Search results

The aim of this paper is to review the literature on the prediction of cutting tool life. Tool life is typically estimated by predicting the time to reach the threshold flank wear width. The cutting tool is a crucial component in any machining process, and its failure affects the manufacturing process adversely. The prediction of cutting tool life by considering several factors that affect tool life is crucial to managing quality, cost, availability and waste in machining processes.

This study has undertaken the critical analysis and summarisation of various techniques used in the literature for predicting the life or remaining useful life (RUL) of the cutting tool through monitoring the tool wear, primarily flank wear. The experimental setups that comprise diversified machining processes, including turning, milling, drilling, boring and slotting, are covered in this review.

Cutting tool life is a stochastic variable. Tool failure depends on various factors, including the type and material of the cutting tool, work material, cutting conditions and machine tool. Thus, the life of the cutting tool for a particular experimental setup must be modelled by considering the cutting parameters.

This submission discusses tool life prediction comprehensively, from monitoring tool wear, primarily flank wear, to modelling tool life, and this type of comprehensive review on cutting tool life prediction has not been reported in the literature till now. The future suggestions provided in this review are expected to provide avenues to solve the unexplored challenges in this field.

The purpose of this paper is to discuss the interval forecasting, prediction interval and its reliability. When the predicted interval and its reliability are construction, the general rule which must satisfy is studied, grey wrapping band forecasting method is perfect.

A forecasting method puts forward a process of prediction interval. It also elaborates on the meaning of interval (the probability of the prediction interval including the real value of predicted variable). The general rule is abstracted and summarized by many forecasting cases. The general rule is discussed by axiomatic method.

The prediction interval is categorized into three types. Three axioms that construction predicted interval must satisfy are put forward. Grey wrapping band forecasting method is improved based on the proposed axioms.

Take the Shanghai composite index as the example, according to the K-line diagram from 4 January 2013 to 9 May 2013, the reliability of predicted rebound height of subsequent two or three trading day does not exceed the upper wrapping curve is 80 per cent. It is significant to understand the forecasting range correctly, build a reasonable range forecasting method and to apply grey wrapping band forecasting method correctly.

Grey wrapping band forecasting method is improved based on the proposed axioms.

A general review has been conducted to emphasize the increasing concern with reliability in the engineering industry. The latest books and publications available to the authors have been reviewed during the survey to identify the latest thinking on the topic. Emphasizes the prediction of reliability and its use for further reliability analysis methods. Describes and briefly explains modern methods and tools for reliability prediction, to give an overview to engineers and managers interested in the subject. Includes a small case study of a subsystem of an aircraft system as example of an application of the subject. Includes in the reference section the books and papers used during the review and references for further reading into the subject.

This paper is focused at studying the current state of research involving the four dimensions of defect management strategy, i.e. software defect analysis, software quality, software reliability and software development cost/effort.

The methodology developed by Kitchenham (2007) is followed in planning, conducting and reporting of the systematic review. Out of 625 research papers, nearly 100 primary studies related to our research domain are considered. The study attempted to find the various techniques, metrics, data sets and performance validation measures used by researchers.

The study revealed the need for integrating the four dimensions of defect management and studying its effect on software performance. This integrated approach can lead to optimal use of resources in software development process.

There are many dimensions in defect management studies. The authors have considered only vital few based on the practical experiences of software engineers. Most of the research work cited in this review used public data repositories to validate their methodology and there is a need to apply these research methods on real datasets from industry to realize the actual potential of these techniques.

The authors believe that this paper provides a comprehensive insight into the various aspects of state-of-the-art research in software defect management. The authors feel that this is the only research article that delves into the four facets namely software defect analysis, software quality, software reliability and software development cost/effort.

This research aims to develop an approach to assess the reliability of integrated construction supply chains via an integrated model of building information modelling (BIM) and the lean supply chain (LSC). It reflects the synergistic workflow between BIM and LSC as a novel approach to improve the reliability of construction projects.

This research evaluates the reliability of the BIM-LSC approach through a combination of entropy theory, set pair analysis (SPA), and Markov chains (EESM). An exploratory survey was conducted to collect data from 316 industry professionals experienced in BIM and LSC. Subsequently, multiple cycles of calculations were performed with indirect data inputs. Finally, a reliability evaluation index is established for the BIM-LSC approach and potential applications are identified.

The results show that the EESM model of BIM-LSC developed in this study can handle not only supply chain reliability evaluation at a given state but also the prediction of reliability in supply chain state transitions due to changing project conditions. This is particularly relevant to the current environment of the construction project, which is characterised by an increasing level of complexity in terms of labour, technology, and resource interactions.

Future research could consider the accuracy and validity of the proposed model in real-life scenarios with by considering both quantitative and qualitative data across the entire lifecycle of projects.

The research offers a model to evaluate the reliability of the BIM-LSC approach. The accuracy of BIM supply chain reliability analysis and prediction in an uncertain environment is improved.

The BIM-LSC reliability evaluation and prediction presented in this study provides a theoretical foundation to enhance understanding of the BIM-LSC in the construction project context.

The paper presents an efficient methodology that was developed for the reliability prediction and the failure mode effects and criticality analysis (FMECA) of electronic devices using fuzzy logic.

