Mathematical modeling of flexible printed circuit configuration: a study in deformation and optimization.

This manuscript offers an exhaustive analysis of Flexible Printed Circuits (FPCs), concentrating on enhancing their design to surmount two primary challenges. Firstly, it seeks to obviate contact with proximate components. Secondly, it aspires to adhere to pre-established curvature constraints. Predicated on the curvature properties of FPCs, we have developed a model adept at accurately forecasting FPC deformation under diverse conditions. Our inquiry entails a thorough examination of various FPC configurations, including bell, 'U', and 'S' shapes. Central to our methodology is the strategic optimization of FPC spatial arrangements, aiming to avert mechanical interference and control curvature, thus mitigating mechanical strain. This dual-faceted strategy is pivotal in enhancing the durability and operational reliability of FPCs, particularly in contexts demanding elevated flexibility and precision. Our research offers essential insights into the refinement of FPC design, skillfully addressing the complexities associated with curvature and physical interaction. Collectively, this study advocates a comprehensive framework for the design and implementation of FPCs, significantly advancing the field of contemporary electronics by ensuring these components meet the evolving demands of the industry.

Parents' view on the effect of online games on social interaction of adolescents with intellectual disability.

Background: Using games as a medium to master certain learning outcomes enables students to explore and understand the world around, which is important for students with intellectual disabilities. The purpose of this study was to gain insight into the potential effects of online games on social interaction of adolescents with intellectual disability, and how parents view these effects. Method: This study employed a quantitative approach using a quasi-experimental, pre-test and post-test and follow up design. Descriptive approach is also employed to investigate how parents view effects of online games on social interaction of adolescents with intellectual disability. To test the third hypothesis, qualitative method through interview was employed. 40 adolescents with mild ID aged between 15 and 18 years and who were enrolled in the ID Inclusion program, as well as their parent/guardian were recruited. Results: On the basis of the statistical analysis, It was observed a significant effect on adolescents with ID's social interaction capability. Looking at between-group differences, the groups were statistically significantly different in their levels of social interaction at the end of the intervention, with participants in the experimental group exhibiting significantly higher levels of social interaction in comparison to the controls. Conclusion: This study provides a potential support to the effectiveness of online games on social interaction of adolescents with intellectual disability. The main conclusion arising from these findings is the need to broaden the study of online games and their possible benefits to other variables than social interaction, such as cognitive variables and diverse populations, especially those with ID and autism.

A novel controllable capacitor commutation based superconducting hybrid direct current breaker.

Featuring low power loss and high reliability, voltage source converter medium voltage direct current (VSC-MVDC) systems have been widely employed for grid-tied renewable energy applications. To maintain high operational safety, circuit breakers are needed to isolate faulted powerlines by comprehensively considering response speed and installation cost. Research efforts have been put to realizing DC fault isolation by coordinating resistive type superconducting fault current limiter (R-SFCL) and integrated-gate-commutated-thyristor (IGCT) based hybrid DC circuit breaker. In this paper, a controllable current commutation based superconducting DC circuit breaker (CCCB-SDCCB) is proposed. By integrating R-SFCL with IGCT based hybrid DC circuit breakers, the current interrupting capacity can be greatly enlarged with the advantage of low cost and fast speed, and hence the overall cost for suppress large fault currents can be greatly reduced for MVDC systems. In addition, a new current injection circuit branch using H-bridge structure is designed to recycle the residual capacitor voltage from the previous fault stage to trigger the IGCTs without the capacitor pre-charging process. Simulation results show that the fault current can be successfully suppressed from 24.2 to 2.1 kA and fully interrupted within 4.11 ms by the proposed CCCB-SDCCB.

Nanomechanical, mechanical and microstructural characterization of electron beam welded Al2219-T6 tempered aerospace grade alloy: A comprehensive study.

