This paper describes the performance assessment of paint manufacturing (PM) unit using stochastic Petri Nets modelling method. The Petri Nets-based modelling method provides more realistic availability results in the performance assessment of complex industrial systems. The ability to deal with multiple failures in the system simultaneously makes the proposed simulation method more powerful graphical tool among the different simulation techniques. The performance assessment of the system is executed with the help of data recorded from the maintenance history sheet for various sub-systems and translated in the input failure and repair rate parameters for proposed simulation method. The Petri Nets modelling method run the plant in virtual manner and the system availability is found to be 80.27% substituting the input parameters in the timed transitions of each sub-system. Further, the performance assessment through Patri Nets Model is compared with Markov Model solution to verify the findings. The results obtained in this paper reflect the realistic behavior of the system and provide a better understanding for maintenance planning.

. As the demand for good governance and smart cities has grown, so has the desire for the use of new technology, software, and procedures to achieve a more effective approach. The Internet of Things (IoT) is one such example of a driving scientific approach to smart city implementation. This paper will cover the IoT, smart cities, and technological advancements such as Big data and other analytical tools in order to synergize and optimise their use in decision making process. Moreover, the existing usage of IoT around the world will be discussed and the challenges associated with it. As urban planning has vast umbrella of domains which comes into conjunction of people, services, decision makers and the many more, there is a high chance of dynamic data and changing of technologies. Thus, a more efficient way in progress of such application in sustainable manner with real time scenario has to be envisaged. Studying of opportunities and challenges in sustainable urban development and IoT shall facilitate conjunction of various sectors with urban planning.

A pivotal development of the obsolescence of conventional methods of sheet forming has enhanced the need of augmenting an agile and novel method of fabricating the tailored components according to the requirements of customers and users. Moreover, businessmen are interested to promote their businesses by manufacturing the tailored and customized parts of intricated and complex geometries at a lower cost and with negligible waste. Single Point Incremental Forming (SPIF), also known as "negative incremental forming", avoids the direct use of any kind of specific die-sets and produces the user-ready parts. The investigation of the surface quality of the formed specimen is important to study the suitability of this process for industrial applications. This paper explores the effects of the interactions of tool radius and forming angle on the surface roughness of parts formed by the SPIF process. A full factorial approach was taken into account as DOE. The average roughness was found to rise with the rise in forming an angle. On the other hand, the increase in tool diameter resulted in the decrease of Ra value of formed components because the increase in the tool diameter allows the decrease in the waviness on the surface of the specimen. It was observed that the Ra value of formed components was decreased by 83.87% when the experimental condition was changed from the combination of higher levels of wall angle (68 degrees) and lower level of tool diameter (7.52 mm) to the combination of lower levels of wall angle (60 degrees) anda higher level of tool diameter (19.50 mm). (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Functional Material, Manufacturing and Performances.

PurposeIn this paper, the objective is to perform mathematical modeling to optimize the steady-state availability of a multi-state repairable crushing system of a sugar plant using the evolutionary algorithm of Particle Swarm Optimization (PSO). The system availability is optimized by evaluating the optimal values of failure and repair rate parameters concerned with the subsystem of the system.Design/methodology/approachMathematical modeling of the multi-state repairable system is performed to develop the first-order differential equations based on the exponential distribution of the failure and repair rates. These differential equations are recursively solved to obtain the availability under normalizing conditions. The availability of the system is optimized by using the PSO algorithm. The results obtained by PSO are validated by using the Genetic Algorithm (GA).FindingsThe availability analysis of the system concludes that the cane preparation (F1) is critical of the crushing system and the optimized availability of the system using PSO is achieved as high as 87.12%.Originality/valueA crushing system of the sugar plant is evaluated as it is the main system of the sugar plant. The maintenance data associated with failure and repair rate parameters were analyzed with the help of maintenance records/logbook and by conducting personal meetings with maintenance executives of the plant. The results obtained in the paper helped them to plan maintenance strategies accordingly to get optimal system availability.

A significant augmentation in the obsolescence of traditional forming techniques results in the strong requirement of developing an emerging and flexible process to fabricate the user-ready parts in the manufacturing units. Single Point Incremental Forming (SPIF) also known as “negative incremental forming” has shown its viability as a novel and emerging forming method for fabricating the customized and batch-type products of sheet materials to satisfy the need of various potential sectors including medical, automobile, and aerospace. This method directly exempts the involvement of dedicated die-sets and turns into the choice of green manufacturing. The surface quality of fabricated parts can greatly decide the suitability and sustainability of the process in various applications. In the current study, the impact of wall angle and tool rotation have been explored for surface roughness during SPIF. Results revealed that the increase in spindle speed resulted in the decrease of Ra value of formed components. Moreover, the Ra value of formed components was found to decrease by 50 % when the experimental condition was changed from the combination of higher levels of wall angle and “free-to-rotate” condition of spindle speed to the combination of lower levels of wall angle (60°) and a higher level of spindle speed (1500 rpm).

