Automotive leaf spring design and manufacturing process improvement using failure mode and effects analysis (FMEA)

Nowadays human safety and comfort are the most considerable parameters in designing and manufacturing of a vehicle, that is why every organization ensures the quality and reliability of components used in the vehicle. Leaf spring is also a component of vehicle which plays an important role in human safety and comfort. It acts as a structural member and an integral part of suspension system. It is important to eliminate the failures in designing and manufacturing process of leaf springs because of its importance in functionality and safety of vehicle. In this research, failure mode and effects analysis has been used to analyze and reduce the risks of 42 possible failures that can occur in automotive leaf spring. It starts from determining, classifying, and analyzing all potential failures and then rating them with the help numeric scores. The four numeric scores namely severity, occurrence, detection, and Risk Priority Number (RPN) are used to find the high potential failures of semi-elliptical leaf springs. In the end, actions are recommended for RPN greater than 250, to increase quality and reliably of product. </jats:p

Algorithms for Data Cleaning in Knowledge Bases

Data cleaning is an action which includes a process of correcting and identifying the inconsistencies and errors in data warehouse. Different terms are uses in these papers like data cleaning also called data scrubbing. Using data scrubbing to get high quality data and this is one the data ETL (extraction transformation and loading tools). Now a day there is a need of authentic information for better decision-making. So we conduct a review paper in which six papers are reviewed related to data cleaning. Relating papers discussed different algorithms, methods, problems, their solutions, and approaches etc. Each paper has their own methods to solve a problem in an efficient way, but all the paper have a common problem of data cleaning and inconsistencies. In these papers data inconsistencies, identification of the errors, conflicting, duplicate records etc problems are discussed in detail and also provided the solutions. These algorithms increase the quality of data. At ETL process stage, there are almost thirty-five different sources and causes of poor quality constraints