The increasing accessibility and falling costs of printing materials and equipment have led to the widespread acceptance of rapid prototyping technologies, especially 3D printing. This article delves into the investigation of specific mechanical strength properties of 3D-printed elements, with a focus on PLA material. While such properties may be less critical for elements serving solely as models for subsequent manufacturing techniques, they become pivotal when 3D-printed components are integral to the functionality of a device, such as those in unique scientific equipment. The research centers on standardized samples printed with low-cost PLA material, utilizing an affordable 3D printer. The study systematically explores the impact of key parameters, namely, infill density, extrusion temperature, and layer height on the tensile strength of the PLA material. By comprehensively testing these parameters, the research aims to provide insights that enable informed decision-making for the printing of durable elements, whether on non-professional printers or through professional manufacturing services. This research contributes valuable knowledge to the growing field of 3D printing, offering practical guidelines for optimizing PLA material properties in diverse printing scenarios.

This research article presents a novel approach for achieving high-speed data transfer in satellite communication systems using power line communication (PLC) and cloud computing. The use of satellite communication systems is growing in various fields, such as remote sensing and telecommunications, which requires high-speed data transfer to support the increasing demand for data transmission. The proposed approach utilizes PLC technology to transfer data over power lines, which provides a high-speed, reliable, and cost-effective alternative to traditional wireless data transfer methods. Additionally, cloud computing is used to manage and process the large amount of data transmitted by the satellite communication system. The results of the research show that the proposed approach is able to effectively transfer data at high speeds and with low latency, making it suitable for use in satellite communication systems.

This study highlights the growing importance of integrating circular economy principles and sustainability metrics within the manufacturing domain. By adopting these practices, companies can minimize resource consumption, reduce waste generation, and promote the efficient use of materials throughout the entire product lifecycle. Despite recognizing the potential benefits, there is a lack of comprehensive understanding and effective implementation strategies. To address these gaps, this work aims to develop methodological implementation of circularity and sustainability metrics for modern manufacturing industries, in order to: (1) identify the circularity metrics used so far and their significance, (2) development of CE definition anchored in the sustainability concept, and (3) provide recommendations on basis of qualitative i.e. case study and quantitative i.e. analytic hierarchy process (AHP) based decisions making method. By identifying challenges and proposing metrics for evaluation, it enriches academic discourse on sustainable manufacturing practices. Moreover, the study likely draws on theories from sustainability, management, and engineering disciplines to develop a holistic understanding of the subject. From a practical standpoint, the study offers valuable insights for manufacturing practitioners and policymakers. It underscores the importance of integrating circular economy principles into business strategies to enhance resource efficiency and environmental stewardship. Moreover, the proposed sustainability metrics provide practical tools for companies to assess and improve their sustainability performance. However, the study also acknowledges the practical challenges hindering the widespread adoption of circular economy practices and suggests the need for innovative management approaches and interdisciplinary collaborations to overcome these barriers.

Sustainability is nowadays a global research priority, especially in machining, where optimizing production processes for increased productivity, profits, and efficiency is key. Addressing this need, the adoption of nanofluids in minimum quantity lubrication machining has surged, aligning with environmental concerns and regulatory demands. In this study, sustainable zinc oxide (ZnO) and zirconium dioxide (ZrO2) nanoparticles fabricated using plant extracts have been incorporated into conventional cutting fluids to enhance their machinability performance under minimum quantity lubrication for turning process. The microstructural analysis confirms the successful synthesis of the targeted nanoparticles with excellent purity and size distribution. The addition of nanoparticles significantly enhanced thermal conductivity from 0.5916 W/(m⋅K) for the base fluid to 0.6286 W/(m⋅K) for ZnO and to 0.6242 W/(m⋅K) for ZrO2. Further, nanofluids exhibited an increased dynamic viscosity, 1.435 mPa.s for ZrO2 and 1.125 mPa.s for ZnO as compare to 0.7644 mPa.s of base fluid, attributed to the nanoparticle confinement effect whereas contact angle measurements indicated an improved wettability for all nanofluids. Machining experiments validate the efficacy of nanofluids, demonstrating reduced cutting temperatures and enhanced surface finish. Notably, ZrO2-based nanofluids exhibit improved tribological response, while ZnO-based nanofluids showcase exceptional heat transfer ability, offering promising solutions to key technical challenges in machining processes. In conclusion, this study underscores the potential of green, sustainable ZnO and ZrO2 nanoparticles as additives in cutting fluids, poised to revolutionize metalworking and manufacturing processes, thereby enhancing product quality and sustainability.

