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Oil‐in‐water (O/W) emulsion has been used in industrial rolling mills for many decades, but its lubrication mechanism is still not adequately understood. There is a need to understand the role of chemical ingredients and emulsifier in lubrication and tribological characteristics of rolling oil. With this purpose, the authors selected three commercially available O/W emulsions of different generations and of known industrial performance. The aim is to understand the lubrication mechanism of these rolling oils and to correlate the laboratory findings with that of industrial rolling mills.

The lubrication mechanism has been studied with the help of an ultra thin film interferometry EHD test rig, an advanced experimental rolling mill and a Coulter LS 230 instrument. Film thickness, rolling parameters and droplet size were measured. The coefficient of friction was computed with the help of the measured values of rolling parameters. Emulsion stability and saponification value (SAP) of the selected emulsions were also determined. The results of film thickness, rolling parameters and droplet size have been presented. The lubrication mechanism of the emulsions has been explained on the basis of film thickness, droplet size, emulsion stability, SAP value and coefficient of friction.

Results of the present study reveal that chemistry of O/W emulsions plays an important role in their film forming and tribological behavior. Rolling emulsions of relatively low stability, higher droplet size and high SAP value are found to provide better lubrication and lower coefficient of friction. The results of the present study correlate well with the actual industrial experience except those obtained on EHD test rig.

Coulter LS 230 instrument was available with M/s LUBRIZOL CORP., USA. Only limited study on droplet size was carried. Although the study carried out has given good information but it would have been more practical if the emulsion samples taken from the experimental mill stand would have been studied for droplet size.

From understanding point of view of lubrication mechanism of O/W emulsion, it will be useful for oil technologists, tribologists and rolling mill users.

The study is original in nature and gives information on lubrication mechanism of O/W emulsions in steel cold rolling of steel strips.

A method for qualitative estimation of reliability of large and complex mechanical and hydraulic systems is presented. It is especially useful for comparison and optimum selection of the structure at the conceptual stage of design when no other information about the salient features or parameters of the system is known. The method permits the identification and analysis of critical paths, loops and subsystems causing failure under different causes and modes. The method is based on graph theory and the graph variants proposed as reliability measures are also modified to yield realistic and useful results. The concept of system graph introduced in the article for dealing with large systems appears to be the most appropriate for analysis, comparison, selection and reliability estimates at the beginning of the system′s design.

The purpose of this paper is to propose the use of graph theoretic structural modeling for assessing the possible reduction in complexity of the work flow procedures in an organization due to lean initiatives. A tool to assess the impact of lean initiative on complexity of the system at an early stage of decision making is proposed.

First, the permanent function-based graph theoretic structural model has been applied to understand the complex structure of a manufacturing system under consideration. The model helps by systematically breaking it into different sub-graphs that identify all the cycles of interactions among the subsystems in the organization in a systematic manner. The physical interpretation of the existing quantitative methods linked to graph theoretic methodology, namely two types of coefficients of dissimilarity, has been used to evolve the new measures of organizational complexity. The new methods have been deployed for studying the impact of different lean initiatives on complexity reduction in a case industrial organization.

The usefulness and the application of new proposed measures of complexity have been demonstrated with the help of three cases of lean initiatives in an industrial organization. The new measures of complexity have been proposed as a credible tool for studying the lean initiatives and their implications.

The paper may lead many researchers to use the proposed tool to model different cases of lean manufacturing and pave a new direction for future research in lean manufacturing.

The paper demonstrates the application of new tools through cases and the tool may be used by practitioners of lean philosophy or total quality management to model and investigate their decisions.

The proposed measures of complexity are absolutely new addition to the tool box of graph theoretic structural modeling and have a potential to be adopted by practical decision makers to steer their organizations though such decisions before the costly interruptions in manufacturing systems are tried on ground.

Reliability assessment does require an effective structural modelling approach for systems, in general and manufacturing systems are no exception. This paper aims to develop it for large manufacturing systems using graph models, a systems approach.

Structural graph models for reliability at various hierarchical levels are developed by considering a CNC cam shaft grinding machine. The system reliability expression is obtained by converting the reliability graphs into equivalent matrices, which helps to evaluate and analyse system.