As compared to traditional fusion welding processes, electron beam welding (EBW) is known to produce structurally robust microstructures and narrow heat-affected zone (HAZ) in metals. The process becomes more significant for the tempered alloys vulnerable to heat exposure. In the present investigation, Al 2219-T6 alloy was joined using the EBW process. The microstructural, mechanical, and nanomechanical characteristics of the resulting joint were investigated. EBW resulted in a narrow HAZ (22 µm) with a 430 mm fusion zone (FZ). A dendritic structure was observed in the FZ zone, while second-phase particles were absent indicating their dissolution during welding and interesting formation of Al2Cu mixture around the dendrites. The limited content of Cu in the base metal (BM) resulted in the formation of a solid solution in the FZ, along with the presence of fine equiaxed grains in the HAZ and equiaxed dendritic grains in the FZ zone. The X-ray diffraction analysis confirmed the absence of peaks corresponding to incoherent phases in the FZ. Compared to the BM, micro-hardness measurements revealed a 12.7 % increase in the hardness in the HAZ, while a significant decrease of approximately 19 % was observed in the FZ. The joint exhibited reduced tensile strength, ultimate strength by 42.2 %, and yield strength by 47.3 % when compared to the BM. The fracture analysis indicated a ductile failure mode with the presence of microvoids. Nano-indentation tests at various loads demonstrated a decrease in the nanohardness from the BM to the HAZ and FZ regions. Atomic force microscopy (AFM) analysis revealed significant pile-ups in the FZ, indicating the occurrence of plastic deformation during the welding process. The presented findings are valuable for the joint and structure design of Al -2219T6 alloy in particular and other Al alloys in general.

Introducing new green machining technology to enhance process performance and reduce environmental pollution in the metal processing industry.

The pursuit of enhanced cooling and lubrication methods for machining processes that are energy-efficient, environmentally friendly, and cost-effective is receiving significant attention from both academia and industry. The reduction of CO2 emissions is closely tied to electrical and embodied energy consumption. This study introduces a novel LN2 oil-on-water (LNOoW) cooling/lubrication (lubricooling) approach for the machining of Ti-6Al-4V alloy. Machinability aspects, energy-related aspects, environmental-related aspects, and economic aspects are measured and compared. More specifically, surface quality, electrical energy, cutting forces, and tool wear were measured in machinability aspects. Similarly, specific total energy and specific cumulative Energy Demand (S_CED), specific carbon emission, and production costs were measured to investigate the energy and environmental and economic aspects, respectively. The LNOoW provided the best machinability results compared with other approaches. Result found that LNOoW produced 37.5% better surface quality, removed 159.17% more material, and reduced 50.56% specific cutting energy and 53.63% specific costs as compared to traditional dry cutting conditions. The 39% increment in specific carbon emissions observed in the LN2 oil-on-water (LNOoW) approach in comparison to the dry-cutting method can be mitigated through the implementation of sustainable practices in the production of liquid nitrogen (LN2). The information provided in this study serves as a valuable resource for the development of environmentally friendly machining processes. The study also helps get the sustainable development goals (SDGs) of the United Nations.

Sustainable operations of a combined cycle power plant using artificial intelligence based power prediction.

Combined Cycle Power Plants (CCPP) are an effective method for Power generation due to their high thermal efficiency, low fuel consumption, and low greenhouse emissions. However, investing millions into building a power plant without knowledge of the power generation capacity seems unproductive. With the help of AI, we have tried to eliminate this conundrum. The present study focuses on the prediction of power produced by a 747 MW Combined Cycle Power Plant (CCPP) using a Back Propagation Neural Network (BPNN) and compares its results with the actual data from CCPP. BPNN is a regression-based prediction technique that is utilized in this study to develop a predictive model and train it using the following input features: Ambient Temperature, Ambient Pressure, Mass Flow rate of fuel in Gas Turbine 1, and Mass Flow rate of fuel in Gas Turbine 2. The Predictive Model with 10 neurons in the hidden layer was found to be most effective with Mean Squared Error (MSE) value, for the validation dataset, of 0.0063237. CCPP is also analyzed through a thermodynamic model, developed using EES. A detailed energy analysis is carried out and the results were compared with predicted and actual data. It was found that the thermal efficiency and total power generation of actual, predicted, and simulated models were 27.541% & 667.32 MW, 28.238% & 683.48 MW and 28.201% & 683.16 MW, respectively. A parametric study was further carried out to investigate the significance of operating parameters on power output and it was concluded that the temperatures across the Gas turbines have a significant impact on the performance of CCPP. Finally, Methane was replaced by 3 different fuels, one by one, and the effect of each fuel was investigated thermodynamically. It was found that the Lower Heating Value (LHV) of fuel was an important parameter in achieving a higher power output. It can be summarized from this research work that predictive models do have accuracy and such data science techniques can be used as a substitute for extensive thermodynamic calculations.

