Open AccessUncertainty Measurement and Analysis for Quality and Reliability in Manufacturing Process
Author(s) -
Altaf Ahmed,
Guozhu Jia,
Arfan Majeed,
Abdus Salam
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/576/1/012007
Subject(s) - reliability (semiconductor) , reliability engineering , process (computing) , measurement uncertainty , quality (philosophy) , key (lock) , uncertainty analysis , sensitivity (control systems) , computer science , manufacturing process , engineering , mathematics , statistics , materials science , simulation , power (physics) , physics , philosophy , computer security , epistemology , quantum mechanics , electronic engineering , composite material , operating system
The variation and uncertainty existed in every process and considered as an essential element in precise, accurate and high-quality manufacturing, especially in the Industry 4.0 era. Manufacturing quality and reliability is affected by variations and uncertainties in different process parameters and influencing factors. The variations and uncertainties are considered very critical in measurement and monitoring process, i.e., prediction or prognostics. These variations and subsequent uncertainties are needed to be identified, quantified, analyzed and controlled. This paper presents an approach to uncertainty measurement and analysis in manufacturing. The key characteristic parameter is identified which is critical for final product quality and reliability. To determine the uncertainty in the specific parameter, the uncertainty factors of both the manufacturing process and measurement process are determined. The combined uncertainty is determined by considering the influencing factors both in manufacturing and measurement process. The relation of each factor to the key parameter is analyzed and the sensitivity coefficient is determined mathematically or experimentally. The combined and expanded uncertainty is determined considering all related factors. The proposed approach has been applied to an additive manufacturing technique, and useful results are achieved.