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In manufacturing or production setup, maintenance cost is one of the major portions of overall operating expenditure. It can vary between 15 to 60 percentage of overall cost for various industries including food related industries, iron, steel and other heavy industries. Such a high cost directly impacts manufacturing setup, profitability and sustainability in long run. In most of the industries, ineffective maintenance management can result in loss of capital and inefficient human resource deployment. This in turn affects the plants’ ability to manufacture quality products that are competitive in the market. Various maintenance management strategies including Operate to Failure (OTF), Design Out Maintenance (DOM), Skill Level Upgrade (SLU), ConditionBased Monitoring (CBM) and Fixed Time Maintenance (FTM) are used in industries for maximizing productivity. In recent years, Computerized Maintenance Management System (CMMS) has become an integral part of most of the industries so its importance and characteristics cannot be understated. While CMMS cannot live standalone, it requires some decision-making techniques to be equipped with. These techniques range from Failure Mode and Effect Analysis (FMEA) to Decision Making Grid (DMG). In this paper, concept of DMG has been applied to an automotive parts Manufacturing Industry in conjunction with Weibull analysis. Parallels are drawn between the results of DMG and Weibull analysis.

Quartz Tuning Fork (QTF) based sensors are used for Scanning Probe Microscopes (SPM), in particular for near-field scanning optical microscopy. Highly sharp Tungsten (W) tips with larger cone angles and less tip diameter are critical for SPM instead of platinum and iridium (Pt/Ir) tips due to their high-quality factor, conductivity, mechanical stability, durability and production at low cost. Tungsten is chosen for its ease of electrochemical etching, yielding high-aspect ratio, sharp tips with tens of nanometer end diameters, while using simple etching circuits and basic electrolyte chemistry. Moreover, the resolution of the SPM images is observed to be associated with the cone angle of the SPM tip, therefore Atomic-Resolution Imaging is obtained with greater cone angles. Here, the goal is to chemically etch W to the smallest possible tip apex diameters. Tips with greater cone angles are produced by the custom etching procedures, which have proved superior in producing high quality tips. Though various methods are developed for the electrochemical etching of W wire, with a range of applications from scanning tunneling microscopy (SPM) to electron sources of scanning electron microscopes, but the basic chemical etching methods need to be optimized for reproducibility, controlling cone angle and tip sharpness that causes problems for the end users. In this research work, comprehensive experiments are carried out for the production of tips from 0.4 mm tungsten wire by three different electrochemical etching techniques, that is, Alternating Current (AC) etching, Meniscus etching and Direct Current (DC) etching. Consequently, sharp and high cone angle tips are obtained with required properties where the results of the W etching are analyzed, with optical microscope, and then with field emission scanning electron microscopy (FE-SEM). Similarly, effects of varying applied voltages and concentration of NaOH solution with comparison among the produced tips are investigated by measuring their cone angle and tip diameter. Moreover, oxidation and impurities, that is, removal of contamination and etching parameters are also studied in this research work. A method has been tested to minimize the oxidation on the surface and the tips were characterized with scanning electron microscope (SEM).

This study focuses on simplification of the Petri net model of a manufacturing system for reduction in the total number of elementary circuits to decrease the analysis time of systems during investigations. A flexible manufacturing system with a tool sharing environment has been developed and investigated using colored Petri nets. The proposed model is extended and implemented on large flexible manufacturing systems (FMSs) having multiple machines and tools. The main objective of this study is to reduce the resultant invariants, as the addition of parts, machines, and tools in black token Petri net models exponentially increases the resultant invariants. Various scenarios have been investigated and compared using the developed model. The structure of the developed colored Petri net (CPN) model allows grouping of the tools that reduces the total number of places in the model. Therefore, it is evident that the developed CPN model reduces the resultant invariant that results in reduced complexity of the system.

PurposeA major factor to the success of flexible manufacturing systems (FMSs) is their ability to transport work pieces between different workstations. FMS have now become more advanced and material‐handling systems have become progressively more sophisticated, it is not exceptional to have automated steering of tools to workstations as well. Such system design will improve the tool‐handling capability and the system productivity while holding tool cost to a minimum. Tool cost could represent as much as 25 percent of the operating cost. The purpose of this paper is to propose a new colored Petri net (CPN)‐based approach to the design and development of a tool sharing control system that is intended to help use of the proper and minimal number of tools for a manufacturing system.Design/methodology/approachA new black token timed PN model is first developed, to reduce the complexity of the graphical representation a new CPN model is developed. The new CPN model also allows to find the optimal sequence. The optimal sequence has no effect on the work in process (WIP) but it influences the number of tools used in the system. The main input to the PN model for a manufacturing system is the process plan. Next, all the invariants and total number of possible elementary circuits are determined using the Integrated Net Analyzer (INA) software. Output from the INA software is exported to the Excel spreadsheet. The Excel spreadsheet can be designed to calculate the total number of tokens, processing time, cycle time, etc. of each elementary circuit. Subsequently, the constraints used in Lingo will be created according to critical circuit rules. Finally, linear programming (LP) technique is used to optimize the WIP and tool inventory. Lingo software is used for the LP, the constraints from the Excel sheet will be the input data to the Lingo program, and based on those constraints the Lingo will provide the optimal values for the desired parameters. The output from Lingo will be used to recalculate the cycle time of each elementary circuit in the Excel sheet. The system is then analyzed before and after the implementation of the CPN model.FindingsA new CPN model based on tool‐sharing philosophy for an FMS with N part types and M stations is proposed.Originality/valueThe paper presents a new CPN‐based approach to the design and development of a tool sharing control system, that is, intended to help use of the proper and minimal number of tools for a manufacturing system. The new CPN model also allows to find the optimal sequence. The idea is new and pure and has not been presented before using the methodology adopted in this paper.

Wireless sensor networks have several applications of direct and indirect benefit to humanity. These applications can be further enhanced by integrating local wireless sensor networks, internet and cloud technology for storing sensors' data. Our proposed application is based on the same concept. The existing processes of collection and analysis of patients' information requires a lot of effort and additionally they are prone to errors and unnecessary delays in real time information accessibility. These issues limit monitoring and diagnostic capabilities to clinics and hospital only. Wireless sensors provide the capability of information exchange among interconnected on-hand health monitoring equipments. The introduction of this concept is based on wireless sensor networks and utility computing. The information can be stored on “cloud” which will be accessible to medical staff. The proof-of-concept aim is to integrate commodity computing to predecessor medical devices, which should ensure simple integration and cost effectiveness into introduction of a system for health monitoring in remote areas for the best benefit of humanity.

Edge detection has beneficial applications in the fields such as machine vision, pattern recognition and biomedical imaging etc. Edge detection highlights high frequency components in the image. Edge detection is a challenging task. It becomes more arduous when it comes to noisy images. This study focuses on fuzzy logic based edge detection in smooth and noisy clinical images. The proposed method (in noisy images) employs a 3 × 3 mask guided by fuzzy rule set. Moreover, in case of smooth clinical images, an extra mask of contrast adjustment is integrated with edge detection mask to intensify the smooth images. The developed method was tested on noise-free, smooth and noisy images. The results were compared with other established edge detection techniques like Sobel, Prewitt, Laplacian of Gaussian (LOG), Roberts and Canny. When the developed edge detection technique was applied to a smooth clinical image of size 270 × 290 pixels having 24 dB 'salt and pepper' noise, it detected very few (22) false edge pixels, compared to Sobel (1931), Prewitt (2741), LOG (3102), Roberts (1451) and Canny (1045) false edge pixels. Therefore it is evident that the developed method offers improved solution to the edge detection problem in smooth and noisy clinical images.