Fault Classification for Cooling System of Hydraulic Machinery Using AI.

Hydraulic systems are used in all kinds of industries. Mills, manufacturing, robotics, and Ports require the use of Hydraulic Equipment. Many industries prefer to use hydraulic systems due to their numerous advantages over electrical and mechanical systems. Hence, the growth in demand for hydraulic systems has been increasing over time. Due to its vast variety of applications, the faults in hydraulic systems can cause a breakdown. Using Artificial-Intelligence (AI)-based approaches, faults can be classified and predicted to avoid downtime and ensure sustainable operations. This research work proposes a novel approach for the classification of the cooling behavior of a hydraulic test rig. Three fault conditions for the cooling system of the hydraulic test rig were used. The spectrograms were generated using the time series data for three fault conditions. The CNN variant, the Residual Network, was used for the classification of the fault conditions. Various features were extracted from the data including the F-score, precision, accuracy, and recall using a Confusion Matrix. The data contained 43,680 attributes and 2205 instances. After testing, validating, and training, the model accuracy of the ResNet-18 architecture was found to be close to 95%.

Clean Energy Based Multigeneration System for Sustainable Cities: Thermodynamic, and Stability Analyses.

This paper concerns the development and analysis of multigeneration systems based on hybrid sources such as biomass and wind. Industry requires different types of sources to provide several outputs, so the goal of this research was to fulfill the industrial requirement with optimization. The multigeneration cycle supplies enough power to satiate energy demands, i.e., power, cooling, hydrogen, air conditioning, freshwater, hot water, and heating. For this, the multigeneration cycle was modeled in the Engineering Equation Solver (EES) and Simulink to obtain optimized results for the industry. Energy and exergy for the multigeneration cycle were determined to assess the performance of the cycle and to investigate the optimized results for the overall system. This study shows that for configuration selection and design, different thermodynamic, economic, and environmental aspects should be considered. Based on the results, the selection of the best location for this multigeneration system was made. Power output from the wind turbine was around 7 MW and from biogas 0.6 MW. The overall exergy efficiency of the multigeneration system was found to be 0.1401.

Tool Health Monitoring of a Milling Process Using Acoustic Emissions and a ResNet Deep Learning Model.

In the industrial sector, tool health monitoring has taken on significant importance due to its ability to save labor costs, time, and waste. The approach used in this research uses spectrograms of airborne acoustic emission data and a convolutional neural network variation called the Residual Network to monitor the tool health of an end-milling machine. The dataset was created using three different types of cutting tools: new, moderately used, and worn out. For various cut depths, the acoustic emission signals generated by these tools were recorded. The cuts ranged from 1 mm to 3 mm in depth. In the experiment, two distinct kinds of wood-hardwood (Pine) and softwood (Himalayan Spruce)-were employed. For each example, 28 samples totaling 10 s were captured. The trained model's prediction accuracy was evaluated using 710 samples, and the results showed an overall classification accuracy of 99.7%. The model's total testing accuracy was 100% for classifying hardwood and 99.5% for classifying softwood.

Universal design for learning for educating students with intellectual disabilities: a systematic review.

