Nadia Belu

Application of Fuzzy Logic in Design Failure Mode and Effects Analysis

Failure Mode and Effects Analysis (FMEA) is one of the basic and the most used techniques of quality management that is used for continuous improvements in product or process designs. While applying this technique, determining the Risk Priority Numbers (RPN), which indicate the levels of risks associated with potential problems, is of prime importance for the success of application. A traditional RPN is obtained as product of three risk factors: occurrence, severity and detection. Values of these factors are generally attained from past experience and this way of risk assessment sometimes leads to inconsistencies and inaccuracies during priority numbering. Fuzzy logic approach is considered a promising solution in order to give a more accurate ranking of potential risks. This paper presented a fuzzy model, in order to assess and rank risks associated to failure modes that could appear in the functioning of a headlining product used in automotive industry.

Comparative Analysis of Awareness and Knowledge of APQP Requirements in Polish and Romanian Automotive Industry

In the present financial and economic context, the automotive industry faces new challenges posed by the current crisis. Companies have to ensure that the quality products are delivered on time and in a competitive price. One of the most recommended techniques of quality management by specific standards of the automotive industry for product development is Advanced Product Quality Planning (APQP). Product Quality Planning is a structured method of defining and establishing the steps which are necessary to ensure the customer satisfaction from the product. The goal of this paper is to present the results of evaluation of awareness and knowledge the APQP techniques in Polish and Romanian automotive industry. The research method was a questionnaire with indicators to measure the awareness APQP tools. The research was conducted among design and technology professionals in Polish and Romanian automotive companies. On the basis of questionnaire it was performed a comparative analysis of results from Poland and Romania. In this way it is shown which methods and tools of APQP are better known and understood in each country. It was pointed out which tools are difficult to use or there is lack of knowledge about them. It was also shown which methods are most often used in the Polish and Romanian automotive companies. They were also summarized the similarities and differences in the knowledge and using the APQP in both countries.

Improvement of Process Failure Mode and Effects Analysis using Fuzzy Logic

Risk analysis increased in importance within environmental, health and safety regulation last few years. Process Failure Mode and Effects Analysis (PFMEA) is one of the most used techniques to evaluate a process for strengths, weaknesses, potential problem areas or failure modes, and to prevent problems before they occur. The traditional PFMEA determines the risk priorities of failure modes using the risk priority numbers (RPNs) by multiplying the scores of the risk factors like the occurrence (O), severity (S) and detection (D) of each failure mode. The method has been criticized to have several shortcomings. Fuzzy logic approach is preferable in order to remove the deficiencies in assigning the risk priority numbers. In this study, a fuzzy-based FMEA is to be applied to improve the manufacturing process of rear bumper, injection part used in automotive industry. The fuzzy model PFMEA can provide the stability of process assurance.

Failure Mode and Effects Analysis on Control Equipment Using Fuzzy Theory

Failure Mode and Effects Analysis is a methodology to evaluate a system, design, process, machine or service for possible ways in which failures (problems, errors, risks and concerns) can occur and it has been used in a wide range of industries. Traditional method uses a Risk Priority Number to evaluate the risk level of a component or process. This is obtained by finding the multiplication of three factors, which are the severity of the failure (S), the probability/occurrence of the failure (O), and the probability of not detecting the failure (D). There are significant efforts which have been made in FMEA literature to overcome the shortcomings of the crisp RPN calculation. Fuzzy logic appears to be a powerful tool for performing a criticality analysis on a system design and prioritizing failure identified in analisys FMEA for corrective actions. In this paper we present a parallel between the typical and the fuzzy computation of RPNs, in order to assess and rank risks associated to failure modes that could appear in the functioning of control equipment.