The employment of a green manufacturing process can directly save energy and materials in industrial sectors. Single Point Incremental Forming (SPIF) is an agile and flexible method of fabricating sheet material components and exempts the use of dedicated die-sets which further makes it a choice of green manufacturing. Furthermore, the customized components can be easily fabricated by SPIF economically. The surface quality of fabricated parts can greatly decide the suitability and sustainability of the process in various applications. This work investigates the impact of significant process factors on the roughness of the parts during the SPIF process. The average roughness has been considered to determine the surface quality. The increment in the value of wall angle and step size resulted in the increment in Ra value of formed components drastically. It was also observed that as the forming angle was raised from a lower level (60°) to a higher level (68°), the Ra value was found to increase significantly.

The Petri nets modeling method is a powerful tool for the performance analysis of industrial systems. The main factor which makes it more effective among the various popular simulation methods is the ability to deal with the real working conditions. The data obtained from such a manufacturing system is generally full of uncertainties and the Petri nets modeling method deals with this data to reflect the real behavioral pattern of different sub-systems installed in the plant. The Petri nets-based simulation method provides the availability of the sub-systems for a long time period by running the plant in virtual manners. The results obtained through the analysis can be utilized to identify those sub-systems which highly affect the system availability and separate maintenance planning for each sub-system can reduce the production loss due to unavailability of any sub-system for performing its intended task. The proposed methodology has been demonstrated in this paper using a complex repairable manufacturing system.

Purpose The purpose of this paper is to optimize the performance for complex repairable system of paint manufacturing unit using a new hybrid bacterial foraging and particle swarm optimization (BFO-PSO) evolutionary algorithm. For this, a performance model is developed with an objective to analyze the system availability. Design/methodology/approach In this paper, a Markov process-based performance model is put forward for system availability estimation. The differential equations associated with the performance model are developed assuming that the failure and repair rate parameters of each sub-system are constant and follow the exponential distribution. The long-run availability expression for the system has been derived using normalizing condition. This mathematical framework is utilized for developing an optimization model in MATLAB 15 and solved through BFO-PSO and basic particle swarm optimization (PSO) evolutionary algorithms coded in the light of applicability. In this analysis, the optimal input parameters are determined for better system performance. Findings In the present study, the sensitivity analysis for various sub-systems is carried out in a more consistent manner in terms of the effect on system availability. The optimal failure and repair rate parameters are obtained by solving the performance optimization model through the proposed hybrid BFO-PSO algorithm and hence improved system availability. Further, the results obtained through the proposed evolutionary algorithm are compared with the PSO findings in order to verify the solution. It can be clearly observed from the obtained results that the hybrid BFO-PSO algorithm modifies the solution more precisely and consistently. Research limitations/implications There is no limitation for implementation of proposed methodology in complex systems, and it can, therefore, be used to analyze the behavior of the other repairable systems in higher sensitivity zone. Originality/value The performance model of the paint manufacturing system is formulated by utilizing the available uncertain data of the used manufacturing unit. Using these data information, which affects the performance of the system are parameterized in the input failure and repair rate parameters for each sub-system. Further, these parameters are varied to find the sensitivity of a sub-system for system availability among the various sub-systems in order to predict the repair priorities for different sub-systems. The findings of the present study show their correspondence with the system experience and highlight the various availability measures for the system analyst in maintenance planning.

PurposeThe purpose of this paper is to identify the criticality of various sub-systems through the behavioral study of a multi-state repairable system with hot redundancy. The availability of the system is optimized to evaluate the optimum combinations of failure and repair rate parameters for various sub-systems.Design/methodology/approachThe behavioral study of the system is conducted through the stochastic model under probabilistic approach, i.e., Markov process. The first-order differential equations associated with the stochastic model are derived with the use of mnemonic rule assuming that the failure and repair rate parameters of all the sub-systems are constant and exponentially distributed. These differential equations are further solved recursively using the normalizing condition to obtain the long-run availability of the system. A particle swarm optimization (PSO) algorithm for evaluating the optimum availability of the system and supporting computational results are presented.FindingsThe maintenance priorities for various sub-systems can easily be set up, as it is clearly identified in the behavioral analysis that the sub-system (A) is the most critical component which highly influences the system availability as compared to other sub-systems. The PSO technique modifies input failure and repair rate parameters for each sub-system and evaluates the optimum availability of the system.Originality/valueA bottom case manufacturing system is under the evaluation, which is the main component of front shock absorber in two-wheelers. The input failure and repair rate parameters were parameterized from the information provided by the plant personnel. The finding of the paper provides the various availability measures and shows the grate congruence with the system behavior.