This research study focuses on the development of a communication network for industrial sectors using internet of things (IoT) technology in order to enhance privacy and security in the cyber-physical system. The increasing reliance on cyber-physical systems in industrial sectors has highlighted the need for secure and private communication networks. The proposed network utilizes advanced encryption techniques and secure communication protocols to protect sensitive data and critical infrastructure. The network architecture is designed to detect and prevent cyber-attacks in real-time and also implements secure communication protocols to prevent unauthorized access. In case of any failures, the proposed communication network has self-healing mechanisms to automatically restore normal operation. The findings of the research show that the proposed communication network is able to effectively protect against cyber-attacks and unauthorized access while maintaining the availability and integrity of the system.

Industries and researchers have been continuously advancing the coolants and lubricants that cope up with the industrial demand in terms of performance, quality and cost without compromising the sustainability requirements like eco-elimination, operator friendly, easier maintenance, treatment, etc. This work discusses the efficacy of nanoparticle-based enhancement in the performance of cutting fluids. Furthermore, it discusses the preparation, lubrication, heat transfer mechanisms, application method and performance of different nano-enhanced lubricants and/or coolants. It also discusses synergistic relationship analysis among various process variables, physicochemical properties, machining mechanisms, and performance. Finally, the future development directions are put forward aiming at current performance limitations of these nano-cutting fluids, which can be taken up for further research.

The study aims to obtain a sustainable solution in terms of material removal rate (MRR, cm 3 /min), surface roughness (Ra, μm), resultant cutting force (F, Newton), and noise level (NL, dBA) as performance measures to improve sustainability in turning process using an integrated MEREC-PIV multi-criteria decision-making (MCDM) method based on L27 orthogonal array (OA) experimental design with three input parameters, i.e., cutting speed (A, 250, 300 & 350 m/min), depth of cut (B, 0.1, 0.3 & 0.5 mm), and feed rate (C, 0.05, 0.25 & 0.45 mm/rev). S/N ratio and ANOM based analysis are also utilized to determine the optimal process parameters for the cumulative target. MEREC based criteria weighting analysis indicates that MRR is the most significant performance measure for the considered domain of input parameters, followed by Ra, F, and NL. The PIV approach, S/N ratio analysis, and ANOM show that the best sustainable alternative is input parameter combination A3B3C3, which is 350 m/min, 0.5 mm, and 0.45 mm/rev. ANOM results show that all four performance measures are highly susceptible to feed rate followed by the depth of cut and cutting speed. ANOVA results reveal that all three input parameters and the feed-depth of cut interaction affect the response significantly. The confirmatory test confirms the optimal input parameter settings.

The purpose of this work is to design and develop an artificial neural network (ANN) model for prediction of the select performance measures (PMs) of a single machine job scheduling (SMJS) system, under the influence of varying levels of input variables. Ten thousand scheduling problems were randomly generated and for each problem values of five PMs were computed which were used to train, test, and validate different ANN models in order to develop the optimal ANN model for accurate prediction of the PMs for a given set of input variables. An ANN model with two hidden layers having 16 neurons in each hidden layer was found to be the optimum model for prediction of the PMs as it resulted in the minimum mean squared error. The actual and predicated values of the PMs obtained from the optimum ANN model were compared and it was found that they were in close agreement. The ANN model developed in this study may be used by the managers, practitioners and other decision makers to define appropriate values of input variables that will yield better PMs. Further, the approach used in the study to develop ANN model may be used for development of similar ANN models for other manufacturing systems. This study provides an ANN-based prediction model to predict PMs of the single machine job scheduling system which may however, be modified as per the requirements of other manufacturing systems to predict their PMs.

Scheduling in Flexible Manufacturing Systems (FMSs) is an important area of research as it significantly affects performance of the systems. In scheduling problems, determination of an appropriate order for jobs to be processed on a machine is a difficult task and to solve such problems, job priority rules (JPRs) are used. Several JPRs have been developed with an aim to obtain better performance, measured in terms of one or more scheduling performance measures (SPMs). However, selection of an appropriate rule is still an area of research as no single rule provides better results for all SPMs considered simultaneously. This work proposes a framework which is based on an integration of simulation and multi criteria decision making (MCDM) methods for the selection of an appropriate JPR yielding optimum results for multiple SPMs taken together. The proposed framework includes development of a simulation model to collect values of the SPMs corresponding to different JPRs. Further, five MCDM methods have been used to determine rank of the JPRs. Since different MCDM methods produce different ranking result therefore, the final rank of the JPRs has been determined by comparing the rank derived from these methods using membership degree method. To exemplify the probable application of the proposed framework, it has been implemented on a specific FMS taken from the literature in order to select the best JPR.

