DeepAI
Log In Sign Up

Statistical Method to Model the Quality Inconsistencies of the Welding Process

02/24/2019
by   Mohammad Aminisharifabad, et al.
0

Resistance Spot Welding (RSW) is an important manufacturing process that attracts increasing attention in automotive industry. However, due to the complexity of the manufacturing process, the corresponding product quality shows significant inconsistencies even under the same process setup. This paper develops a statistical method to capture the inconsistence of welding quality measurements (e.g., nugget width) based on process parameters to efficiently monitor product quality. The proposed method provides engineering efficiency and cost saving benefit through reduction of physical testing required for weldability and verification. The developed method is applied to the real-world welding process.

READ FULL TEXT
10/08/2019

An XML-based Factory Description Language for Smart Manufacturing Plants in Industry 4.0

Industry 4.0 revolution concerns the digital transformation of manufactu...
05/09/2016

Process Information Model for Sheet Metal Operations

The paper extracts the process parameters from a sheet metal part model ...
05/12/2020

One-Shot Recognition of Manufacturing Defects in Steel Surfaces

Quality control is an essential process in manufacturing to make the pro...
06/20/2009

Automatic Defect Detection and Classification Technique from Image: A Special Case Using Ceramic Tiles

Quality control is an important issue in the ceramic tile industry. On t...
12/12/2002

Data Engineering for the Analysis of Semiconductor Manufacturing Data

We have analyzed manufacturing data from several different semiconductor...
01/14/2019

An Approach to Statistical Process Control that is New, Nonparametric, Simple, and Powerful

To maintain the desired quality of a product or service it is necessary ...
06/17/2022

A hybrid deep learning model of process-build interactions in additive manufacturing

Laser powder bed fusion (LPBF) is a technique of additive manufacturing ...