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The global impact of the ongoing COVID-19 pandemic, while somewhat contained, remains a critical challenge that has tested the resilience of humanity. Accurate and timely prediction of COVID-19 transmission dynamics and future trends is essential for informed decision-making in public health. Deep learning and mathematical models have emerged as promising tools, yet concerns regarding accuracy persist. This research suggests a novel model for forecasting the COVID-19’s future trajectory. The model combines the benefits of machine learning models and mathematical models. The SIRVD model, a mathematical based model that depicts the reach of the infection via population, serves as basis for the proposed model. A deep prediction model for COVID-19 using XGBoost-SIRVD-LSTM is presented. The suggested approach combines Susceptible-Infected-Recovered-Vaccinated-Deceased (SIRVD), and a deep learning model, which includes Long Short-Term Memory (LSTM) and other prediction models, including feature selection using XGBoost method. The model keeps track of changes in each group’s membership over time. To increase the SIRVD model’s accuracy, machine learning is applied. The key properties for forecasting the spread of the infection are found using a method called feature selection. Then, in order to learn from these features and create predictions, a model involving deep learning is applied. The performance of the model proposed was assessed with prediction metrics such as R2, root mean square error (RMSE), mean absolute percentage error (MAPE), and normalized root mean square error (NRMSE). The results are also validated to those of other prediction models. The empirical results show that the suggested model outperforms similar models. Findings suggest its potential as a valuable tool for pandemic management and public health decision-making.

Human life has become smarter by utilizing big data, telecommunication technologies, and wearable sensors over pervasive computing to give better healthcare services. Big data is built with the possibility to improve the healthcare industry. Big data makes the interconnection between patients, wearable sensors, healthcare caregivers, and providers through the utilization of Information and Communication Technology (ICT) and software. Most of the economic challenges in developing countries are caused by the healthcare sector, which occurs predominantly due to the increasing population requiring more quality of care concerning older people. Older people need great attention and care as they lead with irreparable damages when a minor accident or insignificant disease occurs. Therefore, the necessity of implementing new technologies and tools has arisen to support senior citizens regarding their healthcare. Various advancements in wireless technology, miniaturization, computing power, and processing made diverse healthcare innovations that led to developing the connected medical devices. Hence, this proposal develops a new healthcare monitoring system for tracking the activities of elderly people, where the Hadoop MapReduce technique for parallel processing the large-sized data. The data collected as mentioned in the available datasets is performed by using the numerous wearable sensors fixed on the “subject’s left ankle, right arm, and chest” that are transformed to the cloud platform and also to the data analytics layer according to the Internet of Medical Things (IoMT) devices. The given input undergoes data splitting to produce tiny chunks. These small chunks of the input files are then considered as Map tasks. Here, in the map phase, the features are optimally selected by the Hybrid Dingo Coyote Optimization (HDCO). The combiner phase classifies the physical activities using the developed Deep Ensemble Learning (DEL) consisting of classifiers such as “Extreme Learning Machine (ELM), deep Convolutional Neural Network (CNN), Long short-term memory (LSTM), Deep Belief Network (DBN), and Deep Neural Network (DNN)”. The parameter tuning in these classifiers is done by the same HDCO. The reducer phase extracts data from different chunks by merging the same classes. The developed HDCO-DEL has secured 13.66%, 16.01%, 17.33%, 13.6%, and 14.01% better accuracy than ELM, CNN, LSTM, DBN, DNN, and HealthFog, respectively on second dataset. The comparison with existing methods shows its better performance and also predicts physical activities with overall high accuracy.

