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The purpose of this study is to analyze the effect of human factor training in aircraft maintenance accident mitigation and aircraft safety in post COVID-19 aviation scenarios. The cause of aircraft accidents and details of three decades of selective aircraft maintenance accidents are analyzed to arrive to the significant aviation safety factor. The effect of COVID-19 pandemic and related technological applications to maintain high standards of safety and their applications in aircraft maintenance with respect to the view of human factors are discussed in details.

This paper details the overview of the human errors, error mitigation and need of human factor applications in aircraft maintenance industry for safe air travel. The criticality of aircraft maintenance in keeping aircraft in airworthy condition to provide safe air transportation without delay and to support airline economy is discussed in this study.

The cause of aircraft accidents and details of three decades of selective aircraft maintenance accidents are analyzed to arrive to the significant aviation safety factor. The effect of COVID-19 pandemic and related technological applications to maintain high standards of safety and their applications in aircraft maintenance with respect to the view of human factors are discussed in details. The route of error mitigation and need of high standard technological training with human factor knowledge, to aircraft maintenance students are analyzed in detail with the opportunity of percentages of error reduction.

This study bridges, gained knowledge for aircraft maintenance error mitigation, current accident rates and future training needs for safest air travel through high standard quality maintenance in aircraft and its systems.

Scheduling of aircraft maintenance operations is a gap in the literature. Maintenance times should be determined close to the real-life to schedule aircraft maintenance operations effectively. The learning effect, which has been studied extensively in the machine scheduling literature, has not been investigated on aircraft maintenance times. In the literature, the production times under the learning effect have been examined in numerous studies but for merely manufacturing and assembly lines. A model for determining base and line maintenance times in civil aviation under the learning effect has not been proposed yet. It is pretty challenging to determine aircraft maintenance times due to the various aircraft configurations, extended maintenance periods, different worker shifts and workers with diverse experience and education levels. The purpose of this study is to determine accurate aircraft maintenance times rigorously with a new model which includes the group learning effect with the multi-products and shifts, plateau effect, multi sub-operations and labour firings/rotations.

Aircraft maintenance operations are carried out in shifts. Each maintenance operation consists of many sub-operations that are performed by groups of workers. Thus, various models, e.g. learning curve for maintenance line (MLC), MLC with plateau factor (MPLC), MLC with group factor (MGLC) were developed and used in this study. The performance and efficiency of the models were compared with the current models in the literature, such as the Yelle Learning model (Yelle), single learning curve (SLC) model and SLC with plateau factor model (SLC-P). Estimations of all these models were compared with actual aircraft maintenance times in terms of mean absolute deviation (MAD), mean absolute percentage error (MAPE) and mean square of the error (MSE) values. Seven years (2014–2020) maintenance data of one of the top ten maintenance companies in civil aviation were analysed for the application and comparison of learning curve models.

The best estimations in terms of MAD, MAPE and MSE values are, respectively, gathered by MGLC, SLC-P, MPLC, MLC, SLC and YELLE models. This study revealed that the models (MGLC, SLC-P, MPLC), including the plateau factor, are more efficient in estimating accurate aircraft maintenance times. Furthermore, MGLC always made the closest estimations to the actual aircraft maintenance times. The results show that the MGLC model is more accurate than all of the other models for all sub-operations. The MGLC model is promising for the aviation industry in determining aircraft maintenance times under the learning effect.

In this study, learning curve models, considering groups of workers working in shifts, have been developed and employed for the first time for estimating more realistic maintenance times in aircraft maintenance. To the best of the authors’ knowledge, the effect of group learning on maintenance times in aircraft maintenance operations has not been studied. The novelty of the models are their applicability for groups of workers with different education and experience levels working in the same shift where they can learn in accordance with their proportion of contribution to the work and learning continues throughout shifts. The validity of the proposed models has been proved by comparing actual aircraft maintenance data. In practice, the MGLC model could efficiently be used for aircraft maintenance planning, certifying staff performance evaluations and maintenance trainings. Moreover, aircraft maintenance activities can be scheduled under the learning effect and a more realistic maintenance plan could be gathered in that way.

In additive manufacturing (AM) process, the physical properties of the products made by fractal toolpaths are better as compared to those made by conventional toolpaths. Also, it is desirable to minimize the number of tool retractions. The purpose of this study is to describe three different methods to generate fractal-based computer numerical control (CNC) toolpath for area filling of a closed curve with minimum or zero tool retractions.

This work describes three different methods to generate fractal-based CNC toolpath for area filling of a closed curve with minimum or zero tool retractions. In the first method, a large fractal square is placed over the outer boundary and then rest of the unwanted curve is trimmed out. To reduce the number of retractions, ends of the trimmed toolpath are connected in such a way that overlapping within the existing toolpath is avoided. In the second method, the trimming of the fractal is similar to the first method but the ends of trimmed toolpath are connected such that the overlapping is found at the boundaries only. The toolpath in the third method is a combination of fractal and zigzag curves. This toolpath is capable of filling a given connected area in a single pass without any tool retraction and toolpath overlap within a tolerance value equal to stepover of the toolpath.

