Failure Mode and Effects Analysis of PCB for Quality Control Process

Abstract

The aim of this study is to highlight the key elements for optimizing printed circuit board (PCB) fabrication productivity through improving manufacturing process efficiency. Failure mode and effect analysis (FMEA) is a technique used to reduce the percentage of finished goods that are found to be defective during the manufacturing process and final inspection, resulting in low rejection ratios and optimized PCB design. This paper presents all the quality steps to achieve high efficiency in PCB design. The study is done in electronics manufacturing industry in which its production begins from receiving PCBs, raw material then bringing them into punching and assembly processes through surface mount machines (SMT). To find defective items and lower the possibility of defective final products, or IPQC (in-process quality control), is used. The average of customer manufacturers lot reject rate (%LRR of CMs) has been improved by using improved quality control. For assessing the risk connected to probable issues discovered during a failure mode and effects analysis, the risk priority number (RPN) methodology technique is applied. The FMEA RPN assists the responsible team or individual in prioritizing risks and choosing the appropriate remedial measures. Lot reject rate (LRR) improved by FMEA is from 5500 parts per million (PPM) to 900 parts per million (PPM), and faults have decreased by 0.76% as a result of improved quality control.

Utilising Failure Mode and Effect Analysis and the Kano Model for Induction Stove Performance Improvement During Indonesia’s Energy Conversion Program

“…There is a direct relationship between effect and Severity. For example, if the impact is critical, the Severity value is high and vice versa (Gupta, 2023). Furthermore, Occurrence is the adjusted assessment value with an estimate of the frequency and cumulative number of possible failures (Gupta, 2023;Wang et al, 2022) and detection is a measure of the ability to monitor or control potential losses (Gupta, 2023).…”

“…For example, if the impact is critical, the Severity value is high and vice versa (Gupta, 2023). Furthermore, Occurrence is the adjusted assessment value with an estimate of the frequency and cumulative number of possible failures (Gupta, 2023;Wang et al, 2022) and detection is a measure of the ability to monitor or control potential losses (Gupta, 2023). These three criteria (S, O and D) are multiplied to determine the RPN of product failures that need to be prioritised (Stamatis, 2003).…”

“…2). (Gupta, 2023;Wang et al, 2022) Furthermore, the detection rank (D) comprises 10 levels, where the more complex the possibility of catching a loss, the higher the detection level (Tab. 3).…”

“…The probability of the cause is relatively very high; Prevention methods do not work Source: (Gupta, 2023)…”

Printed circuit board and printed circuit board assembly methods for testing and visual inspection: a review

“…Nowadays, the manufacturing is "smart" as quality control is often performed through automatic optical inspection (AOI) to verify and validate important production tasks [1], to prevent appearance of defects in electronics production and to notify an operator [2], even for identification of micro-size defects on PCB [3]. Statistics approaches are also applied such as failure mode and effects analysis for quality control improvement [4] and statistical process control for monitoring the quality of the manufacturing process and for obtaining quality of the end product [5]. Artificial intelligence is increasingly entering for supporting conductance of different tasks such as optimization of the components position [6], as well as machine learning for detecting patterns or anomalies on PCB [7] and deep learning for defects classification in PCB production [8].…”