One of the problems encountered in the design and implementation of a serial production line (SPL) is the buffer size between the machine tools. The buffer size of the SPL has an important impact on the productivity of the whole production system. The machine tools’ characteristics including their uptimes and downtimes and the process parameters are the main factors that affect the decision regarding the buffer size, and thus the productivity of the SPL. Due to the dynamic nature of this problem, it is complex to find the optimal buffer size in SPL. Thus, in this paper, an Efficient Prediction Model (EPM) is developed using Artificial Neural Network (ANN). The purpose of the developed EPM is to find the buffer size between each succeeding pair of machine tools in SPL at any given uptimes and downtimes of machine tools. An optimization model based on genetic algorithms (GA) is used to generate the learning data for the prediction model to find the optimal or near optimal buffer size of the bay of each machine tool in SPL. The proposed approach integrates the optimization and prediction methodologies to evaluate, and predict the optimal buffer sizes for maximum productivity. Including uptime and downtime parameters enable the proposed method to be used to improve the design of running SPL as well as to design a new SPL. Numerical examples for five and fifteen machine tools were conducted independently in this research and the results show the ability of the proposed method to determine the optimal buffer sizes in a reasonable amount of time. In particular, the results of case studies show that the developed model accurately predict the optimal buffer size, especially for the case of five machines and even for a higher number of machine tools yet with acceptable but less accuracy. Finally, the performance of the proposed approach was compared with some results of the state of the art methods reported in the literature. The comparison shows the superiority of the present approach to identify buffer sizes for higher throughput under the same uptimes and downtimes.

Dispatching rules are generally useful for scheduling jobs in flexible manufacturing systems (FMSs). However, the appropriateness of these rules relies heavily on the condition of the system; thus, there is no single rule that always outperforms others. In this state of affairs, diverse machine-learning technology offers an effective approach for dynamic scheduling, as it allows managers to identify the most suitable rule at each moment. Nonetheless, various machine-learning algorithms may provide diverse recommendations. The main objective of this study is to implement FMS scheduling using intelligent hybrid learning algorithms with metaheuristic improvements. The developed model involves three phases: feature extraction, optimal weighted feature extraction, and prediction. After the benchmark datasets for the FMS are gathered, feature extraction is performed using t-distributed stochastic neighbor embedding, linear discriminant analysis, linear square regression, and higher-order statistical features. Further, an optimal weighted feature extraction method is developed to select the optimal features with less correlation using the improved lion algorithm (LA), which is called the modified nomadic-based LA (MN-LA). Finally, the optimally selected weighted features are subjected to a hybrid learning algorithm with the integration of a fuzzy classifier and a deep belief network (DBN). For improving the prediction model, the membership function of the fuzzy classifier is optimized using the proposed MN-LA. Moreover, the activation function and the number of hidden neurons in the DBN are optimized using the MN-LA. The main objective of the optimized hybrid classifier is to enhance prediction accuracy. The experimental results indicate the effectiveness of the proposed heuristic-based scheduling method for FMSs.

Sub-surface damage during machining of aluminum-based metal matrix composites (MMCs) has been modeled using finite element models. These models are based on reinforcement particles size and volume fractions and particles are distributed uniformly in the metal matrix. In order to simulate particle debonding cohesive zone elements (CZE) have been incorporated along the parting line. In addition, failure criteria based on brittle fracture have been added for ceramic particles to simulate particle fracture. To reduce computational time and simplify the model both CZE and particle fracture is limited to the reinforced particles along the parting lines facing the tip of the cutting tool. The damage depth beneath the machined surface is measured by using the non-zero plastic strain values in the equivalent plastic strain contours obtained from the FE models. The results were compared against published experimental data and found to be in good agreement.

This paper derives an iterative deadlock prevention policy for systems of simple sequential processes with resources (S3PRs) based on structural analysis, which consists of two stages. The first stage is called siphons control. Strict minimal siphons (SMSs) in an S3PR net are computed and control places are added by imposing P-invariants associated with the complementary sets of the SMSs, which restricts no legal system behavior. The original resource places are removed and the newly added control places are regarded as resource places, resulting in a new net, which needs to add control places for its SMSs if deadlocks persist. Repeat this step until a new net without SMSs is obtained. Then, an S4PR, called the first-controlled net, is obtained by integrating all added control places into the original net. The second stage, called non-max-marked siphons control, is performed in an iterative way if the system is not live yet. At each iteration, a mixed integer linear programming (MILP) problem is formulated to compute a non-max-marked siphon, and a control place is added for the siphon to the first-controlled net, resulting in an augmented net. The iteration is executed until a final-augmented net generates no new non-max-marked siphon. In general, based on the above two stages, this paper can obtain a supervisor with more behavior permissiveness compared to the previous studies. Moreover, an optimal supervisor can be found if a first-controlled net has no non-max-marked siphon, implying that the second stage is not necessary. Finally, some examples are provided to demonstrate the proposed policy.