This review analyzes studies carried out between 2008 and 2018 which examined the educational impact of interventions based on the Universal Design for Learning (UDL) for students with Intellectual Disabilities (ID). The review aims to determine the research designs and methods of these studies, and their interventions and effectiveness. Seven studies met the inclusion criteria used in this review. The review demonstrated the diversity of research designs and methods used, which included quantitative, qualitative and mixed methods. The interventions included the use of digital environments, e-books, shared stories, audio systems, software and educational activities; all based on the principles of UDL. The reviewed studies confirmed the effectiveness of these interventions in educating such students, whether in academic, social or behavioral aspects. The study recommends further research to give greater clarification of the impact of UDL on the academic, social and behavioral aspects of the education of students with ID. It also recommends increasing the number of research participants and using a qualitative approach to better understand the lived experience of applying these interventions.

Characteristics of Ultrasound and Magnetic Resonance Imaging of Normal Testes and Epididymis Besides Angiography of Testicular Artery in Dromedary Camel.

Decreasing male fertility encouraged the investigators to innovate accurate diagnostic non-invasive methods for detection of changes in the testicular parenchyma. Ultrasonography (US) has the potential to be used in this manner for decades, but magnetic resonance imaging (MRI) is still of limited application in animals for this purpose. The current study was designed to describe appearances and quantitative MRI attributes of the normal testes, epididymis besides angiography of testicular artery in camels. About 30 apparently healthy male dromedary camels aged 8-14 years were slaughtered during the rutting season. Immediately after slaughtering, the male gonads (n = 30 pairs of testicles and epididymis) were subjected to morphometric evaluation using a Vernier caliper and ultrasound scanning. Epididymial sperms were evaluated for motility, vitality and abnormality. MRI was performed for testes (n=16) by using a 1.5T Excite-II MRI apparatus of Sigma. Radiography and angioarchitecture of testicular artery (n=24) were done. Camel testicular length, width, and depth showed non-significant differences between a Vernier caliper or sonar. The MRI results revealed that both the testis and epididymis have homogenously intermediate signal (T1) and testes have hyperintense signal, with slightly lower signal in the epididymis (T2). In conclusion, both the ultrasonography and MRI techniques, with each respective computer-assisted imaging, could be used to detect the histomorphological changes of the camels' testicles. However, US imaging remains the first diagnostic technique for evaluating the reproductive health in men for its lower cost and accuracy. MRI is beneficial when the sonograms are inconclusive and/or equivocal. It shows the examined tissues in greater anatomical details compared to ultrasonography. Further studies are needed to compare between characteristics of US and MRI of normal testes and epididymis with testicular artery angiography in living camel during rut season and non-rut season and between normal healthy and affected diseased genitalia.

Parametric Study and Optimization of End-Milling Operation of AISI 1522H Steel Using Definitive Screening Design and Multi-Criteria Decision-Making Approach.

End-milling operation of steel grade material is a challenging task as it is hard-to-cut material. Proper selection of cutting tools, cutting conditions, and cutting process parameters is important to improve productivity, surface quality, and tool life. Therefore, the present study investigated the end-milling operation of AISI 1522H steel grade under minimum-quantity lubrication (MQL) conditions using a novel blend of vegetable oils, namely canola and olive oil. Cutting process parameters considered were spindle speed (s), feed rate (f), depth of cut (d), width of cut (w), and cutting conditions (c), while responses were average surface roughness (Ra), cutting forces (Fc), tool wear (TW), and material removal rate (MRR). Experimental runs were designed based on the definitive screening design (DSD) method. Analysis of variance (ANOVA) results show that feed rate significantly affects all considered responses. Nonlinear prediction models were developed for each response variable, and their validity was also verified. Finally, multi-response optimization was performed using the combinative distance-based assessment (CODAS) method coupled with criteria importance through inter-criteria correlation (CRITIC). The optimized parameters found were: s = 1200 rpm, f = 320 mm/min, d = 0.6 mm, w = 8 mm, and c = 100 mL/h. Further, it was compared with other existing multi-response optimization methods and induced good results.

In silico approaches to develop herbal acaricides against R. (Boophilus) Microplus and In vitro Anti-Tick activities of selected medicinal plants.