Using the obtained reliability expressions at various hierarchical levels of the system, it is possible not only to evaluate its reliability from structure point of view but also to identify weak structural elements from reliability point of view.

The approach can be extended to include the influence of other parameters, such as human, component and environment, etc., on the system reliability.

The approach helps to design and develop manufacturing systems from reliability consideration by assessing their possible alternatives among these.

The suggested methodology is useful for reliability evaluation of large and complex manufacturing systems.

The purpose of this paper is to attempt to integrate manufacturing system analysis to obtain system‐wide optimized solutions and to increase the level of comprehensiveness of the manufacturing system modelling and to develop method of characterization of manufacturing systems based on its structure.

Elements constituting the manufacturing plant and the interactions between them have been identified through a literature survey and have been represented by graph‐based model. The matrix models and the variable permanent function models are developed for carrying out decomposition, characterization and the total analysis.

Structural patterns and combination sets of subsystems interacting in various ways have been recognized as capabilities of manufacturing system in different performance dimensions. The permanent function of the manufacturing system matrix has been proposed as a systematic technique for structural analysis of manufacturing system. Also, the terms of permanent multinomial characterize the manufacturing systems uniquely and are highly useful for computational storage, retrieval, communication as well as analysis of the structural information of manufacturing system.

The structure‐based characterization technique developed has the potential of aiding the ongoing research activities in the field of benchmarking, and business process reengineering. The graph theory‐based methodology will serve as a framework to develop composite performance measures building on the performance measures of the individual elements of the manufacturing system graph in various dimensions.

Through the use of proposed methodology, a manufacturing manager will be able to make better informed decisions towards organizational efforts of improving the productivity and speed. For aiding several decisions, different “what‐if” scenarios may be generated with several structural modifications.

This graph theory‐based methodology is a novel mechanism to seamlessly integrate manufacturing system giving way to system wide optimization. The paper is an attempt to address the need for comprehensive and integrated analysis of the manufacturing system.

Reliability and maintainability estimation of any system depends on the identification of the best-fitted probability distribution of failure and repair rates. The parameters of the best-fitted probability distribution are also contributing significantly to reliability estimation. In this work, a case study of load haul dump (LHD) machines is illustrated that consider the optimization of failure and repair rate parameters using two well established metaheuristic approaches, namely, genetic algorithm (GA) and particle swarm optimization (PSO). This paper aims to analyze the aforementioned points.

The data on time between failures (TBF) and time to repairs (TTR) are collected for a LHD machine. The descriptive statistical analysis of TBF & TTR data is performed, trend and serial correlation tested and using Anderson–Darling (AD) value best-fitted distributions are identified for repair and failure times of various subsystems. The traditional methods of estimation like maximum likelihood estimation, method of moments, least-square estimation method help only in finding the local solution. Here, for finding the global solution two well-known metaheuristic approaches are applied.

The reliability of the LHD machine after 60 days on the real data set is 28.55%, using GA on 250 generations is 17.64%, and using PSO on 100 generations and 100 iterations is 30.25%. The PSO technique gives the global best value of reliability.

The present work will be very convenient for reliability engineers, researchers and maintenance managers to understand the failure and repair pattern of LHD machines. The same methodology can be applied in other process industries also.

In this case study, initially likelihood function of the best-fitted distribution is optimized by GA and PSO. Reliability and maintainability of LHD machines evaluated by the traditional approach, GA and PSO are compared. These results will be very helpful for maintenance engineers to plan new maintenance strategies for better functioning of LHD machines.

The purpose of this study is to propose the time series decomposition approach to analyze and predict the failure data of the repairable systems.

This study employs NHPP to model the failure data. Initially, Nelson's graph method is employed to estimate the mean number of repairs and the MCRF value for the repairable system. Second, the time series decomposition approach is employed to predict the mean number of repairs and MCRF values.

The proposed method can analyze and predict the reliability for repairable systems. It can analyze the combined effect of trend‐cycle components and the seasonal component of the failure data.

This study only adopts simulated data to verify the proposed method. Future research may use other real products' 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.