A deluge of approaches has been proposed to implement and deploy a computationally effective and maximally permissive liveness-enforcing supervisor for sequential resource allocation systems (RASs). However, they are stalled by the computational complexity of a large state space that grows exponentially with respect to the size of an underlying RAS. The attention of this work is restricted to a special class of RASs: Disjunctive/Single-unit (D/SU)-RASs. Given an initial resource configuration of a D/SU-RAS, a complete state enumeration can be obtained through pure algebraic operations on this configuration with the pre-computed initial basis state space. When an explicit and complete enumeration of reachable states is impossible (due to the limited memory and storage space), we propose the exact number of reachable states. In this case, given a state vector, its reachability can be decided by an algebraic method. Experimental studies demonstrate the efficiency of the proposed approach.

Worldwide manufacturing and or service organizations aim for continuous elimination of waste of all types. But, these organizations lack in waste management and its evaluation. The paper addresses a case study of medium scale manufacturing organization in Saudi Arabia, which emphasizes the implementation of lean principles to improvise waste in the manufacturing plant. A multi criteria lean performance score (MCLPS) approach is proposed. The MCLPS approach analyses effectiveness of the manufacturing strategies, and identifies the potential opportunities for future improvement. It is observed that implementation of lean strategies improve plant utilization from 62.72% to 67%; inventory turnover ratio from 22.2 to 25.78 times per six months; wastage of material dropped by 536 bottles of material per startup activity; and non-value added time reduced by 5.84%. Similarly with help of MCLPS approach the management measures leanness of a manufacturing organization, which helps to track and monitor step by step desired improvements in the present manufacturing systems. For the improved manufacturing system it is evident that multi-criteria lean performance score is improved by 34%.

The problem of state-space explosion and state reachability decision has been a focus in Petri net analysis. In this article, some algorithms based on data features of state space are proposed for state-space reduction and reachability analysis in Petri nets. A caching arrangement algorithm for the state reachability graph is proposed to alleviate the memory overhead when all states are generated by a traditional reachability graph generation algorithm. For the reduction of the state space, a compression algorithm based on a hybrid coding is formulated. The changing characteristics of state elements are fully utilized to realize a sharp reduction of the whole state space. For the state reachability analysis, a state reachability retrieval algorithm based on the locally sensitive hashing is reported. In this algorithm, all the states in the compressed state space are distributed to different hash buckets according to the distances among the states, which realize the primary state retrieval efficiently. Then, a state retrieval strategy that reverts the primary retrieved state to its original data is adopted. Finally, the proposed compression and retrieval algorithms are validated on several Petri net models, showing high compression efficiency and exact accuracy of state retrieval.

A tube is an important structural element for fluid manipulation in piped networks in many industries. Tube branching is achieved using tube fittings of various shapes, including T, Y, X, and L shapes. This study proposes a new innovative technique to produce T-shaped tubular fittings. The technique uses a specially designed die setup where a tube is placed inside a T-shaped die cavity and a metallic insert is used to deform the tube into the cavity, creating the T-fitting shape. Experimental and numerical methods are used to evaluate the process. The main outcome of this research is the successful creation of T-shaped copper tube fittings using a technique similar to tube hydroforming without the need for internal pressure. This technique could be modified to assist the production of T-fittings with thicknesses outside the hydroforming limits.

Titanium alloy is widely used in modern automobile industries due to its higher strength with corrosion resistance. Such higher strength materials can be effectively machined using unconventional machining processes, especially the electro-chemical micro machining (ECMM) process. It is important to enhance the machining process by investigating the effects of electrolytes and process parameters in ECMM. The presented work describes the influence of three different combinations of Sodium Chloride-based electrolytes on machining Titanium (Ti-6Al-4V) alloy. Based on the ECMM process parameters such as applied voltage, electrolytic concentration, frequency and duty cycle on response, characteristics are determined by the Taguchi design of experiments. The highest material removal rate (MRR) was achieved by the Sodium Chloride and Sodium Nitrate electrolyte. The combination of Sodium Chloride and Citric Acid achieve highest Overcut and Circularity. The optimal overcut was observed from the Sodium Chloride and Glycerol electrolyte due to the presence of glycerol. The better conicity was obtained from Sodium Chloride and Citric Acid in comparison with other electrolytes. A Sodium Chloride and Glycerol combination could generate better machined surface owing to the chelating effect of Glycerol.

