, namely by providing training to employees, periodic machine maintenance and ensuring that the SOPs that have been made are implemented correctly and adequately. Keywords: six sigma, DMAIC, product defect, quality control, manufacturing

scholarly journals Smart Manufacturing: Quality Control Perspectives

2021 ◽  
Author(s):  
Joseph Evans Agolla
Keyword(s):  
Quality Control ◽  
Manufacturing Process ◽  
Quality Criteria ◽  
Smart Manufacturing ◽  
Physical Conditions ◽  
Manufacturing Operations ◽  
Accuracy And Precision ◽  
Manufactured Products ◽  
Data Analytic

Quality Control (QC) is a guideline or set of procedures intended to ensure that a manufactured product or performed service adheres to a defined set of quality criteria or meets the requirements of the client or customer. Smart manufacturing is where the work is interfaced work pieces and associated tools that include logistics operations, Cyber Physical Systems, Artificial Intelligence, and Big Data Analytic tools. These form the norm of manufacturing operations to generate large amounts of data, which are used for analysis and prediction. Therefore, help to optimise the quality of manufacturing operations and manufactured products. The change in technologies have, however, altered the traditional way of manufacturing process as well as QC systems. Therefore, to address the challenge of data reliability, the sensors, actuators and instruments used at various levels of integration in the manufacturing process often operating under adverse physical conditions need to provide adequate levels of data accuracy and precision. Methodologically, the Chapter followed critical literature review on QC concepts and Industry 4.0 revolution, thereby culminating into conceptual framework of QC in Smart Manufacturing, which is the main contribution of this Chapter.

scholarly journals Agricultural machine parts quality control by dynamic non-destructive methods

2018 ◽  
Vol 226 ◽  
pp. 04034 ◽  
Author(s):  
Alexey N. Beskopylny ◽  
Viktor B. Rykov ◽  
Elena M. Zubrilina ◽  
Andrey D. Chistyakov
Keyword(s):  
Quality Control ◽  
Wear Resistance ◽  
Manufacturing Process ◽  
Mechanical Characteristics ◽  
Point Of View ◽  
Agricultural Machinery ◽  
Machine Parts ◽  
Non Destructive ◽  
Destructive Methods

The problem of quality control of agricultural machinery parts by means of dynamic non-destructive methods of impact indentation of conical shape indenters is considered. Quality of the crucial parts is considered from the point of view of a set of mechanical characteristics that determine the strength, hardness, wear resistance and deformability. These characteristics must be known and be able to control on all the details promptly and accurately both in the manufacturing process and in operation. The mechanical characteristics of metals are stochastic in nature. For this purpose, an impact indentation device was developed, and the method based on probabilistic laws of dispersion of mechanical characteristics is obtained.

A Unified Quality Control Model for E-Learning Systems

2017 ◽  
Vol 7 (3) ◽  
pp. 1355
Author(s):  
Khalid Hamed Allehaibi ◽  
Nasser Nammas Albaqami
Keyword(s):  
Quality Control ◽  
Quality Management ◽  
Learning Process ◽  
Control Model ◽  
Learning Systems ◽  
Learning System ◽  
Quality Requirements ◽  
Learning Platform ◽  
E Learning

Defining, measuring, and achieving quality of e-learning systems are not an easy task. Accordingly, one of the most essential goals for the higher educational institutes is how to reach a high and satisfied level of quality in their learning systems. Achieving such level needs adequate and continuous improvements for the whole e-learning environment elements. Therefore, we aim in our work to construct a unified framework for total quality management system (TQMS) that attempt to satisfy the quality requirements, needs, and standards. The objective of this paper is to present a quality control model for e-learning system that adopts the e-learning platform according to the on-line determination of both user's requirements and global standards. This paper proposed software architecture of quality Management framework for e-learning that could be adopted by different higher education institutes to control the quality of the e-learning process, and assure the quality of the e-learning process outcome. The proposed framework is based on a tri-dimensions quality model. The three dimensions are set of quality requirements for e-learning environment represented in Quality Assurance (QA) policies that will be formalized by using policy based approach, the specifications of e-learning platform that provide learning and teaching activities, and quality control process loop. The architecture for monitor and ensure quality control of the QA policies for e-learning system will deliver the whole learning services in an optimal way. It is also flexible and can be implemented over any e-learning system.

Study on Design and Application of a Manufacturing Process Management System of Large Sized Structure Components in Construction Machinery

2013 ◽  
Vol 313-314 ◽  
pp. 689-692
Author(s):  
Ke Zhou ◽  
Xiao Dong Li
Keyword(s):  
Manufacturing Process ◽  
Management System ◽  
Process Management ◽  
Improve Quality ◽  
Function Structure ◽  
Construction Machinery ◽  
Enterprise Application ◽  
Manufacturing Resources ◽  
Manufacturing Process Management

In order to realize precise management and improve quality of construction machinery, a manufacturing process management system of large sized structure components was designed and applied. Firstly manufacturing process of large sized structure components in construction machinery was analyzed. Secondly manufacturing resources of large sized structure components was analyzed. Thirdly the architecture, function structure and database were designed. Then the application steps of the system were discussed. Feasibility and effectiveness of the system were verified by a case of enterprise application.

