Building a Graphical User Interface for Concrete Production Processes: A Combined Application of Statistical Process Control and Design of Experiment

2018 ◽  
Vol 44 (5) ◽  
pp. 4373-4393
Author(s):  
Barış Şimşek ◽  
Fatma Pakdil ◽  
Yusuf Tansel İç ◽  
Ali Bilge Güvenç
2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Shahryar Sorooshian

Process control tools are a widely used approach in many operations and production processes. Process control chart ranks as one of the most important theories used in these disciplines. This paper reviewed the bias of quality characteristics monitoring. Specifically, this study tries to provide a comprehensive understanding of theories of process control. The text starts with a theoretical review of statistical process control theories and follows by a technical introduction to developed tools for process control.


2020 ◽  
Vol 2 (5) ◽  
Author(s):  
Jonathan Simon Greipel ◽  
Gina Nottenkämper ◽  
Robert Heinrich Schmitt

Abstract In this study, we present and compare four grouping algorithms to combine samples from low volume production processes. This increases their sample sizes and enables the application of Statistical Process Control (SPC) to low volume production processes. To develop the grouping algorithms, we define different grouping criteria and a general grouping process. To identify which algorithm is optimal, we deduct following requirements on the algorithms from real production datasets: their ability to handle different amount of characteristics and sample sizes within each characteristic as well as being able to separate characteristics possessing distributions with different spreads and locations. To check the fulfillment of these requirements, we define two performance indices and conduct a full-factorial Design of Experiments. We achieve the performance indices for each algorithm by using simulations with artificial data incorporating the aforementioned requirements. One index rates the achieved group sizes and the other one the compactness within groups and the separation between groups. To validate the applicability of grouping algorithms within SPC, we apply real production data to the grouping algorithms and control charts. The result of this analysis shows that the grouping algorithm based on cluster analysis and splitting exceeds the other algorithms. In conclusion, the grouping algorithms enable the application of SPC to small sample sizes. This provides companies, which produce in low volumes, with new means of reducing scrap, generating process knowledge and increasing quality.


Author(s):  
Pappu Rama Subramaniam ◽  
Pappu Rama Subramaniam

Traditional quality management and monitoring has been shown to be unsuccessful. Today, emerging companies are vying for more value to the consumer in order to ensure their maximum success and sustainability. Many businesses want to ensure that their goods and services are of high quality in order to attract customers. The current situation is implementing quality engineering solutions in industries. Quality engineering is the method of evaluating, handling, designing, and maintaining various systems in compliance with high standards. This method ensures that each stage of the product development cycle is subjected to a thorough inspection by quality engineers, reducing possible losses by eliminating defects from the start. Furthermore, highquality maintenance is important and should be made available for a long time after the product has been shipped. Customers' preferences are shifting significantly, necessitating improvements in design and production technology, which is becoming increasingly critical in satisfying individual customers. This necessitates paying particular attention to quality engineering. The paper starts with a review on quality emphasis over the last 37 years, quality concepts, and quality model evolution followed by i) a contrast of quality management and quality engineering, ii) developments in quality engineering tools and techniques, such as statistical process control (SPC), design of experiment (DoE), Taguchi processes, and quality function (QFD). This paper also looks at quality engineering-related problems. There are brief reviews of recent developments in well-known quality tools, such as statistical process control, quality function deployment, and design of experiment. The aim of this paper is to place quality engineering in context and emphasise its significance, as well as to present some issues at the frontiers of quality engineering.


2021 ◽  
Vol 8 (2) ◽  
pp. 1425-1432
Author(s):  
Rafael Eloy de Souza ◽  
Alfredo José dos Santos Junior ◽  
Alan Henrique Marques de Abreu ◽  
Natália Dias de Souza ◽  
Ananias Francisco Dias Júnior

The control of production processes can assist in the standardization of variability, reducing waste, and improving the quality of a service or product. Thus, this study aimed to analyze the non-conformities in a production system of forest seedlings from Atlantic Forest aiming at the standardization of the production system and adjustments for field cultivation. The definition of the attributes was made through a technical visit to the forestry nursery to know the location and the production process of the seedlings. For the process evaluation, statistical process control tools were used. The non-conformities analyzed were: coiled root growth, disintegrated substrates of plants, presence of roots fixed to the ground, presence of phytopathogen attack symptoms and/or herbivory and symptoms of nutritional deficiency. In general, variability was detected in the production process, compromising the success in planting the seedlings in the field, as well as their quality.


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