The generated toolpath has several applications in AM and constant Z-height surface finishing. Experiments have been performed to verify the toolpath by depositing material by hybrid layered manufacturing process.

Third toolpath method is suitable for the hybrid layered manufacturing process only because the toolpath overlapping tolerance may not be enough for other AM processes.

Development of a CNC toolpath for AM specifically hybrid layered manufacturing which can completely fill any arbitrary connected area in single pass while maintaining a constant stepover.

Multi-jet deposition of the materials is a matured technology used for graphic printing and 3 D printing for a wide range of materials. The multi-jet technology is fine-tuned for liquids with a specific range of viscosity and surface tension. However, the use of multi-jet for low viscosity fluids like water is not very popular. This paper aims to demonstrate the technique, particularly for the water-ice 3 D printing. 3 D printed ice parts can be used as patterns for investment casting, templates for microfluidic channel fabrication, support material for polymer 3 D printing, etc.

Multi-jet ice 3 D printing is a novel technique for producing ice parts by selective deposition and freezing water layers. The paper confers the design, embodiment and integration of various subsystems of multi-jet ice 3 D printer. The outcomes of the machine trials are reported as case studies with elaborate details.

The prismatic geometries are realized by ice 3 D printing. The accuracy of 0.1 mm is found in the build direction. The part height tends to increase due to volumetric expansion during the phase change.

The present paper gives a novel architecture of the ice 3 D printer that produces the ice parts with good accuracy. The potential applications of the process are deliberated in this paper.

The support structures of sacrificial material are built in deposition-based additive manufacturing (AM), which are later removed either by breaking or dissolving. Such a sacrificial material is not feasible in metal AM. The purpose of this study is to find a suitable method for eliminating the need of support mechanism. In this work, the authors use the tilting of the substrate to alleviate the need for the support mechanism altogether.

As in the traditional AM, the object is grown in horizontal layers. However, wherever undercuts are encountered, the substrate is tilted appropriately to capture the droplets. Such a tilt involves two rotary axes invariably. To conform to the slice geometry, these two tilts are accompanied by the three linear movements. Thus, the object with undercuts is grown in planar layers using five-axis deposition without any support structure. Each pair of the corresponding top and bottom contours of any slice defines a ruled surface. The axis of the deposition head will be aligned with the rules of this surface.

The need for the support mechanism was eliminated using five-axis deposition. This was experimentally demonstrated by building an aluminum impeller using a metal inert gas cladding head.

In the proposed methodology, the objects with an abrupt change in the geometry are not possible to realize.

This manuscript proposed a novel method of eliminating the support mechanism through continuous five-axis deposition.

The purpose of this paper is to analyze and build a deep learning model that can furnish statistics of COVID-19 and is able to forecast pandemic outbreak using Kaggle open research COVID-19 data set. As COVID-19 has an up-to-date data collection from the government, deep learning techniques can be used to predict future outbreak of coronavirus. The existing long short-term memory (LSTM) model is fine-tuned to forecast the outbreak of COVID-19 with better accuracy, and an empirical data exploration with advanced picturing has been made to comprehend the outbreak of coronavirus.

This research work presents a fine-tuned LSTM deep learning model using three hidden layers, 200 LSTM unit cells, one activation function ReLu, Adam optimizer, loss function is mean square error, the number of epochs 200 and finally one dense layer to predict one value each time.

LSTM is found to be more effective in forecasting future predictions. Hence, fine-tuned LSTM model predicts accurate results when applied to COVID-19 data set.

The fine-tuned LSTM model is developed and tested for the first time on COVID-19 data set to forecast outbreak of pandemic according to the authors’ knowledge.

Hybrid manufacturing technologies combining individual processes can be recognized as one of the most cogent developments in recent times. As a result of integrating additive, subtractive and inspection processes within a single system, the relative benefits of each process can be exploited. This collaboration uses the strength of the individual processes, while decreasing the shortcomings and broadening the application areas. Notwithstanding its numerous advantages, the implementation of hybrid technology is typically affected by the limited process planning methods. The process planning methods proficient at effectively using manufacturing sources for hybridization are notably restrictive. Hence, this paper aims to propose a computer-aided process planning system for hybrid additive, subtractive and inspection processes. A dynamic process plan has been developed, wherein an online process control with intelligent and autonomous characteristics, as well as the feedback from the inspection, is utilized.

In this research, a computer-aided process planning system for hybrid additive, subtractive and inspection process has been proposed. A framework based on the integration of three phases has been designed and implemented. The first phase has been developed for the generation of alternative plans or different scenarios depending on machining parameters, the amount of material to be added and removed in additive and subtractive manufacturing, etc. The primary objective in this phase has been to conduct set-up planning, process selection, process sequencing, selection of machine parameters, etc. The second phase is aimed at the identification of the optimum scenario or plan.

