Prasad K. Yarlagadda

A review on degradation models in reliability analysis

With increasingly complex engineering assets and tight economic requirements, asset reliability becomes more crucial in Engineering Asset Management (EAM). Improving the reliability of systems has always been a major aim of EAM. Reliability assessment using degradation data has become a significant approach to evaluate the reliability and safety of critical systems. Degradation data often provide more information than failure time data for assessing reliability and predicting the remnant life of systems. In general, degradation is the reduction in performance, reliability, and life span of assets. Many failure mechanisms can be traced to an underlying degradation process. Degradation phenomenon is a kind of stochastic process; therefore, it could be modelled in several approaches. Degradation modelling techniques have generated a great amount of research in reliability field. While degradation models play a significant role in reliability analysis, there are few review papers on that. This paper presents a review of the existing literature on commonly used degradation models in reliability analysis. The current research and developments in degradation models are reviewed and summarised in this paper. This study synthesises these models and classifies them in certain groups. Additionally, it attempts to identify the merits, limitations, and applications of each model. It provides potential applications of these degradation models in asset health and reliability prediction.

Bacterial adherence and biofilm formation on medical implants : a review

Biofilms are a complex group of microbial cells that adhere to the exopolysaccharide matrix present on the surface of medical devices. Biofilm-associated infections in the medical devices pose a serious problem to the public health and adversely affect the function of the device. Medical implants used in oral and orthopedic surgery are fabricated using alloys such as stainless steel and titanium. The biological behavior, such as osseointegration and its antibacterial activity, essentially depends on both the chemical composition and the morphology of the surface of the device. Surface treatment of medical implants by various physical and chemical techniques are attempted in order to improve their surface properties so as to facilitate bio-integration and prevent bacterial adhesion. The potential source of infection of the surrounding tissue and antimicrobial strategies are from bacteria adherent to or in a biofilm on the implant which should prevent both biofilm formation and tissue colonization. This article provides an overview of bacterial biofilm formation and methods adopted for the inhibition of bacterial adhesion on medical implants

A neural network system for the prediction of process parameters in pressure die casting

In this work an artificial intelligent neural network system is developed to generate the process parameters for the pressure die casting process. The scope of this work includes analysing a physical model of the pressure die casting filling stage based on the governing equations of die cavity filling, and the collection of feasible casting data for the training of the network through the use of simulation package MELTFLOW and also from experts in the die casting industry. The multi-layer feed-forward network is trained with data collected directly from the industry using MATLAB application tool box. In this work the neural network is developed using three different training algorithms; namely the error back-propagation algorithm, the momentum and adaptive learning algorithm, and the Levenberg–Mrquardt approximation algorithm. It is found that the Levenberg–Mrquardt approximation algorithm is the preferred method for this application, as it reduces the sum-squared error to a small value. The accuracy of the network developed is tested by comparing the data generated from the network with that from an expert from a local die casting industry. It has been realised that with the use of this system the selection of process parameters becomes much simpler to even a novice user without prior knowledge of die casting process and optimisation techniques.

Optimal design of neural networks for control in robotic arc welding

Robotic gas metal arc (GMA) welding is a manufacturing process which is used to produce high quality joints and has to a capability to be utilized in automation systems to enhance productivity. Despite its widespread use in the various manufacturing industries, the full automation of the robotic GMA welding has not yet been achieved partly because mathematical models for the process parameters for a given welding tasks are not fully understood and quantified. In this research, an attempt has been made to develop a neural network model to predict the weld bead width as a function of key process parameters in robotic GMA welding. The neural network model is developed using two different training algorithms; the error back-propagation algorithm and the Levenberg–Marquardt approximation algorithm. The accuracy of the neural network models developed in this study has been tested by comparing the simulated data obtained from the neural network model with that obtained from the actual robotic welding experiments. The result shows that the Levenberg–Marquardt approximation algorithm is the preferred method, as this algorithm reduces the root of the mean sum of squared (RMS) error to a significantly small value.

Feasibility studies on the production of electro-discharge machining electrodes with rapid prototyping and the electroforming process

Tooling is an important area in the manufacturing process. Increase in the complexity of tooling for any operation results in a corresponding increase in the time and costs required to develop such tooling. Rapid tooling is the concept of producing tools through the aid of rapid prototyping. The ideal candidate operations for rapid tooling have been those for which it is difficult to develop tooling by the usual methods. Non-traditional machining operations are potential candidates that can make use of the advantages of rapid tooling. One such operation is electric discharge machining (EDM) or spark eroding. In this paper the use of rapid prototype patterns, made by the stereolithography technique, for the manufacture of EDM electrodes is discussed. The use of other techniques such as silicone rubber casting and electroforming in the making of the EDM electrode is also described. This work deals with the viability of using an electroformed shell of copper, backed with a suitable material, as an EDM electrode. Based on the present study it can be concluded that electroformed copper electrodes seem to possess an excellent potential for use as EDM tools.