Thin structures are often required for several engineering applications. Although thick sections are relatively easy to produce, the cutting of thin sections poses greater challenges, particularly in the case of thermal machining processes. The level of difficulty is increased if the thin sections are of larger lengths and heights. In this study, high-aspect-ratio thin structures of micrometer thickness (117–500 µm) were fabricated from D2 steel through wire electrical discharge machining. Machining conditions were kept constant, whereas the structure (fins) sizes were varied in terms of fin thickness (FT), fin height (FH), and fin length (FL). The effects of variation in FT, FH, and FL were assessed over the machining errors (FT and FL errors) and structure formation and its quality. Experiments were conducted in a phased manner (four phases) to determine the minimum possible FT and maximum possible FL that could be achieved without compromising the shape of the structure (straight and uniform cross-section). Thin structures of smaller lengths (1–2 mm long) can be fabricated easily, but, as the length exceeds 2 mm, the structure formation loses its shape integrity and the structure becomes broken, deflected, or deflected and merged at the apex point of the fins.

Technological advancements in healthcare influence medical practitioners as much as they impact the routine lives of the patients. The mandible reconstruction, which constitutes an important branch in facioplasty, has been a challenging task for medical professionals. As part of scientific innovation, tailor-made implants are valuable for sustaining and regenerating facial anatomy, as well as preserving the natural appearance. The challenge of choosing an acceptable implant design is a tedious process due to the growing number of designs with conspicuous effectiveness. The design should be agreeable, easy-to-design, sustainable, cost-effective, and undemanding for manufacturing. The optimal implant design can efficiently and effectively recover the structure and morphology of the flawed region. Evidently, among the many variants, the choice of appropriate design is one of the prevalent implant design problems and is still under consideration in most studies. This work is focused on the multiattribute decision-making (MCDM) approach to choosing the most effective implant design. The prevalence of subjectivity in decision-making and the presence of inconsistency from multiple sources emphasize the strategies that must take ambiguity and vagueness into account. An integrated MCDM methodology, assimilating two modern and popular techniques is adopted in this work. The preferred approach implements the Fuzzy Analytical Hierarchy Process based on the trapezoidal fuzzy number to extract the criteria weights in decision mapping and the Technique for Order of Preference by Similarity to Ideal Solution and VIKOR to assess design choices. A two-stage mechanism is the cornerstone of the established methodology. The first stage analyses the criteria from the point of view of the designer, the context of fabrication, and consumer experience. The second stage identifies the most viable and feasible design. The procedure applied in this analysis can be considered to choose the optimal implant design and to decide on areas of improvement that ensure greater patient experience.

Data classification has been considered extensively in different fields, such as machine learning, artificial intelligence, pattern recognition, and data mining, and the expansion of classification has yielded immense achievements. The automatic classification of maintenance data has been investigated over the past few decades owing to its usefulness in construction and facility management. To utilize automated data classification in the maintenance field, a data classification model is implemented in this study based on the analysis of different mechanical maintenance data. The developed model involves four main steps: (a) data acquisition, (b) feature extraction, (c) feature selection, and (d) classification. During data acquisition, four types of dataset are collected from the benchmark Google datasets. The attributes of each dataset are further processed for classification. Principal component analysis and first-order and second-order statistical features are computed during the feature extraction process. To reduce the dimensions of the features for error-free classification, feature selection was performed. The hybridization of two algorithms, the Whale Optimization Algorithm (WOA) and Spotted Hyena Optimization (SHO), tends to produce a new algorithm—i.e., a Spotted Hyena-based Whale Optimization Algorithm (SH-WOA), which is adopted for performing feature selection. The selected features are subjected to a deep learning algorithm called Recurrent Neural Network (RNN). To enhance the efficiency of conventional RNNs, the number of hidden neurons in an RNN is optimized using the developed SH-WOA. Finally, the efficacy of the proposed model is verified utilizing the entire dataset. Experimental results show that the developed model can effectively solve uncertain data classification, which minimizes the execution time and enhances efficiency.