Penerapan model kendali mutu data hidrologi dalam rangka peningkatan kualitas data

2018 ◽  
Vol 13 (2) ◽  
pp. 131-146
Author(s):  
Mirwan Rofiq Ginanjar ◽  
Sri Mulat Yuningsih
Keyword(s):  
Quality Control ◽  
Data Analysis ◽  
Water Resources ◽  
Data Quality ◽  
Control Model ◽  
Rainfall Data ◽  
Data Quality Control ◽  
Discharge Data ◽  
Hydrological Data

Planning and management of water resources are dependent on the quality of hydrological data. Hydrological data plays an important role in hydrological analysis. The availability of good and qualified hydrological data is one of the determinants of the results of hydrological analysis. However, the facts indicate that many of the available data do not fit their ideal state. To solve this problem, a hydrological data quality control model should be established in order to improve the quality of national hydrological data. The scope includes quality control of rainfall and discharge data. Analysis of the quality control of rainfall data was conducted on 58 rainfall stations spread on the island of Java. The analysis shows that 41 stations are good categorized, 14 stations are in moderate category and 3 stations are badly categorized. Based on these results, a light improvement scenario was performed, good category Station increased to 46 stations, moderate category decreased to 11 stations and bad category reduced to 1 Stations. Quality control of discharge data analysis was conducted on 14 discharge stations spread on Java Island. Analyzes were performed for QC1, QC2 and QC3 then got final QC value. The results on the final QC show no stations for good category, 2 stations for moderate categories and 12 stations for bad category. Based on the results of the analysis, a light improvement scenario was performed with the result of bad category increased to good category 5 stations, bad category increased to moderate 7 stations, and moderate category 1 stations.

Processing Quality Dynamic Control Based on Quality State

2010 ◽  
Vol 37-38 ◽  
pp. 905-909 ◽  
Author(s):  
Yi Yong Yao ◽  
Li Ping Zhao ◽  
Yong Tao Qin
Keyword(s):  
Quality Control ◽  
Correlation Analysis ◽  
Control Strategies ◽  
Dynamic Control ◽  
Control Model ◽  
Quality Data ◽  
Formal Description ◽  
Identification Methods ◽  
The Stability ◽  
And Control

Depending on the identification methods of quality state such as correlation analysis, a quality identification and control model based on quality state was established in this paper. This model included formal description methods, identification methods, and quality control strategies. Based on the product quality data correlation analysis, the model’s application procedures were put forward. It provides an effective approach to maintaining ideal quality state, to improving the stability and controllability of quality control system. It constructs an application foundation for quality dynamic control.

scholarly journals Pengendalian Kualitas untuk Meminimalkan Jumlah Cacat pada Produk Kaleng Aeorosol

2012 ◽  
Vol 3 (1) ◽  
pp. 584
Author(s):  
Rida Zuraida ◽  
Bima Rantautama ◽  
Notri Sutrisnohadi ◽  
Chondro Dewo Adi Pratomo
Keyword(s):  
Quality Control ◽  
Data Processing ◽  
Manufacturing Process ◽  
Process Capability ◽  
Statistical Quality Control ◽  
Welding Wire ◽  
Effect Analysis ◽  
Aerosol Cans ◽  
Fmea Method

2.64% is the largest defect percentage of aerosol can product of PT Multi Makmur Indah Industri. To improve the product quality, the study focused on the goal of reducing the percentage of defects using the Statistical Quality Control. After gaining control of the company, we can calculate the process capability in the aerosol can manufacture. The next step is identification of the defects causes that arise using the failure mode and effect analysis (FMEA) method to measure the defect causes risks and as an input in determining control recommendations. From the observations and data processing, it is recognized that the overall phase of the process has a process capability value above 99%. 80% of defects in the aerosol cans product occur during the process of can making, component making and printing. While the most risking defect cause is the destruction ofmachine B coating on the printing process (RPN = 245) and the quality of the welding wire on can manufacturing process (RPN = 160). Therefore, the solution to reduce the defect percentage is replacing the damaged coating machine B as well as upgrading the quality of the welding wire. Thus, it can reduce the losssale as much as 1.06% (Rp110,716,000) per month.

scholarly journals PROCESS CAPABILITY IMPROVEMENT BY PUTTING ‘STATISTICAL PROCESS CONTROL’ INTO PRACTICE

2013 ◽  
pp. 251-256
Author(s):  
PIYUSH KUMAR SONI ◽  
IMTIYAZ KHAN ◽  
ABHISHEK ROHILLA
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Product Quality ◽  
Statistical Process Control ◽  
Manufacturing Process ◽  
Process Capability ◽  
Statistical Process ◽  
Quality Of Products ◽  
Capability Improvement

Quality control helps industries in improvement of its product quality and productivity. Statistical Process Control (SPC) is one of the tools to control the quality of products that practice in bringing a manufacturing process under control. In this paper, the process control of a CNC Grinder manufactured at PMT Machines Ltd. Halol, (Gujarat) India is discussed. The varying measurements have been recorded for a number of samples of a Cam Roller Shoe obtained from a number of trials with the CNC Grinder. SPC technique has been adopted, by which the process is finally brought under control and process capability is improved.

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