Parveen S. Goel

A fuzzy logic based approach to reliability improvement estimation during product development

Abstract During early stages of product development process, a vast amount of knowledge and information is generated. However, most of it is subjective (imprecise) in nature and remains unutilized. This paper presents a formal structure for capturing this information and knowledge and utilizing it in reliability improvement estimation. The information is extracted as improvement indices from various design tools, experiments, and design review records and treated as fuzzy numbers or linguistic variables. Fuzzy reasoning method is used to combine and quantify the subjective information to map their impact on product reliability. The crisp output of the fuzzy reasoning process is treated as new evidence and incorporated into a Bayesian framework to update the reliability estimates. A case example is presented to demonstrate the proposed approach.

Reliability demonstration test planning: A three dimensional consideration

Increasing customer demand for reliability, fierce market competition on time-to-market and cost, and highly reliable products are making reliability testing more challenging task. This paper presents a systematic approach for identifying critical elements (subsystems and components) of the system and deciding the types of test to be performed to demonstrate reliability. It decomposes the system into three dimensions, (i.e. physical, functional and time) and identifies critical elements in the design by allocating system level reliability to each candidate. The decomposition of system level reliability is achieved by using criticality index. The numerical value of criticality index for each candidate is derived based on the information available from failure mode and effects analysis (FMEA) document or warranty data from a prior system. It makes use of this information to develop reliability demonstration test plan for the identified (critical) failure mechanisms and physical elements. It also highlights the benefits of using prior information in order to locate critical spots in the design and in subsequent development of test plans. A case example is presented to demonstrate the proposed approach.

A Framework for Reliability Prediction During Product Development Process Incorporating Engineering Judgments

This paper presents a comprehensive framework for reliability prediction during the product development process. Early in the product development process, there is typically little or no quantitative evidence to predict the reliability of the new concept except indirect or qualitative information. The proposed framework addresses the issue of utilizing qualitative information in the reliability analysis. The framework is based on the Bayesian approach. The fuzzy logic theory is used to enhance the capability of the Bayesian approach to deal with qualitative information. This paper proposes to extract the information from various design tools and design review records and incorporate it into the Bayesian framework through a fuzzy inference system. The Weibull distribution is considered as failure/survival time distribution with the assumption of a known value of shape factor. Initial parameters of the Weibull distribution are estimated from warranty data of prior systems to estimate the initial Bayesian parameter ( λ t ). The applicability of the framework is illustrated via an example.

Insights and learnings from lean manufacturing implementation practices

Lean manufacturing as a set of principles is now fairly rooted in the literature. Barring some voices of discontent (Gordon, 1995; Berggren, 1992) regarding the adoption and ultimate effectiveness of lean production, many case examples exist to demonstrate how companies are changing their production methods and management practices to become more lean and fit. The main aim of this study is to first collect information on fundamental lean principles and then investigate the level of lean implementation in the automotive industry. Furthermore, it describes some learnings from actual implementation practices particularly in the USA, UK and Indian automotive sectors. Attempts are made here to discover the inside stories and present the gaps between the principles and actual practices.

A multi-objective framework for the design of vacuum sealed molding process

Abstract This paper provides a framework for process design to achieve simultaneous improvement of the multiple features of the product. The methodology provides a way to integrate process parameters with the product features. Many of the features may be conflicting in nature, and thus may limit the use of a particular setting of design parameter of the process to produce the quality product. In this paper we discuss how to resolve the conflicting objectives of the process design problem. A case study for the design of vacuum-sealed molding process using the proposed methodology is included.

A new paradigm for durable product design with up-front consideration of lifetime performance

Abstract In this paper, we provide a new paradigm for product design by considering lifetime performance of a product at an early stage of product development. Two new concepts are introduced: time-dependent stepped quality loss function for product life and floating targets for functional characteristics. Both the concepts are used in developing a product life cycle cost function. A mathematical programming model is developed and illustrated by a case study.

Analytical Approach to Meeting Reliability and Confidence Requirements of Automotive Steering Systems

Meeting reliability requirements specified by customers, solely by physical testing can result in serious resource implications. Demonstration of a high reliability at a high statistical confidence level requires a large number of test samples and lengthy test cycles. In this paper we intend to present our reliability management methodology that accounts for both physical testing and non-physical testing activities. In particular, we will focus on how three important product dimensions - namely, function, physical structure, and time-in-service requirements - are systematically examined and tied to validation test planning. The ultimate goal here is intelligent information management.