Finite element (FE) models and the multi objective genetic algorithm (MOGA-II) have been applied for tool performance optimization while machining aluminum-based metal matrix composites. The developed and verified FE models are utilized to generate data for the full factorial design of experiment (DOE) plan. The FE models consist of a heterogenous workpiece, which assumes uniform distribution of reinforced particles according to size and volume fraction. Cutting forces, chip morphology, temperature contours, stress distributions in the workpiece and tool by altering cutting speed, feed rate, and reinforcement particle size can be estimated using developed FE models. The DOE data are then utilized to develop response surfaces using radial basis functions. To reduce computational time, these response surfaces are used as solver for optimization runs using MOGA-II. Tool performance has been optimized with regard to cutting temperatures and stresses while setting a limit on specific cutting energy. Optimal solutions are found with low cutting speed and moderate feed rates for each particle size metal matrix composite (MMC).

Iron−nickel based ultra-high strength steel (wt. 18~20% Ni) is characterized by its high strength and low thermal conductivity, and is normally used to make key components by forming and machining processes. The optimization of these processes is based on a deep understanding of the mechanical and dynamic behaviors under high strain, high strain rate, and high temperature. In this paper, the relationship of stress to strain, strain rate, and temperature is systematically investigated by the dynamic compression tests combined with quasi-static compression tests, and the hardening and softening is associated with the transformation in microstructures. According to the analysis, dynamic recrystallization around 600 °C is assumed to be one important influencing factor, hence hot deformation equations are established and the critical strain for dynamic recrystallization and the volume fraction of the dynamic recrystallized grains are defined.

Reconstruction of zygomatic complex defects is a surgical challenge, owing to the accurate restoration of structural symmetry as well as facial projection. Generally, there are many available techniques for zygomatic reconstruction, but they hardly achieve aesthetic and functional properties. To our knowledge, there is no such study on zygomatic titanium bone reconstruction, which involves the complete steps from patient computed tomography scan to the fabrication of titanium zygomatic implant and evaluation of implant accuracy. The objective of this study is to propose an integrated system methodology for the reconstruction of complex zygomatic bony defects using titanium comprising several steps, right from the patient scan to implant fabrication while maintaining proper aesthetic and facial symmetry. The integrated system methodology involves computer-assisted implant design based on the patient computed tomography data, the implant fitting accuracy using three-dimensional comparison techniques, finite element analysis to investigate the biomechanical behavior under loading conditions, and finally titanium fabrication of the zygomatic implant using state-of-the-art electron beam melting technology. The resulting titanium implant has a superior aesthetic appearance and preferable biocompatibility. The customized mirrored implant accurately fit on the defective area and restored the tumor region with inconsequential inconsistency. Moreover, the outcome from the two-dimensional analysis provided a good accuracy within 2 mm as established through physical prototyping. Thus, the designed implant produced faultless fitting, favorable symmetry, and satisfying aesthetics. The simulation results also demonstrated the load resistant ability of the implant with max stress within 1.76 MPa. Certainly, the mirrored and electron beam melted titanium implant can be considered as the practical alternative for a bone substitute of complex zygomatic reconstruction.

The quality of micro-features in various technologies is mostly affected by the choice of the micro-fabrication technique, which in turn results in several limitations with regard to materials, productivity, and cost. Laser beam micro-machining has a distinct edge over other non-traditional methods in terms of material choices, precision, shape complexity, and surface integrity. This study investigates the effect of laser fluence and pulse overlap while developing microchannels in alumina ceramic using an neodymium-doped yttrium aluminum garnet (Nd:YAG) laser. Microchannels 200 µm wide with different depths were machined using different laser peak fluence and pulse overlap (percentage of overlap between successive laser pulses) values. It was found that high pulse overlaps and fluences should be avoided as they give rise to V-shaped microchannels i.e., 100% bottom width errors. The optimal peak fluence range was found to be around 125−130 J/cm2 corresponding to 3−5 µm depth per scan. In addition, channels fabricated with moderate pulse overlap were found to be of good quality compared to low pulse overlaps.

