Product Infant Failure Research Articles

Development of a TQM-based Framework for Product Infant Failure Assessment

Product infant failures have been traced to the product development and production stage. Researchers and practitioners have opined that total quality management (TQM) can be used to properly managed these failures. While their suggestions have helped in this regard, there is limited information on how to scientifically aggregate criteria that can be used to specifically identify the most suitable TQM technique for product infant failure improvement, especially at the development stage. Hence, this study proposes a fuzzy-based multi-criteria framework for this problem. The framework uses intuitionistic fuzzy set to handle vague and imprecise judgment and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) to rank selected TQM techniques. Real-world data sets were used to evaluate the framework performance, while TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) was used to validate the framework performance. Based on the results obtained, the IFWG-VIKOR and TOPSIS methods rank the most and least suitable TQM practices as TM4 and TM11, respectively. The framework can be used to provides insights into the management of techniques that can address infant product failure issues at the early stage of product development. Â

Modeling and detecting approach for the change point of electronic product infant failure rate

Infant failures are the natural enemy of customer satisfaction of electronic product. The change point is a precondition to optimize the burn-in time, warranty period, and root causes in production reliability assurance. However, the method for modeling and detecting the change point is unavailable due to the deficient research on the mechanism of infant failure, thereby hindering the continuous optimization of reliability in this field. Therefore, a quantitative method for modeling and detecting the change point of infant failure rate by using the Weibull distribution is proposed in this paper. Firstly, the mechanism of infant failure and the connotation of the change point of infant failure rate are explained. Then a statistic index is proposed to quantitatively model the trend of change point with the non-constant Weibull shape parameter. Also, the proposed statistic index is adopted to detect the trend of change point by CUSUM chart. Finally, an analysis example of a control board is presented to verify the effectiveness of the proposed approach in detecting the changing trend of infant failure rate for electronic products.

Big Data-Oriented Product Infant Failure Intelligent Root Cause Identification Using Associated Tree and Fuzzy DEA

Infant failure analyzing is an effective approach to improve production quality continuously. The root causes of infant failure have always been a puzzle to manufacturers. To satisfy the increasing demand for the fuzzy root cause analysis of product infant failure in the era of big data, a novel root cause identification approach based on the associated tree and fuzzy data envelopment analysis (DEA) is presented for product infant failure. First, to decrease fuzziness with regard to the mechanism of infant failure, the associated tree is adapted to guide the analysis process for possible root causes based on axiomatic domain mapping. Second, considering the fuzzy mechanism and massive data, the fuzzy DEA technique is adopted to cluster all the potential factors of functional parameters, physical parameters, and process parameters from big data regarding product life cycle. Third, the ranking method of decision-making unit efficiency in fuzzy DEA is used to model and rank the weight of each node in the established associated tree of infant failure. Finally, a case study of root cause identification for a typical infant failure of the vibration and noise of a washing machine is presented to demonstrate the feasibility and validity of the proposed method.

Product infant failure risk modeling based on quality variation propagation and functional failure dependency

Product infant failure is the most crucial part of product quality risk that critically affects customer satisfaction. However, most of manufacturers lack sufficient understanding of the formation mechanism of product infant failure and quantitative infant failure risk modeling technology. These issues cause the inefficiency of warranty policy in preventing and controlling infant failure risk. Therefore, first, on the basis of the reverse mapping process of axiomatic design, an infant failure risk formation chain, that is, “process quality variation—physical defect—functional vulnerability—infant failure,” is proposed in this study by considering quality variation propagation and functional failure dependency to determine the inherent formation and dependent enlarging process of the risk. Second, on the basis of the risk formation chain, the infant failure risk is modeled from inherent risk and dependent risk. Specifically, the inherent infant failure risk is computed based on the stream of quality variation in production, whereas the dependent infant failure risk is computed by a Bayesian network by considering the functional failure dependency. Finally, a case study of an illustrative electromechanical system is introduced to verify the applicability of the proposed method. Result shows that the proposed method has a better performance in infant failure risk modeling.

Big data oriented root cause identification approach based on Axiomatic domain mapping and weighted association rule mining for product infant failure

Product infant failure formation mechanism is a maze that remains unclear to most manufacturers. Root cause analysis is an important and challenging task in exploring this mechanism in the era of quality and big data. Therefore, a novel big data oriented root cause identification approach based on weighted association rule mining (WARM) is proposed in this paper. First, the mechanism is expounded based on big data in the product lifecycle, and the requirements of root cause identification are determined simultaneously. Second, in view of domain mapping theory in Axiomatic design, the associated tree is proposed to provide a framework for the root cause search and identification. Then, the big data of root causes is defined based on the proposed associated tree. Third, a root cause mining technique using the WARM is presented, and the weight computation approach for the node on the associated tree based on the weight confidence is provided. Finally, the validity of the proposed method is verified by a case study on mining root causes for severe infant failure of an automatic washing machine. The final result shows that the proposed approach is conducive to heuristically identify the root causes of the complicated product infant failure from the big data of product lifecycle.

Modelling infant failure rate of electromechanical products with multilayered quality variations from manufacturing process

The optimisation of product infant failure rate is the most important and difficult task for continuous improvement in manufacturing; how to model the infant failure rate promptly and accurately of the complex electromechanical product in manufacturing is always a dilemma for manufacturers. Traditional methods of reliability analysis for the produced product usually rely on limited test data or field failures, the valuable information of quality variations from the manufacturing process has not been fully utilised. In this paper, a multilayered model structured by ‘part-level, component-level, system-level’ is presented to model the reliability in the form of infant failure rate by quantifying holistic quality variations from manufacturing process for electromechanical products. The mechanism through which the multilayered quality variations affect the infant failure rate is modelled analytically with a positive correlation structure. Furthermore, an integrated failure rate index is derived to model the reliability of electromechanical product in manufacturing by synthetically incorporating overall quality variations with Weibull distribution. A case study on a control board suffering from infant failures in batch production is performed. Results show that the proposed approach could be effective in assessing the infant failure rate and in diagnosing the effectiveness of quality control in manufacturing.

A fuzzy TOPSIS and Rough Set based approach for mechanism analysis of product infant failure

Root causes identification of product infant failure is nowadays one of the critical topics in product quality improvements. This paper puts forward a novel technical approach for mechanism analysis of product infant failure based on domain mapping in Axiomatic Design and the quality and reliability data from product lifecycle in the form of relational tree. The proposed method could intelligently decompose the early fault symptoms into root causes of critical functional parameters in function domain, design parameters in physical domain and process parameters in process domain successively. More specifically, both qualitative and quantitative attributes of quality and reliability types are considered for solving the root causes weight computation problem of product infant failure, this approach emphasizes the integrated application of artificial intelligence techniques of Rough Set and fuzzy TOPSIS to compute the weight of root causes. In order to enumerate the latent root causes of product infant failure, connotation of product infant failure based on the product reliability evolution model in the life cycle and data integration model of quality and reliability in production based on the extended QR chain are presented firstly. Then, a decomposition method for relational tree of product infant failure is studied based on domains of functional, physical and process in Axiomatic Design. The failure relation weight computation of root causes (nodes of relational tree) is considered as multi-criteria decision making problem (MCDM) by integrated application of Rough Set and fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), which the Rough Set is used to mining the quality data and fuzzy TOPSIS is adopted to model the computation process of failure relation weight. Finally, the validity of the proposed method is verified by a case study of analyzing a car infant failure about body noise vibration harshness complaint, and the result proves that the proposed approach is conducive to improve the intelligent level of root causes identification for complex product infant failure.