Variation in flowmeter manufacture: Some observations and lessons

Abstract

This paper discusses two concepts essential to high-quality manufacture, in particular, of flowmeters. The first is the understanding, control and reduction of variation so that high quality in the product can be achieved. The second is consideration of the need for good housekeeping and how this is an essential for documentation of the meter's manufacture to allow the product to be verified through its working life and to be withdrawn if it fails to meet its specification. The theoretical basis is reviewed and examples are then given from a particular industrial groupflowmeter manufacturers-for evidence of the effects of manufacturing variation, its control and the resultant high quality that can be achieved. This is done by reviewing relevant theoretical and experimental work on various flowmeters so as to understand the causes of variation. Lessons can be learned from this relating to design for production, manufacturing, alternatives to calibration and the importance of software. This is illustrated by a simplified case study of one type of flowmeter, the electromagnetic flowmeter. This leads to conclusions on the possible lessons for companies on design, variation/quality and measuring on line, verification and customer communications.

Experimental Determination of the Standard Enthalpy of Formation of Trimellitic Acid and Its Prediction by Supervised Learning

“…This allowed us to obtain well-defined fusion peaks (Figure S3 in the Supporting Information); thus, the fusion enthalpy could be obtained, as revealed by the compound high purity. However, the melting temperature was shifted to a higher value, T fus = (521.8 ± 1.2) K, compared to those reported in the literature. − Probably, a high pressure inside the capsule was generated, which provoked the melting temperature shift. On other hand, the experiments in SDT show that TMAc starts sublimating approximately at 485.15 K, and melted at 507.0 K, consequently immediately vaporizes.…”

Bayesian Information for Sensors

“…For example, the output of a pressure sensor in a pipeline may change undesirably under a varying environmental temperature, even if the pipeline pressure does not change. For example, Baker discussed the importance of understanding, controlling, and reducing the variability for high‐quality measuring devices. He noted that slight changes in manufacturing will require calibration of each instrument and suggested that reducing variation and ensuring good housekeeping can lead to high‐quality products.…”