Electron beam melting (EBM) technology is a novel additive manufacturing (AM) technique, which uses computer controlled electron beams to create fully dense three-dimensional objects from metal powder. It gives the ability to produce any complex parts directly from a computer aided design (CAD) model without tools and dies, and with variety of materials. However, it is reported that EBM has limitations in building overhang structures, due to the poor thermal conductivity for the sintered powder particles under overhang surfaces. In the current study, 2D thermo-mechanical finite element models (FEM) are developed to predict the stresses and deformation associated with fabrication of overhang structures by EBM for Ti-6Al-4V alloy. Different support structure geometries are modeled and evaluated. Finally, the numerical results are validated by experimental work.

A custom made implant is critical in cranioplasty to cushion and restore intracranial anatomy, as well as to recover the appearance and attain cognitive stability in the patient. The utilization of customized titanium alloy implants using three-dimensional (3D) reconstruction technique and fabricated using Electron Beam Melting (EBM) has gained significant recognition in recent years, owing to their convenience and effectiveness. Besides, the conventional technique or the extant practice of transforming the standard plates is unreliable, arduous and tedious. As a result, this work aims to produce a customized cranial implant using 3D reconstruction that is reliable in terms of fitting accuracy, appearance, mechanical strength, and consistent material composition. A well-defined methodology initiating from EBM fabrication to final validation has been outlined in this work. The custom design of the implant was carried out by mirror reconstruction of the skull’s defective region, acquired through computer tomography. The design of the customized implant was then analyzed for mechanical stresses by applying finite element analysis. Consequently, the 3D model of the implant was fabricated from Ti6Al4V ELI powder with a thickness of ≃1.76−2 mm. Different tests were employed to evaluate the bio-mechanical stability and strength of the fabricated customized implant design. A 3D comparison study was performed to ensure there was anatomical accuracy, as well as to maintain gratifying aesthetics. The bio-mechanical analysis results revealed that the maximum Von Mises stress (2.5 MPa), strain distribution (1.49 × 10−4) and deformation (3.26 × 10−6 mm) were significantly low in magnitude, thus proving the implant load resistance ability. The average yield and tensile strengths for the fabricated Ti6Al4V ELI EBM specimen were found to be 825 MPa and 880 MPa, respectively, which were well over the prescribed strength for Ti6Al4V ELI implant material. The hardness study also resulted in an acceptable outcome within the acceptable range of 30−35 HRC. Certainly, the chemical composition of the fabricated EBM specimen was intact as established in EDX analysis. The weight of the cranial implant (128 grams) was also in agreement with substituted defected bone portion, ruling out any stress shielding effect. With the proposed approach, the anatomy of the cranium deformities can be retrieved effectively and efficiently. The implementation of 3D reconstruction techniques can conveniently reduce tedious alterations in the implant design and subsequent errors. It can be a valuable and reliable approach to enhance implant fitting, stability, and strength.

Segmental mandibular reconstruction has been a challenge for medical practitioners, despite significant advances in medical technology. There is a recent trend in relation to customized implants, made up of porous structures. These lightweight prosthesis scaffolds present a new direction in the evolution of mandibular restoration. Indeed, the design and properties of porous implants for mandibular reconstruction should be able to recover the anatomy and contour of the missing region as well as restore the functions, including mastication, swallowing, etc. In this work, two different designs for customized prosthesis scaffold have been assessed for mandibular continuity. These designs have been evaluated for functional and aesthetic aspects along with effective osseointegration. The two designs classified as top and bottom porous plate and inner porous plate were designed and realized through the integration of imaging technology (computer tomography), processing software and additive manufacturing (Electron Beam Melting). In addition, the proposed designs for prosthesis scaffolds were analyzed for their biomechanical properties, structural integrity, fitting accuracy and heaviness. The simulation of biomechanical activity revealed that the scaffold with top and bottom porous plate design inherited lower Von Mises stress (214.77 MPa) as compared to scaffold design with inner porous plate design (360.22 MPa). Moreover, the top and bottom porous plate design resulted in a better fit with an average deviation of 0.8274 mm and its structure was more efficiently interconnected through the network of channels without any cracks or powder material. Verily, this study has demonstrated the feasibility and effectiveness of the customized porous titanium implants in mandibular reconstruction. Notice that the design and formation of the porous implant play a crucial role in restoring the desired mandibular performance.