The complexity in industrial system design under specific practical constraints has a great impact on the range of prediction in system behavior. The data collected in such conditions lead to the high range of uncertainties and the consequence is a possibility of low system performance. Thus, the main objective of the present study is to analyze the system behavior and remove the uncertainties up to the desired accuracy. For this, the mathematical formulation of the system is carried out using probabilistic approach i. e. Markov process. The input failure and repair rate parameters of various sub-systems used in the mathematical expression are considered as constant and statistically independent. Further, the particle swarm optimization (PSO) technique has been used to optimize the system performance in order to improve the system efficiency. A complex repairable system of ton container manufacturing plant has been considered to demonstrate the effectiveness of proposed methodology.

The paper deals with performance optimisation for ethanol manufacturing system of distillery plant using particle swarm optimisation (PSO) algorithm. The performance of the system is first estimated with the help of transition diagram and a mathematical model based on Markov approach in real working environment. The differential equations associated with the transition diagram are developed assuming that the failure and repair rate parameters of each component follow the exponential distribution. The long-run availability expression for the system has been derived with probabilistic approach using normalising condition. The availability of the system is then computed with the help of PSO technique and compared with the genetic algorithm (GA) results to verify the solution. The results show that PSO modifies the solution more precisely and provides the optimum combinations of failure and repair rate parameters for various subsystems which are practically useful in maintenance planning for plant personnel.

In the present study an attempt has been made to identify and eliminate different types of wastages with the application of Lean tools in an automobile industry. In this case study, Labels has been selected due to high aesthetic value, new plant is setup and complicated processing cycle resulting into excessive work in process inventory and long lead time problem. Company is facing problem in the production of Labels. Company does not achieve daily production target as per customer end. Company facing the problems like as large WIP, Production Lead Time, worker motivation and no systematically production approach. VSM has proved effective in identifying and eliminating wastages under these circumstances. After the identification of the gap areas, the Lean Manufacturing tools such as Kaizen, Visual Controls, Kanban System, workplace organization were proposed for productivity and quality improvements. The detailed analysis of current state has been presented and the opportunities for improvement have been presented for eliminating various waste elements and future state VSM has been developed. Solutions suggested lead to a significant decrease in production lead time, work in process inventory and hence overall cost ultimately and solve many problems at customer end.

With increasingly environmental constraints the modern Power and Energy Systems are experiencing huge transformations in many ways.The quantum of data in power systems is growing rapidly due to large database used by power systems engineers for various operations.From the power generation plants the electrical energy is transmitted and distributed to end users.Frequent failure of various equipments and the systems has made it impossible to maintain the continuity of supply.Sometimes these failures are beyond the control of the power system operator.The operation and planning of power systems provide a large amount of data and it is difficult to extract the useful information from this large database that is continuously used by operators.Data mining is a process of extracting interesting and previously unknown knowledge from a set of data.The data mining techniques help power systems planner/operator to have smooth system planning, operation and are useful for extracting useful information from the existing data banks.The paper describes the data mining technology and its applications that would be useful in power systems.

Finding the geographical location of the sensors is the most common problem associated with the Wireless sensor networks. The Sequential Monte Carlo Localization (SMCL) algorithm is the base for most of the localization algorithms proposed. These localization algorithms requires high seed node density and they also suffers from low sampling efficiency. There are some papers which solves this problems but they are not energy efficient. Another approach The Monte Carlo Localization Boxed (MCB) method was used to reduce the scope of searching the candidate samples and thus reduces the time for finding the set of valid samples. In this paper we have proposed an energy efficient approach which will consider the direction of movement and the speed of acceleration of the sensor nodes. This additional information will help in further reducing the scope of searching the candidate samples. The valid samples will have more accurate location information and they are less likely to be filtered in the filtering step of SMCL. Thus it reduces the number of iterations the algorithm needs and hence achieves high localization accuracy in less time as compared to traditional SMCL and MCB.