Results in this study can provide a valuable reference for engineers when constructing quality feedback systems for assessing current quality conditions, providing logistical support, correcting product design, facilitating optimal component‐replacement and maintenance strategies, and ensuring that products meet quality requirements.

The time series decomposition approach was used to model and analyze software aging and software failure in 2007. However, the time series decomposition approach was rarely used for modeling and analyzing the failure data for repairable systems. This study proposes the time series decomposition approach to analyze and predict the failure data of the repairable systems and the proposed method is better than the ARIMA model and neural networks in predictive accuracy.

The purpose of this paper is to implement the six sigma (SS) strategy in a bag sector under actual operating circumstances based on defining-measure-analyze-improve and control (DMAIC). During the project, several statistical tools and methods have been used efficiently to create inferences. Thus, to measure and enhance system efficiency, the author calculate reliability, availability and maintainability (RAM) indices. Based on this research, the author show how the SS method and RAM analysis are very helpful in determining maintenance intervals, as well as in planning and organizing the appropriate maintenance strategy.

This study introduces the step-by-step application of the DMAIC methodology for the identification and reduction of bag production line downtime and examines the present operations management. Thus, statistical techniques are used to analyze the failure and repair database. Pareto analysis, histograms and descriptive statistics at the machine and line-level of the historical data were conducted. Trend and serial correlation testing validated the hypothesis of independence and identical distribution of database was performed. In addition, with their best fit allocation, the RAM of both the bag production line and its machines was estimated at separate mission times.

The main goals of the applied method are to understand the nature of the downtime patterns and to accurately and quantitatively estimate the RAM characteristics of the bag production system. The assessment defines the production line's critical points, requiring further enhancement through an efficient maintenance approach. Therefore, by improving plant efficiency and safety, the author can decrease unplanned downtime and equipment failures.

This research is expected to serve as an attempt to conduct SS DMAIC methodology through RAM assessment and its impact on system efficiency under actual circumstances. The benefit of the methodology is that the manufacturing process is continuously monitored by suitable indicators, the use of which leads to a continuous improvement cycle.

The purpose of this paper is to propose a practical method of performing maintenance in the offshore industry where engineers have to manage problems such as the high cost of operations, assuring an high availability of the plant, safety on board and environmental protection. Indeed an efficient maintenance method it is necessary in order to offer methods and criteria to select the rights maintenance strategies keeping in to account the environmental, safety and production constrains.

The paper provides an overview of reliability centered maintenance (RCM) and reliability, availability, maintainability methodologies and an integration of the two methodologies in a particular case study in the oil and gas sector.

This paper suggests an improvement of the well-established RCM methodology applicable to industries with high priority level. It is proposed an integration between a reliability analysis and an availability analysis and an application on the offshore oil and gas industry.

The methodology provides an excellent tool that can be utilized in industries, where safety, regulations and the availability of the plant play a fundamental role.

The proposed methodology provides a practical method for selecting the best maintenance strategy considering the equipment redundancy and sparing, the asset’s performance over long time scales, and the system uptime, downtime and slowdowns.

The purpose of this paper is to present a hybridized technique for analyzing the behavior of an industrial system stochastically utilizing vague, imprecise, and uncertain data. The press unit of a paper mill situated in a northern part of India, producing 200 tons of paper per day, has been considered to demonstrate the proposed approach. Sensitivity analysis of system's behavior has also been done.

In the proposed approach, two important tools namely traditional Lambda‐Tau technique and genetic algorithm have been hybridized to build genetic algorithms‐based Lambda‐Tau (GABLT) technique to analyze the behavior of complex repairable industrial systems stochastically up to a desired degree of accuracy. This technique has been demonstrated by computing six well‐known reliability indices used for behavior analysis of the considered system in more promising way.

The behavior analysis results computed by GABLT technique have reduced region of prediction in comparison of existing Lambda‐Tau technique region, i.e. uncertainties involved in the analysis are reduced. Thus, it may be a more useful analysis tool to assess the current system conditions and involved uncertainties. The paper suggested an approach to improve the system's performance.

The paper suggests a hybridized technique for analyzing the stochastic behavior of an industrial subsystem by computing six well‐known reliability indices in the form of fuzzy membership function.