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In recent years, additive manufacturing techniques have attracted much research attention because of their ability to fabricate customised parts with complex geometry. The range of composites suitable for laser-based powder bed fusion technique is limited, and has not been investigated yet. This paper aims to study the fabrication of AlSi₁₀Mg reinforced with nAl₂O₃ using the laser-based powder bed fusion technique.

An experimental approach was used to investigate the densification of AlSi₁₀Mg–nAl₂O₃ composites using laser-based powder bed fusion technique. Optimisation of the porosity was performed, and microstructure evolution was evaluated.

In this study, laser volumetric energy density (approximately 109 J/mm³) was found to be required for the fabrication of AlSi₁₀Mg–nAl₂O₃ composites with a relative volumetric density approximating 99%. The use of laser volumetric energy density resulted in larger grains. Columnar grain structure was observed via the use of electron backscatter diffraction mapping.

This paper examines the processing of new aluminium composite material suitable for the fabrication via the laser-based powder bed fusion technique.

Selective laser melting (SLM) is an additive manufacturing technology that is gaining industrial and research interest as it can directly fabricate near full density metallic components. The paper aims to identify suitable process parameters for SLM of processing of pure nickel powder and to study the microstructure of such products. The study also aims to characterize the microhardness and tensile properties of pure nickel produced by SLM.

A 2⁴ factorial design experiment was carried out to identify the most significant factors on the resultant porosity of nickel parts. A subsequent experiment was carried out with a laser power of 350 W. The scanning speeds and hatch spacings were varied.

Scanning speed and hatch spacing have significant effects on the porosity of SLM components. A high relative density of 98.9 per cent was achieved, and microhardness of 140 to 160 Hv was obtained from these samples. A tensile strength 452 MPa was obtained.

As the energy input levels were made in steps of 20 J/mm³ for the optimization study, the true optimal combination of parameters may have been missed. Therefore, researchers are encouraged to test the parameters with smaller variations in energy levels.

The paper provides a set of optimized parameters for the SLM of pure nickel. This study enables the three-dimensional (3D) printing of objects with nickel, which has applications in chemical catalyses and in microelectromechanical systems with its magnetostrictive properties.

This research is the first in direct processing of pure nickel using SLM, with the identification of suitable process parameters. The study also provides an understanding of the porosity, microhardness, strength and microstructure of SLM produced nickel parts. This work paves the way for standardization of 3D printed nickel components and enables the applications of pure nickel via SLM.

Limitations encountered with the models developed in the previous studies had occurrences of global minima; due to which this study developed a new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization. Ubiquitous machine learning computational model process performs training in a better way than regular supervised learning or unsupervised learning computational models with deep learning techniques, resulting in better learning and optimization for the considered problem domain of cloud-based internet-of-things (IOTs). This study aims to improve the network quality and improve the data accuracy rate during the network transmission process using the developed ubiquitous deep learning computational model.

In this research study, a novel intelligent ubiquitous machine learning computational model is designed and modelled to maintain the optimal energy level of cloud IOTs in sensor network domains. A new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization is developed. A new unified deterministic sine-cosine algorithm has been developed in this study for parameter optimization of weight factors in the ubiquitous machine learning model.

The newly developed ubiquitous model is used for finding network energy and performing its optimization in the considered sensor network model. At the time of progressive simulation, residual energy, network overhead, end-to-end delay, network lifetime and a number of live nodes are evaluated. It is elucidated from the results attained, that the ubiquitous deep learning model resulted in better metrics based on its appropriate cluster selection and minimized route selection mechanism.

In this research study, a novel ubiquitous computing model derived from a new optimization algorithm called a unified deterministic sine-cosine algorithm and deep learning technique was derived and applied for maintaining the optimal energy level of cloud IOTs in sensor networks. The deterministic levy flight concept is applied for developing the new optimization technique and this tends to determine the parametric weight values for the deep learning model. The ubiquitous deep learning model is designed with auto-encoders and decoders and their corresponding layers weights are determined for optimal values with the optimization algorithm. The modelled ubiquitous deep learning approach was applied in this study to determine the network energy consumption rate and thereby optimize the energy level by increasing the lifetime of the sensor network model considered. For all the considered network metrics, the ubiquitous computing model has proved to be effective and versatile than previous approaches from early research studies.

The developed ubiquitous computing model with deep learning techniques can be applied for any type of cloud-assisted IOTs in respect of wireless sensor networks, ad hoc networks, radio access technology networks, heterogeneous networks, etc. Practically, the developed model facilitates computing the optimal energy level of the cloud IOTs for any considered network models and this helps in maintaining a better network lifetime and reducing the end-to-end delay of the networks.

The social implication of the proposed research study is that it helps in reducing energy consumption and increases the network lifetime of the cloud IOT based sensor network models. This approach helps the people in large to have a better transmission rate with minimized energy consumption and also reduces the delay in transmission.

In this research study, the network optimization of cloud-assisted IOTs of sensor network models is modelled and analysed using machine learning models as a kind of ubiquitous computing system. Ubiquitous computing models with machine learning techniques develop intelligent systems and enhances the users to make better and faster decisions. In the communication domain, the use of predictive and optimization models created with machine learning accelerates new ways to determine solutions to problems. Considering the importance of learning techniques, the ubiquitous computing model is designed based on a deep learning strategy and the learning mechanism adapts itself to attain a better network optimization model.