In tropical and sub-tropical areas of the world the most damaging pest of the livestock sector are cattle tick, Rhipicephalus microplus. The current study was aimed to generate phytochemical derived acaricides to control Rhipicephalus microplus populations, to maintain livestock herd production, minimize economic losses and to reduce uses of man-made chemicals acaricides. To achieve this goal, Adult immersion and larval package test were used to determine the feasibility of Berberium lyceum and Tamarixa aphylla against Rhipicephalus microplus ticks. Further, an In silico technique was employed to discover biologically active substances from both plants using docking method. Berberium lyceum and Tamarixa aphylla exhibited a reasonably high fatal effect at 40.0 mg/L on egg laying (index of egg laying = 0.19 and 0.19) respectively, thus inhibiting the oviposition (49.5 and 45.1, respectively) and the larval mortality (97% and 93%, respectively). Further, we also used Chem-Draw ultra-software (v. 12.0.2.1076. 2010) to illustrate different structures of38 known bioactive phytochemicals which are discovered in the PubChem database and verify the hypothesis that tick inhibition was linked to acetylcholinesterase (AChE). Barbamunine and rutin from Berberium lyceum showed remarkable interaction with RmAChE1 active site residues with docking scores of -9.11 to -8.71 while phytol and dehydrodigallic acid from Tamarix aphylla showed comparable docking scores of -7.17 and -7.14 respectively against Rhipicephalus microplus acetylcholinesterase protein. Based on obtained result, we believe that Berberium lyceum and Tamarixa aphylla bioactive components could be potential candidates in the control and management of Rhipicephalus microplus and should be studied further as a supplement or replacement for synthetic acaricides.

Biological control: An effective approach against nematodes using black pepper plants (Piper nigrum L.).

Black pepper (Piper nigrum L.) is one of the oldest spices in the world, additionally, it is highly demanded. Several biotic and abiotic variables pose black pepper production worldwide. Plant-parasitic nematodes play a key role among biotic factors, causing considerable economic losses and affecting the production. Different synthetic nematicides were used for controlling plant nematodes, however the majority of pesticides have been pulled from the market due to substantial non-target effects and environmental risks. As a result, the search for alternative eco-friendly agents for controlling plant-parasitic nematodes populations. Microbial agents are a precious option. In this review the bacterial and fungal agents used as an alternative nematicides, they were studied and confirmed as essential anti-microbial agents against plant nematodes which infected Piper nigrum L. This work examines the most common plant nematodes infected Piper nigrum L., with a focus on root knot and burrowing nematodes, in addition, how to control plant parasitic nematodes using microorganisms.

Molecular epidemiology of virulent E. coli among rural small scale dairy herds and shops: Efficacy of selected marine algal extracts and disinfectants.

Virulent pathotypes of E. coli seriously affect the livestock regarding the misuse of antibiotics. All 180 samples collected from cow's environment and dairy shops in Qena, Egypt were serologically and molecularly positive for coliforms. Enteropathogenic E. coli (EPEC), Shiga toxin-producing E. coli (STEC), Enteroinvasive E. coli (EIEC) and Enterotoxigenic E. coli (ETEC) pathotypes were isolated from water and milk-related samples. STEC serogroups O26, O55, O111, O113, O145 were also recovered. The non-O157 STEC serotypes were recovered from human diarrheagenic patients contacting cattle or consuming contaminated water/milk products. BlaCTX-M and blaTEM genes were detected in 25.5% and 100%, respectively. Disinfectants and algal extracts, identified by GC-MS, were evaluated in vitro for antibacterial activities. TH4+® disinfectant and methanol extract of Turbinaria decurrens reduced E. coli at 13 log10 at 1.5% and 3 mg/ml concentrations, respectively. Ag-NPs/T. decurrens showed 8-9 log10 reduction at concentration of 1.6 × 105 NPs/ml. Examined water sources, milk and milk products were potential reservoirs for virulent antibiotic-resistant E.coli which may impose animal and public health threats.Abbreviations: APEC: Avian pathogenic E. coli; blaCTX-M: ß-lactamase inhibitors-Cefotaximase gene; blaTEM: ß-lactamase inhibitors-Temoneira gene; CFU: Colony-forming unit; DAEC: Diffusely adherent E. coli; DEC: Diarrheagenic Escherichia coli; DEMSO: Dimethyl sulfoxide; eaeA: Intimin or E. coli attaching gene; EAEC: Enteroaggregative E. coli; EHEC: Enterohemorrhagic E. coli; EIEC: Enteroinvasive E. coli; EOSQC: Egyptian Organization for Standardization and Quality Control; EPEC: Enteropathogenic E. coli; ETEC: Enterotoxigenic E. coli; ExPEC: Extra-intestinal pathogenic E. coli; GC-MS: Gas chromatography-mass spectrometry technique; hly: Hemolysin gene; STEC: Shiga like producing E. coli; stx1: Shiga-toxin 1 gene; ESBLs: Extended-spectrum beta-lactamases.

