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Machine Learning Models in Product Development and Its Statistical Evaluation
Published in P. Kaliraj, T. Devi, Artificial Intelligence Theory, Models, and Applications, 2021
Quality control involves a collection of procedures that are implemented to ensure the quality of the manufactured product or performed service adheres to a defined set of quality criteria to meet the customer’s requirements. This method is based on the statistical techniques to determine and control the quality through sampling. Random sampling, probability, and statistical inferences are used to develop the process, using this method to control the product’s quality. There are two types of quality control tools, which include process control techniques and product control techniques. Process control techniques control the product development process through every stage of production. In contrast, the product control technique involves checking their units and determining their lot if they are within their specifications towards the final product before going to market. Here both the producer and consumer can determine their quality checking.
Statistical Process Control
Published in Roger W. Berger, Thomas Hart, Statistical Process Control, 2020
In contrast to the traditional approach to quality control, statistical process control involves the integration of quality control into each step in the production process. Standards arc established for each step and an acceptable range about each standard is determined. As long as the procedure for each step yields a product within its set range, quality is ensured. When the acceptable range is exceeded, or a trend is identified which indicates the range will soon be exceeded, the step is stopped and adjusted to bring it back in line with the standard. It should be mentioned that the statistical methods also help prevent over adjustment by indicating when the process should be left alone. In this manner quality is ensured at each step in the process and rejects are not passed along for further processing.
Precast segmental bridge construction in seismic zones
Published in Fabio Biondini, Dan M. Frangopol, Bridge Maintenance, Safety, Management, Resilience and Sustainability, 2012
Fabio Biondini, Dan M. Frangopol
In this section, statistical process control and how it can be applied to damage alarming for expansion joints is discussed. Statistical process control is a tool of statistical quality control to detect if the process is out of control. It plots the quality characteristic as a function of the sample number. The chart has lower and upper control limits, which are computed from those samples only when the process is assumed to be in control. When unusual sources of variability are present, sample statistics will plot outside the control limits. In that occasion an alarm is triggered. There exist different control charts, differing on their plotted statistics. Several univariate and multivariate control charts for damage detection were studied in the past (Fugate et al. 2001, Kullaa 2003, Deraemaeker et al. 2008).
A data-driven scheduling knowledge management method for smart shop floor
Published in International Journal of Computer Integrated Manufacturing, 2022
Yumin Ma, Shengyi Li, Fei Qiao, Xiaoyu Lu, Juan Liu
Quality control is a method to ensure that the production process meets the product quality requirements. A commonly used technique is statistical process control (SPC) (Turner, Mike, and Case 1978). The control chart is an effective method in SPC. It monitors whether the production process is under control by measuring, recording, and evaluating the key quality characteristic values in the production process (Gasparin et al. 2013). The shop floor dynamic scheduling process can also be regarded as the processing process. The scheduling knowledge corresponds to the machining system, the production performance of the shop floor can correspond to the actual size of the product, the expected production performance of the shop floor corresponds to the nominal size of the product. Therefore the shop floor production performance is used as the key quality characteristic value to monitor the scheduling process. However, the shop floor production performance is not a constant value, so it is difficult to monitor whether the scheduling result is satisfactory in real-time. The paper defines the scheduling satisfaction degree (as shown in Eq.4) to solve this problem, representing the degree of satisfaction with the scheduling results.
Integrating risk and performance management in quality management systems for the development of complex bespoke systems (CBSs)
Published in Production Planning & Control, 2018
Quality management practices for integrating risk and performance in CBSs fall into two mentalities: quality control and development. Quality control refers to management efforts in establishing standardized processes and/or product characteristics, against which any changes are measured, tolerated and corrected (Liu 2015). Quality development takes place where the way of integrating technology and configuring systems is novel or the application of solutions is too complex to allow measurable common quality standards to apply, as in mass-production processes. Quality development does not necessarily rely on structural models and strategies, but suppliers and customers need to develop processes, conceive relationship strategies and invent tools to tackle unpredicted changes and to agree conformance criteria with each other on how the system is operating and how the performance is measured (Bunduchi 2013; Dey 2012; Ehlers 2007; Rapaccini and Visintin 2015).
Production and maintenance strategy for a high-reliability imperfect process with free-repair warranty
Published in International Journal of Systems Science: Operations & Logistics, 2018
In numerous industries, quality control is a process that is employed to ensure that a product is free from defects and operational problems. In auto manufacturing quality control, cars are subjected to rigorous testing to ensure they are well engineered, safe and conform to specifications. After a period of run time, some conforming cars will incur postsale servicing costs when sold under FRW. These conforming cars exhibit inferior performance characteristics. This study defined an inferior item as a conforming product that satisfies specifications and is usable but exhibits considerably inferior performance characteristics. For example, an inferior item may have a shorter mean time between failures compared with those of other items. An increasing proportion of products may become inferior as the production system deteriorates, generating a high postsale cost when the products are sold under warranty. Defective goods produced in the out-of-control state may be partially reworked to satisfy their specifications, but without improving their performance characteristics. The reworked items may incur a higher postsale servicing cost than do defect-free items when sold under FRW.