Statistical analysis on accuracy of wax patterns used in investment casting process

Abstract The primary objective of this research work is to determine the accuracy of wax patterns produced by hard and soft tooling and optimise the injection parameters used in low pressure injection moulding. Wax patterns are produced using both the hard (polyurethane mould) and soft (RTV mould) tools. It is essential to use the optimal injection parameters during moulding in order to obtain good dimensional accuracy of wax patterns. From the current study it is noticed that the polyurethane mould produce accurate patterns than the silicone mould. Based on the study on optimisation of the injection parameters, it is found that using a lower pressure with higher temperature for the polyurethane mould will produce an accurate patterns provided that appropriate care is taken while choosing the holding time. A short holding time will yield a more accurate pattern, but too short a holding time will cause distortion when removing it from the mould, as it is too soft. Too long a holding time will cause more shrinkage. For the silicone mould, only the injection temperature has an effect on the dimensions of the wax patterns. The dimensional errors incurred during dipping are also measured and found that generally, there is a reduction of 0.2–0.4% in dimension. These studies will help the investment caster to estimate the allowance required in the initial CAD drawings to produce a final casting with minimal dimensional inaccuracy.

Development of an integrated neural network system for prediction of process parameters in metal injection moulding

In this present work attempts have been made to develop an integrated neural network system for prediction of process parameters such as injection pressure and injection time in metal injection moulding (MIM) process. The current system has been developed by integrating the different aspects of MIM process. The aspects that are addressed in this system are the physical model of MIM filling stage based on governing equations of mould filling, and process parameters for debinding and sintering stages generated by experimentation. In this work the feed forward type of neural network has been used, which was initially trained with the analytical data before incorporating as part of an integrated system. In this work Gauss training method has been incorporated for the usage of function approximation. This integrated system has been implemented in MatLAB environment by using neural networks toolbox. This integrated system was successfully tested to solve the real world problems ofMIM process. The analytical algorithm based on governing equations of mould filling process first produces a feasible injection time for the MIM process. Injection time data is then used to train the neural network system. In order to validate the results generated by the neural network system are checked with the simulation results of the ‘‘Moldflow’’ software and found that the results generated by integrated neural network system are not different from the simulated results.

Wayfinding: A simple concept, a complex process

Wayfinding is the process of finding your way to a destination in a familiar or unfamiliar setting using any cues given by the environment. Due to its ubiquity in everyday life, wayfinding appears on the surface to be a simply characterized and understood process; however, this very ubiquity and the resulting need to refine and optimize wayfinding has led to a great number of studies that have revealed that it is in fact a deeply complex exercise. In this article, we examine the motivations for investigating wayfinding, with particular attention being paid to the unique challenges faced in transportation hubs, and discuss the associated principles and factors involved as they have been perceived from different research perspectives. We also review the approaches used to date in the modelling of wayfinding in various contexts. We attempt to draw together the different perspectives applied to wayfinding and postulate the importance of wayfinding and the need to understand this seemingly simple, but concurrently complex, process.

Development of a hybrid neural network system for prediction of process parameters in injection moulding

Abstract In this paper, the attempts made by the authors to develop an artificial neural network system for prediction of injection moulding process parameters is presented. In this work, attempts have been made to determine the process parameters that could affect injection moulding process based on governing equations of the filling process. Focus is then directed to parameters that require the use of trial and error methods or other complex software to determine the process parameters. The two parameters that are predicted from the developed network are injection time and injection pressure. In this work, the training data are generated by simulation using C-MOLD flow simulation software. A total of 114 data were collected out of which 94 were used to train the network using MATLAB and the remaining 20 for testing the network. Two algorithms are used during the training phase, namely the error-back-propagation algorithm and the Levenberg–Marquardt approximation algorithm. Results showed that the latter algorithm is more suitable for this application since the Leverberg’s algorithm converged rapidly with lesser training cycles when compared to the error-back-propagation algorithm. The accuracy of the developed network has been tested by predicting the injection pressure and injection time for few engineering components and found that the overall error is 0.93% with a deviation of 3.93%.

A review on reliability models with covariates

Modern Engineering Asset Management (EAM) requires the accurate assessment of current and the prediction of future asset health condition. Suitable mathematical models that are capable of predicting Time-to-Failure (TTF) and the probability of failure in future time are essential. In traditional reliability models, the lifetime of assets is estimated using failure time data. However, in most real-life situations and industry applications, the lifetime of assets is influenced by different risk factors, which are called covariates. The fundamental notion in reliability theory is the failure time of a system and its covariates. These covariates change stochastically and may influence and/or indicate the failure time. Research shows that many statistical models have been developed to estimate the hazard of assets or individuals with covariates. An extensive amount of literature on hazard models with covariates (also termed covariate models), including theory and practical applications, has emerged. This paper is a state-of-the-art review of the existing literature on these covariate models in both the reliability and biomedical fields. One of the major purposes of this expository paper is to synthesise these models from both industrial reliability and biomedical fields and then contextually group them into non-parametric and semiparametric models. Comments on their merits and limitations are also presented. Another main purpose of this paper is to comprehensively review and summarise the current research on the development of the covariate models so as to facilitate the application of more covariate modelling techniques into prognostics and asset health management.