Effect of integrating industrial and agricultural wastes on concrete performance with and without microbial activity.

Cement is an essential material used in constructional activities. An emerging concern in the industry however is the CO2 emissions, which are triggered by cement manufacturing units. These emissions can be controlled to some extent by not using cement exclusively and instead replacing a percentage of it with waste material with properties similar to cement. Electric arc furnace dust (EAFD) and rice straw ash (RSA) are waste materials from industrial and agricultural sources which also contain similar constituents that are present in cement. Thus, the objective of this study is to check the effect of EAFD and RSA on concrete properties with the application of Aspergillus niger and Bacillus megaterium. Taguchi's design of experiments has been utilised to explore the effect of operating parameters (i.e. EAFD and RSA replacement (5%, 10% and 15%), curing period of concrete cubes (7, 14 and 28 days) and cell count of fungus/bacteria (104, 106 and 108 CFU/ml)) on the compressive strength and water absorption capacity of concrete blocks in three different scenarios. Optimisation has then been carried out by using the multi-objective genetic algorithm to evaluate the maximum performance of concrete. However, the results of the study indicate best performance in the 2nd context where dust replacement, curing time and cell count were 5%, 18 days and 9.39 × 107 cells per ml of water, respectively, for concrete production utilising Aspergillus niger.

A Hybrid Approach for Noise Reduction in Acoustic Signal of Machining Process Using Neural Networks and ARMA Model.

Intelligent machining has become an important part of manufacturing systems because of the increased demand for productivity. Tool condition monitoring is an integral part of these systems. Airborne acoustic emission from the machining process is a vital indicator of tool health, however, it is highly affected by background noise. Reducing the background noise helps in developing a low-cost system. In this research work, a feedforward neural network is used as an adaptive filter to reduce the background noise. Acoustic signals from four different machines in the background are acquired and are introduced to a machining signal at different speeds and feed-rates at a constant depth of cut. These four machines are a three-axis milling machine, a four-axis mini-milling machine, a variable speed DC motor, and a grinding machine. The backpropagation neural network shows an accuracy of 75.82% in classifying the background noise. To reconstruct the filtered signal, a novel autoregressive moving average (ARMA)-based algorithm is proposed. An average increase of 71.3% in signal-to-noise ratio (SNR) is found before and after signal reconstruction. The proposed technique shows promising results for signal reconstruction for the machining process.

Impact Toughness of Hybrid Carbon Fiber-PLA/ABS Laminar Composite Produced through Fused Filament Fabrication.