To accomplish this goal, economic models for additive and subtractive manufacturing were used. The objective of the third phase was to generate a dynamic process plan depending on the inspection feedback. For this purpose, a multi-agent system has been used. The multi-agent system has been used to achieve intelligence and autonomy of different phases.

A case study has been developed to test and validate the proposed algorithm and establish the performance of the proposed system.

The major contribution of this work is the novel dynamic computer-aided process planning system for the hybrid process. This hybrid process is not limited by the shortcomings of the constituent processes in terms of tool accessibility and support volume. It has been established that the hybrid process together with an appropriate computer-aided process plan provides an effective solution to accurately fabricate a variety of complex parts.

This study aims to improve the acceleration in the additive manufacturing (AM) process. AM tools, such as extrusion heads, jets, electric arcs, lasers and electron beams (EB), experience negligible forces. However, their speeds are limited by the positioning systems. In addition, a thin tool must travel several kilometers in tiny motions with several turns while realizing the AM part. Hence, acceleration is a more significant limiting factor than the velocity or precision for all except EB.

The sawtooth (ST) scanning strategy presented in this paper minimizes the time by combining three motion features: zigzag scan, 45º or 135º rotation for successive layers in G00 to avoid the CNC interpolation, and modifying these movements along 45º or 135º into sawtooth to halve the turns.

Sawtooth effectiveness is tested using an in-house developed Sand AM (SaAM) apparatus based on the laser–powder bed fusion AM technique. For a simple rectangle layer, the sawtooth achieved a path length reduction of 0.19%–1.49% and reduced the overall time by 3.508–4.889 times, proving that sawtooth uses increased acceleration more effectively than the other three scans. The complex layer study reduced calculated time by 69.80%–139.96% and manufacturing time by 47.35%–86.85%. Sawtooth samples also exhibited less dimensional variation (0.88%) than zigzag 45° (12.94%) along the build direction.

Sawtooth is limited to flying optics AM process.

Development of scanning strategy for flying optics AM process to reduce the warpage by improving the acceleration.

The principal aim of the present study was to identify and model the subject structure of the research area on collaborative information behaviour (CIB).

A qualitative, inductive and exploratory approach was adopted, and the method of thematic analysis was used. This study was based on the analysis of 79 publications selected from the Library, Information Science and Technology Abstracts (LISTA) database in April 2019.

Collaborative and collective information behaviours were differentiated, and the subject structure of the CIB research area was identified to contain collaborative activities oriented to both information access and content, their various conditions, means of conducting, experiences of selected communities and metascientific research on the area itself.

The limitations result primarily from relying on the research material selected from the database (LISTA) focussed mainly on the issues of library and information science.

This study contributes by proposing an original model of the CIB research area representing its subject structure and providing a coherent list of subjects of interest to CIB researchers. Hopefully, it will also contribute to the harmonisation of terminology related to this research area and thus facilitate communication between CIB researchers and accelerate the cumulative development of scientific knowledge on CIB.

The purpose of this paper is to classify the given image as indoor or outdoor with higher success rate by mixing various features like brightness, number of straight lines, number of Euclidean shapes and recursive shapes.

For annotating an image, it is very easy, if the image is categorized as indoor or outdoor. Many methods are proposed to classify the given image in these criteria but still the rate of uncategorized images occupies considerable area. This proposed work is the extension of the existing works already proposed by experts in this field. Some of the parameters mainly focused to classify are color histogram, orientation of edges, straightness of edges, discrete cosine transform coefficients, etc. In addition to that, this work includes finding of Euclidean shapes i.e. closed contours and recursive shapes in the given image. When the Euclidean shaped object dominates the recursive shapes then it is classified as indoor object and if the recursive shapes dominates, it is categorized as outdoor object.

This work is carried out on the standard image data sets. The data sets are Microsoft Research Cambridge (MRC) object recognition image database 1.0. and Kodak and Coral image data set. Totally 540 images are taken into account and the images are classified 95.4 percent correctly.

Many methods are proposed to classify the given image in these criteria but still the rate of uncategorized images occupies considerable area. This proposed work is the extension of the existing works already proposed by experts in this field. Some of the parameters mainly focused to classify are color histogram, orientation of edges, straightness of edges, discrete cosine transform coefficients, etc. In addition to that, this work includes finding of Euclidean shapes i.e. closed contours and recursive shapes in the given image. When the Euclidean shaped object dominates the recursive shapes then it is classified as indoor object and if the recursive shapes dominates, it is categorized as outdoor object. This work is carried out on the standard image data sets. The data sets are MRC object recognition image database 1.0. and Kodak and Coral image data set. Totally 540 images are taken into account and the images are classified 95.4 percent correctly.