The reliability prediction is based on the general features and characteristics of the MIL‐HDBK‐217FN2 technical document and a derating plan for the system design is developed in order to maintain low components’ failure rates. These failure rates are used in the FMECA, which uses fuzzy sets to represent the respective parameters. A fuzzy failure mode risk index is introduced that gives priority to the criticality of the components for the system operation, while a knowledge base is developed to identify the rules governing the fuzzy inputs and output. The fuzzy inference module is Mamdani type and uses the min‐max implication‐aggregation.

A typical power electronic device such as a switched mode power supply was analyzed and the appropriate reliability indices were estimated using the stress factors of the derating plan. The fuzzy failure mode risk indices were calculated and compared with the respective indices calculated by the conventional FMECA.

Further research efforts are needed for the application of fuzzy modeling techniques in the area of reliability assessment of electronic devices. These research efforts can be concentrated in certain applications that have practical value.

Practical applications can use a fuzzy FMECA modeling instead of the classical FMECA one, in order to obtain a more accurate analysis.

Fuzzy modeling of FMECA is described which can calculate fuzzy failure mode risk indices.

The purpose of this paper is to propose an accurate product reliability prediction model in order to enhance product quality and reduce product costs.

This study proposes a new method for predicting the reliability of repairable systems. The novel method employed constructs a predictive model by integrating neural networks and genetic algorithms. Findings – The novel method employed constructs a predictive model by integrating neural networks and genetic algorithms. Genetic algorithms are used to globally optimize the number of neurons in the hidden layer, the learning rate and momentum of neural network architecture. Research limitations/implications – This study only adopts real failure data from an electronic system to verify the feasibility and effectiveness of the proposed method. Future research may use other product's failure data to verify the proposed method. The proposed method is superior to ARIMA and neural network model prediction techniques in the reliability of repairable systems. Practical implications – Based on the more accurate analytical results achieved by the proposed method, engineers or management authorities can take follow‐up actions to ensure that products meet quality requirements, provide logistical support and correct product design. Originality/value – The proposed method is superior to other prediction techniques in predicting the reliability of repairable systems.

The purpose of this paper is to achieve high accuracy in forecasting generation reliability by accurately evaluating the reliability of power systems. This study uses the RTS-79 reliability test system to measure the method’s effectiveness, using mean absolute percentage error as the performance metrics. Accurate reliability predictions can inform critical decisions related to system design, expansion and maintenance, making this study relevant to power system planning and management.

This paper proposes a novel approach that uses a radial basis kernel function-based support vector regression method to accurately evaluate the reliability of power systems. The approach selects relevant system features and computes loss of load expectation (LOLE) and expected energy not supplied (EENS) using the analytical unit additional algorithm. The proposed method is evaluated under two scenarios, with changes applied to the load demand side or both the generation system and load profile.

The proposed method predicts LOLE and EENS with high accuracy, especially in the first scenario. The results demonstrate the method’s effectiveness in forecasting generation reliability. Accurate reliability predictions can inform critical decisions related to system design, expansion and maintenance. Therefore, the findings of this study have significant implications for power system planning and management.

What sets this approach apart is the extraction of several features from both the generation and load sides of the power system, representing a unique contribution to the field.

While increased mechanization and automation make considerable contributions to mine productivity, unexpected equipment failures and imperfect planned or routine maintenance prohibit the maximum possible utilization of sophisticated mining equipment and require significant amount of extra capital investment. Traditional preventive/planned maintenance is usually scheduled at a fixed interval based on maintenance personnel's experience and it can result in decreasing reliability. This paper deals with reliability analysis and prediction for mining machinery. A software tool called GenRel is discussed with its theoretical background, applied algorithms and its current improvements. In GenRel, it is assumed that failures of mining equipment caused by an array of factors (e.g. age of equipment, operating environment) follow the biological evolution theory. GenRel then simulates the failure occurrences during a time period of interest based on Genetic Algorithms (GAs) combined with a number of statistical procedures. The paper also discusses a case study of two mine hoists. The purpose of this paper is to investigate whether or not GenRel can be applied for reliability analysis of mine hoists in real life.

Statistical testing methods are applied to examine the similarity between the predicted data set with the real-life data set in the same time period. The data employed in this case study is compiled from two mine hoists from the Sudbury area in Ontario, Canada. Potential applications of the reliability assessment results yielded from GenRel include reliability-centered maintenance planning and production simulation.

The case studies shown in this paper demonstrate successful applications of a GAs-based software, GenRel, to analyze and predict dynamic reliability characteristics of two hoist systems. Two separate case studies in Mine A and Mine B at a time interval of three months both present acceptable prediction results at a given level of confidence, 5 percent.

Potential applications of the reliability assessment results yielded from GenRel include reliability-centered maintenance planning and production simulation.

Compared to conventional mathematical models, GAs offer several key advantages. To the best of the authors’ knowledge, there has not been a wide application of GAs in hoist reliability assessment and prediction. In addition, the authors bring discrete distribution functions to the software tool (GenRel) for the first time and significantly improve computing efficiency. The results of the case studies demonstrate successful application of GenRel in assessing and predicting hoist reliability, and this may lead to better preventative maintenance management in the industry.

The use of “fault‐counting” models with “discrete” data in the case of commercial software has considerable advantages for the vendor. The adapted Littlewood Stochastic Reliability Growth model has the advantage of allowing a variety of fault manifestation rates. The process of inferring the parameters of this model is presented graphically in a way intended to clarify untuitively some of the problems commonly experienced with estimation, particularly where long‐term predictions are required. Based on this, alternative objective functions are suggested for fitting the model to failure data.