Nowadays, the components of carbon fiber-reinforced polymer composites (an important material) are directly produced with 3D printing technology, especially Fused Filament Fabrication (FFF). However, such components suffer from poor toughness. The main aim of this research is to overcome this drawback by introducing an idea of laying down a high toughness material on the 3D-printed carbon fiber-reinforced polymer composite sheet, thereby making a hybrid composite of laminar structure. To ascertain this idea, in the present study, a carbon-reinforced Polylactic Acid (C-PLA) composite sheet was initially 3D printed through FFF technology, which was then laid upon with the Acrylonitrile Butadiene Styrene (ABS), named as C-PLA/ABS hybrid laminar composite, in an attempt to increase its impact toughness. The hybrid composite was fabricated by varying different 3D printing parameters and was then subjected to impact testing. The results revealed that toughness increased by employing higher layer thickness and clad ratio, while it decreased by increasing the fill density, but remained unaffected due to any change in the raster angle. The highest impact toughness (23,465.6 kJ/m2) was achieved when fabrication was performed employing layer thickness of 0.5 mm, clad ratio of 1, fill density of 40%. As a result of laying up ABS sheet on C-PLA sheet, the toughness of resulting structure increased greatly (280 to 365%) as compared to the equivalent C-PLA structure, as expected. Two different types of distinct failures were observed during impact testing. In type A, both laminates fractured simultaneously without any delamination as a hammer hit the sample. In type B, the failure initiated with fracturing of C-PLA sheet followed by interfacial delamination at the boundary walls. The SEM analysis of fractured surfaces revealed two types of pores in the C-PLA lamina, while only one type in the ABS lamina. Further, there was no interlayer cracking in the C-PLA lamina contrary to the ABS lamina, thereby indicating greater interlayer adhesion in the C-PLA lamina.

A COVID-19 Supply Chain Management Strategy Based on Variable Production under Uncertain Environment Conditions.

The management of a controllable production in the manufacturing system is essential to achieve viable advantages, particularly during emergency conditions. Disasters, either man-made or natural, affect production and supply chains negatively with perilous effects. On the other hand, flexibility and resilience to manage the perpetuated risks in a manufacturing system are vital for achieving a controllable production rate. Still, these performances are strongly dependent on the multi-criteria decision making in the working environment with the policies launched during the crisis. Undoubtedly, health stability in a society generates ripple effects in the supply chain due to high demand fluctuation, likewise due to the Coronavirus disease-2019 (COVID-19) pandemic. Incorporation of dependent demand factors to manage the risk from uncertainty during this pandemic has been a challenge to achieve a viable profit for the supply chain partners. A non-linear supply chain management model is developed with a controllable production rate to provide an economic benefit to the manufacturing firm in terms of the optimized total cost of production and to deal with the different situations under variable demand. The costs in the model are set as fuzzy to cope up with the uncertain conditions created by lasting pandemic. A numerical experiment is performed by utilizing the data set of the multi-stage manufacturing firm. The optimal results provide support for the industrial managers based on the proactive plan by the optimal utilization of the resources and controllable production rate to cope with the emergencies in a pandemic.

On the Effects of Process Parameters and Optimization of Interlaminate Bond Strength in 3D Printed ABS/CF-PLA Composite.

The scope of additive manufacturing, particularly fused deposition modelling (FDM), can indeed be explored with the fabrication of multi-material composite laminates using this technology. Laminar composite structures made up of two distinct materials, namely acrylonitrile butadiene styrene (ABS) and carbon fiber reinforced polylactic acid (CF-PLA), were produced using the FDM process. The current study analyzes the effect of various printing parameters on the interfacial bond strength (IFBS) of the ABS/CF-PLA laminar composite by employing response surface methodology. The physical examination of the tested specimens revealed two failure modes, where failure mode 1 possessed high IFBS owing to the phenomenon of material patch transfer. Contrarily, failure mode 2 yielded low IFBS, while no patch transfer was observed. The analysis of variance (ANOVA) revealed that printing parameters were highly interactive in nature. After extensive experimentation, it was revealed that good quality of IFBS is attributed to the medium range of printing speed, high infill density, and low layer height. At the same time, a maximum IFBS of 20.5 MPa was achieved. The study presented an empirical relation between printing parameters and IFBS that can help in forecasting IFBS at any given printing parameters. Finally, the optimized printing conditions were also determined with the aim to maximize IFBS.