To survive in the ever-increasing competition in the market, manufacturing firms must adopt the latest technologies available. Flexible manufacturing systems (FMS) provide an effective solution to the problem, but its performance should be continually enhanced to remain competitive in the market. The aim of this work was to identify the effect of routing flexibility and system load conditions on the performance of FMS using both production and inventory related performance measures. ARENA simulation software was used to model and study the system. (R, r) inventory control policy was applied to the production/inventory system. Routing flexibility was found to have a significant effect on all the performance parameters, with maximum improvement of performance observed at increase of level from 0 to 1. It was further observed that the system load condition does not have a significant effect on any of the performance measure. The FMS showed slightly improved performance when the system was fully balanced. It can be concluded that the inventory related performance measures give comparable results to that of production related performance measures.

This chapter presents an exploratory study based on an industrial survey concerning the status of machining industries in Aligarh, Uttar Pradesh, India, which is known for its lock and hardware industry worldwide. The survey was performed on 50 manufacturing firms that are directly involved with basic machining processes. The developed questionnaire dealt with four fundamental concerns. The first concern is the machining industry's current status in terms of employed workpieces, machine tools, cutting fluids, etc., and the type of products from each. The second question concerns the methods/technologies used at their shop floor like cutting fluid application methods, automation levels, quality control measures/practices, etc. And the last concern is related to the problems associated with the existing facilities and future initiatives to be taken to better the firm's overall performance. Survey responses showed that most respondents are engaged with conventional processes, machine tools, cutting fluids, etc. Furthermore, the conventional application method of cutting fluids resulted in wastage, economic loss and health, and environmental concerns. The response from future initiatives/investments showed that introducing new cutting tool materials, automation and quality control practices, new cutting fluid and its application method, and quality control practices are the most common future developments opted by manufacturers. Lastly, sustainable-based solutions and discussions have been made from available peer works.

Choosing a suitable supplier of materials and equipment in the healthcare sector is essential because it directly affects the patient's health and the organization's effectiveness and quality of services. Moreover, studies on supplier ranking are few in the field of health, particularly in the dental sector. For this purpose, an integrated fuzzy (AHP-TOPSIS) model has been developed for supplier ranking in the dental sector. The F-AHP is used to evaluate the importance of criteria, and then the F-TOPSIS method is applied to the supplier ranking process. A real case study is conducted on dental composite filling suppliers. Six evaluation criteria are identified, and five potential suppliers are selected through a direct interview with a group of experts. Then a questionnaire is applied to 12 experts (dentists) to rate the importance of evaluation criteria and to evaluate suppliers based on evaluation criteria. Evaluating the importance of criteria using F-AHP indicated that quality is the most important criterion. It has a weight of 0.22, followed by esthetic and durability with a weight of 0.21 for both. Moreover, the result of ranking using F-TOPSIS indicated that supplier A5 is the top supplier with a value of (0.532).

The advancement of modern technology, specifically speaking Industry 3.0 and 4.0 has brought enormous amount of changes to the manufacturing sector. It has resulted in a very high automation of existing manufacturing sectors. Flexible Manufacturing Systems (FMS) have become the backbone of the modern industries due to this technological advancement. However, the complexity of the FMS requires a system to be developed to analyze and design complex manufacturing systems. This paper attempts to find a solution to this problem. Simulation is a powerful tool that is used to study and analyze the system performance. Numerous works has been done by various researchers considering various types of flexibilities. The main aim of this study is to develop a demonstrative model of Flexible Manufacturing System (FMS) using ARENA simulation software and to study the effect of operations flexibility on the performance of FMS. One of the reasons to select the operations flexibility for the study is due to very limited work done for this flexibility type. Based on the selected configurations, the conceptual and simulation models and their respective animations have been developed by ARENA Software. To verify the simulation results, multi-criteria decision making techniques viz. Grey Relational analysis (GRA) is used. Keywords Flexible Manufacturing Systems, Simulation, Operations Flexibility, Grey Relational analysis

The main objective of this paper is to propose a structured model for evaluating different Flexible Manufacturing System (FMS) using the Analytic Hierarchy Process (AHP). The paper aims to demonstrate how the model can help in taking correct decisions regarding the type of manufacturing flexibility needed for best FMS. This is because huge amount of money is invested to implement FMS in the industry. Hence, managers need to take judicious decision regarding the type and level of manufacturing flexibility. There are a number of manufacturing flexibility being reported in the literature. In this paper the most fundamental types i.e., machine, routing and product flexibility are examined. The AHP is aimed at integrating different measures into a single overall score for ranking decision alternatives. Its main characteristic is that it is base on pairwise comparison judgment. A usability evaluation of the AHP based model of FMS along with structure of the hierarchy is developed. The framework that is used in this example could serve as one of the tools for making a strategic decision. The effectiveness of our model is demonstrated through numerical examples.