Optimizing flexible routing in flexible manufacturing systems is one of the aspects that increase the efficiency of flexible manufacturing systems especially in dynamic environment systems. This article presents a multistage approach to solve flexible routing problem in flexible manufacturing systems. Multistage approach includes three stages; the first stage is a production simulation system to find the fitness of the flexible manufacturing systems corresponding to different products’ routes’ groups. The second stage proposes an artificial neural network approach to predict the products’ routes’ group in flexible manufacturing systems. The last stage combines genetic algorithms and artificial neural network to optimize proper routes for all product types in flexible manufacturing systems. Multistage approach proposed in this study aims to reduce the computational time required to obtain and optimize the flexible routes in flexible manufacturing systems. The results of this study show that the artificial neural network can be used efficiently to predict the flexible routes in flexible manufacturing systems and it reduces the computational time for routes’ optimization required with production simulation system. This characteristic improves the flexibility of flexible manufacturing systems since it can be adapted frequently against any change in production ratios.

This study aims to perform comparative analyses in modeling serrated chip morphologies using traditional finite element and smoothed particles hydrodynamics methods. Although finite element models are being employed in predicting machining performance variables for the last two decades, many drawbacks and limitations exist with the current finite element models. The problems like excessive mesh distortions, high numerical cost of adaptive meshing techniques, and need of geometric chip separation criteria hinder its practical implementation in metal cutting industries. In this study, a mesh free method, namely, smoothed particles hydrodynamics, is implemented for modeling serrated chip morphology while machining AISI H13 hardened tool steel. The smoothed particles hydrodynamics models are compared with the traditional finite element models, and it has been found that the smoothed particles hydrodynamics models have good capabilities in handling large distortions and do not need any geometric or mesh-based chip separation criterion.

Ceramic microchannels have important applications in different microscale systems like microreactors, microfluidic devices and microchemical systems. However, ceramics are considered difficult to manufacture owing to their wear and heat resistance capabilities. In this study, microchannels are developed in alumina ceramic using direct Nd:YAG laser writing. The laser beam with a characteristic pulse width of 10 µs and a beam spot diameter of 30 µm is used to make 200 µm width microchannels with different depths. The effects of laser beam intensity and pulse overlaps on dimensional accuracy and material removal rate have been investigated using different scanning patterns. It is found that beam intensity has a major influence on dimensional accuracy and material removal rate. Optimum parameter settings are found using grey relational grade analysis. It is concluded that low intensity and low to medium pulse overlap should be used for better dimensional accuracy. This study facilitates further understanding of laser material interaction for different process parameters and presents optimum laser process parameters for the fabrication of microchannel in alumina ceramic.

The proper positioning of machine tools in flexible manufacturing system is one of the factors that lead to increase in production efficiency. Choosing the optimum position of machine tools curtails the total part handling cost between machine tools within the flexible manufacturing system. In this article, a two-stage approach is presented to investigate the best locations of the machine tools in flexible manufacturing system. The location of each machine tool is selected from the available specific and fixed locations in such a way that it will result in best throughput of the flexible manufacturing system. In the first stage of the two-stage approach, the throughput of randomly selected locations of the machine tool in flexible manufacturing system is computed by proposing a production simulation system. The production simulation system utilizes genetic algorithms to find the locations of the machine tools in flexible manufacturing system that achieve the maximum throughput of the flexible manufacturing system. In the second stage, the generated locations are fed into artificial neural network to find a relation between a machine tool’s location and the throughput that can be used to predict the throughput for any other set of locations. Artificial neural network will result in mitigating the computational time.