An improved method for risk evaluation in failure modes and effects analysis of CNC lathe

Failure mode and effects analysis (FMEA) is one of the most popular reliability analysis tools for identifying, assessing and eliminating potential failure modes in a wide range of industries. In general, failure modes in FMEA are evaluated and ranked through the risk priority number (RPN), which is obtained by the multiplication of crisp values of the risk factors, such as the occurrence (O), severity (S), and detection (D) of each failure mode. However, the crisp RPN method has been criticized to have several deficiencies. In this paper, linguistic variables, expressed in Gaussian, trapezoidal or triangular fuzzy numbers, are used to assess the ratings and weights for the risk factors S, O and D. A new risk assessment system based on the fuzzy set theory and fuzzy rule base theory is to be applied to assess and rank risks associated to failure modes that could appear in the functioning of Turn 55 Lathe CNC. Two case studies have been shown to demonstrate the methodology thus developed. It is illustrated a parallel between the results obtained by the traditional method and fuzzy logic for determining the RPNs. The results show that the proposed approach can reduce duplicated RPN numbers and get a more accurate, reasonable risk assessment. As a result, the stability of product and process can be assured.

Monitoring of the Human Resources Process: Examples in Automotive Industry

The article is dealing with two problems regards the quality management in automotive industry such as: human resources management and processes monitoring. This connection is really difficult because of entrepreneurs generally have a problem with processes monitoring. Additionally human resources management is unmeasurable, so measurement must often be done using qualitative criteria, rather than quantitative. Furthermore the regulations of the new ISO 9001 include evaluate the performance of processes. It emphasizes the importance of the problem. The study provides examples of ways to monitor the process of human resource management in small and medium-sized enterprises automotive. Examples were evaluated for suitability to assess the effectiveness of the process and to generate guidelines for improvement. The form of keeping and visualization in order to use the information for periodic reviews of the management was discussed.

Contributions to Ranking an Ergonomic Workstation, Considering the Human Effort and the Microclimate Parameters, Using Neural Networks

The paper presents a method to use a feed forward neural network in order to rank a working place from the manufacture industry. Neural networks excel in gathering difficult non-linear relationships between the inputs and outputs of a system. The neural network is simulated with a simple simulator: SSNN. In this paper, we considered as relevant for a work place ranking, 6 input parameters: temperature, humidity, noise, luminosity, load and frequency. The neural network designed for the study presented in this paper has 6 input neurons, 13 neurons in the hidden layer and 1 neuron in the output layer. We present also some experimental results obtained through simulations.

A Matlab Neural Network Application for the Study of Working Conditions

The paper presents a method based on the neural networks to study of working conditions, for the workstations from the manufacture industry. The neural networks were chosen because they excel in gathering difficult non-linear relationships between the inputs and outputs of a system. The neural network was simulated with Matlab. In this paper, we considered as relevant for the study of working conditions, 6 input parameters: temperature, humidity, noise, luminosity, load and frequency. The neural network designed for the study presented in this paper has 6 input neurons and 3 neurons in the output layer. Some experimental results obtained through simulations, are presented in the final part of the paper.

Improvement of the customer satisfaction through Quality Assurance Matrix and QC-Story methods: A case study from automotive industry

Presently, in the automotive industry, the tendency is to adapt permanently to the changes and introduce the market tendency in the new products that leads of the customer satisfaction. Many quality techniques were adopted in this field to continuous improvement of product and process quality and advantages were also gained. The present paper has focused on possibilities that offers the use of Quality Assurance Matrix (QAM) and Quality Control Story (QC Story) to provide largest protection against nonconformities in the production process, throughout a case study in the automotive industry. There is a direct relationship from the QAM to a QC Story analysis. The failures identified using QAM are treated with QC Story methodology. Using this methods, will help to decrease the PPM values and will increase the quality performance and the customer satisfaction.

Model of areas for identifying risks influencing the compliance of technological processes and products

Operation of every company is associated with the risk of interfering with proper performance of its fundamental processes. This risk is associated with various internal areas of the company, as well as the environment in which it operates. From the point of view of ensuring compliance of the course of specific technological processes and, consequently, product conformity with requirements, it is important to identify these threats and eliminate or reduce the risk of their occurrence. The purpose of this article is to present a model of areas of identifying risk affecting the compliance of processes and products, which is based on multiregional targeted monitoring of typical places of interference and risk management methods. The model is based on the verification of risk analyses carried out in small and medium-sized manufacturing companies in various industries..