Steel is the most commonly employed material in various engineering applications, and their successful machining demands finding the optimized set of machining parameters along with appropriate cooling strategies. Moreover, the significance of process parameter optimization is progressively perceived in the wake of expensive CNC machine adaptation on the shop floor for machining. Further, a competent cooling strategy is essential with a minimal amount of coolant to obtain the best quality products. In the present work, the optimization of process parameters for Near Dry Turning (NDT) of two steel grades, EN8 and EN31, was done. NDT utilizes a minimal coolant with a major amount of compressed air. For competent cooling, Al 2 O 3 nanofluid as coolant was used with compressed air. Speed, feed, and depth of cut were taken as the machining parameters for the turning process. Two response variables, the surface roughness of machined specimen and cutting zone temperature, were considered for the analysis. Three levels of each turning parameter were chosen, and the Taguchi L9 orthogonal array was adopted for the experimentation. The optimized turning parameter was found through the Grey Relational Analysis (GRA). Further, the applicability of compressed air was also presented to achieve sustainable and green machining to eliminate the negative impact on environmental footprints. For this purpose, results at the obtained optimized set of parameters were compared with plain base fluid and compressed dry air as coolants. The reduction in surface roughness of ∼12.3% and ∼14.6% for EN8 and EN31 steel were observed using nanofluid in near dry turning. Similarly, the reduction in cutting zone temperature was ∼7% in both cases. These results show the significance of process parameter optimization and the applicability of nanofluid in near dry turning of steels.

Flexible manufacturing systems (FMSs) help in producing reliable and good-quality products. The performance of FMSs depends on the various input variables and therefore, it is important to investigate their effect on the performance of the system. This work explores the effect of three input variables i.e., newly developed sequencing rules (SR), the number of machines, and AGV selection rules to determine their optimal setting that yields optimal results of three performance measures viz. makespan time, AGV utilization, and machine utilization simultaneously. ARENA simulation software was used to develop a replicate model of an FMS problem taken from literature. Nine models for a different combination of variables as input as per Taguchi's L9 orthogonal array were developed and data for three performance measures were collected. Further, multi-response optimization using the integrated MEREC-MARCOS method was performed. The results of the study revealed that considering 3 machines, with GMPD as SR and shortest distance as AGV selection rule yields the optimal performance of the FMS. Further, among the three input variables, the number of machines was found to have a significant effect on the multi-performance measures. Finally, the optimal solution obtained in the study was validated by performing a confirmation test.

This work proposes two new priority dispatching rules (PDRs) for solving single machine scheduling problems. These rules are based on the geometric mean (GM) and harmonic mean (HM) of the processing time (PT) and the due date (DD) and they are referred to as GMPD and HMPD respectively. Performance of the proposed PDRs is evaluated on the basis of five measures/criteria i.e. Total Flow Time (TFT), Total Lateness (TL), Number of Late Jobs (TNL), Total Earliness (TE) and Number of Early Parts (TNE). It is found that GMPD performs better than other PDRs in achieving optimal values of multiple performance measures. Further, effect of variation in the weight assigned to PT and DD on the combined performance of TFT and TL is also examined which reveals that for deriving optimal values of TFT and TL, weighted harmonic mean (WHMPD) rule with a weight of 0.105 outperforms other PDRs. The weighted geometric mean (WGMPD) rule with a weight of 0.37 is found to be the next after WHMPD followed by the weighted PDT i.e. WPDT rule with a weight of 0.76.

Many research works focused on using vegetable oils alone as cutting fluid and some with nanoparticles for better lubrication. But very few literature pieces are available for the use of environment-friendly nanoparticles and vegetable oils in machining. Also, the nanoparticles generally used for the research is expensive. With this paper’s help, some alternatives to conventional machining fluids are exposed through the formulation and analysis of nanofluids, which are economically feasible, effective lubricants, and environmentally friendly. For this, vegetable oils such as palm oil, peanut oil, sunflower oil, coconut oil, and castor oil are used to turn AISI 52100 (EN31) steels using tungsten carbide insert (VNMG160408) under the environment of a minimum quantity of lubrication. A newly developed environment-friendly metal cutting nanofluids of three base fluids is prepared with three zinc oxide concentrations. The most suitable nanoparticle inclusion ratio for nanofluid preparation is determined after machining at fixed levels of machining parameters.Further experiments and analyses are carried out based on Taguchi’s L9 orthogonal array to identify the appropriate cutting parameters for optimization. A considerable reduction in surface roughness and the cutting temperature are achieved using an environmentally-friendly metal cutting nanofluids instead of the base fluids. A maximum reduction in surface roughness of about 57% is obtained for EN31 using palm oil-based metal cutting nanofluids with 0.125% of the nanoparticle. About 13% drop in cutting temperature is observed using sunflower and groundnut based metal cutting nanofluids, both with 0.125% concentration. Results also indicate that the environment-friendly metal cutting nanofluids used in the experiment can replace the conventional fluids used in most metal cutting industries.