Regression and Artificial Intelligence Models to Predict the Surface Roughness in Additive Manufacturing

In additive manufacturing (AM), it is necessary to study the surface roughness, which affected the building parameters such as layer thickness and building orientation. Some AM machines have minimum layer thickness that doesn't satisfy the desired roughness. Also, it produces a fine surface that isn't required. This increases the building time and cost without any benefits. To overcome these problems and achieve a certain surface roughness, a prediction model is proposed in this chapter. Regression models were used to predict the surface roughness through the building orientation. ANN was used to predict the surface roughness through the building orientation and the layer thickness together. ANN was constructed based on experimental work that study the effect of layer thickness and building orientation on the surface roughness. Some data were used in the training process and others were used in the verification process. The results show that the layer thickness parameter has an effect more than the building orientation parameter on the surface roughness.

An Investigation of Design Considerations to Achieve Thermal Comfort in Warm Humid Climatic Zone of West Bengal

This research paper presents the investigation of design consideration to achieve thermal comfort and the warm humid climatic zone of West Bengal is considered as the primary study area for the investigation. The varying thermal comfort behavior of humans in different climate conditions and seasons clearly demonstrates that the building design strategy must conform with the region of the building. In this paper, first studying the climatic characteristics of the warm humid region design factors are selected like building materials, cross ventilation, building orientation, roofing orientation, and materials, etc. After that, all those design factors are studied and the effect of all those factors on building in various conditions is observed. Keywords: Warm Humid Climate, Thermal Comfort, Building Materials, U-value, Cross Ventilation, Building Orientation

DCRN: An Optimized Deep Convolutional Regression Network for Building Orientation Angle Estimation in High-Resolution Satellite Images

Recently, remote sensing satellite image analysis has received significant attention from geo-information scientists. However, the current geo-information systems lack automatic detection of several building characteristics inside the high-resolution satellite images. The accurate extraction of buildings characteristics helps the decision-makers to optimize urban planning and achieve better decisions. Furthermore, Building orientation angle is a very critical parameter in the accuracy of automated building detection algorithms. However, the traditional computer vision techniques lack accuracy, scalability, and robustness for building orientation angle detection. This paper proposes two different approaches to deep building orientation angle estimation in the high-resolution satellite image. Firstly, we propose a transfer deep learning approach for our estimation task. Secondly, we propose a novel optimized DCRN network consisting of pre-processing, scaled gradient layer, deep convolutional units, dropout layers, and regression end layer. The early proposed gradient layer helps the DCRN network to extract more helpful information and increase its performance. We have collected a building benchmark dataset that consists of building images in Riyadh city. The images used in the experiments are 15,190 buildings images. In our experiments, we have compared our proposed approaches and the other approaches in the literature. The proposed system has achieved the lowest root mean square error (RMSE) value of 1.24, the lowest mean absolute error (MAE) of 0.16, and the highest adjusted R-squared value of 0.99 using the RMS optimizer. The cost of processing time of our proposed DCRN architecture is 0.0113 ± 0.0141 s. Our proposed approach has proven its stability with the input building image contrast variation for all orientation angles. Our experimental results are promising, and it is suggested to be utilized in other building characteristics estimation tasks in high-resolution satellite images.

ENVIRONMENTAL PARAMETERS FOR CAMPUS OUTDOOR SPACE: A MICROCLIMATE ANALYSIS OF THE EASTERN MEDITERRANEAN UNIVERSITY (EMU) CAMPUS

ABSTRACT Open spaces—whether public, urban, or part of a campus—offer a variety of activities and opportunities to people. Therefore, open spaces should be considered a vital component of any built-up area and designed to meet the needs and address the comfort of potential users. Because of their presence in daily life and their preponderance of characteristics, open spaces have drawn the attention of many researchers, designers, and planners with varying perspectives. The current study takes a scientific approach to analyzing the environmental parameters of the Campus Outdoor Space (COS) in the case of the Eastern Mediterranean University (EMU). An extensive literature review supported the identification of seven important environmental parameters effective in the microscale analysis of a COS: geographical location, meteorological situation, urban form, surface materials, amount of vegetation and watershed, and anthropogenic pollution. Analysis of the environmental parameters called for a hybrid method that included a detailed field survey and the following set of simulations: sun-path, radiation, sky view factor, and turbulence analysis. The accuracy of the field survey directly contributed to the effectiveness of the simulations. Grasshopper® 3D software and Computational Fluid Dynamics were used to simulate the conditions of the EMU study area. The outcomes show that the spatial organization, building forms, and building orientation negatively affect the COS of EMU. In the Mediterranean climatic region of EMU, shade and flowing breezes greatly enhance comfort and usability of outdoor spaces from April to October. The massive form of buildings and minimal planning for effective building orientation to the sun increased heat storage capacity and neglected prevailing winds, resulting in flow separation and formation of eddies on the leeward side of buildings. These negatively influenced the microclimate, and thereby user comfort, at the core of the EMU’s main COS.

Passive Daylighting Design Strategies of Colonial Mosques in Malaysia

This study aims to investigate the daylight performance of Colonial Mosques in Malaysia. The first objective of this study is to identify passive daylighting strategies from the colonial mosques. The second objective of this study is to evaluate the daylight performance of the colonial mosques. The research methodology consisted of table research, field observation, and daylight analysis simulation of the prayer hall in the Colonial Mosques, using Sefaira daylighting simulation software. The results have shown that the daylight in Colonial Mosques was affected by the building orientation, shading elements, window to wall ratio, and window type. It was also found that the clerestory window type enhances indoor daylight performance. Keywords: Passive Daylighting, Colonial Mosques. eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI:

Multi-Perspective Analysis of Building Orientation Effects on Microstructure, Mechanical and Surface Properties of SLM Ti6Al4V with Specific Geometry

Building orientation is important in selective laser melting (SLM) processes. Current studies only focus on the horizontal and vertical building orientations without considering different modes of horizontal orientations. In fact, for horizontal orientation, different surfaces of the sample that contact the substrate will affect the heat transfer mode and efficiency, and in turn affect the microstructure and material properties. In this paper, the effect of two modes of horizontal building orientations on microstructure, mechanical and surface properties of SLM Ti6Al4V was studied. Current research about building orientation is deficient because the geometry of samples or test surfaces are not strictly defined, which seriously influences the results due to their different heat transfer efficiency and mode. Therefore, the geometry of the samples and test surfaces were clearly defined, and its necessity was proved in this study. To achieve the research goal, three test samples were prepared: sample SLM-PB-S with the building orientation parallel to the substrate and the shorter side L1 contacts it, sample SLM-PB-L with the building orientation parallel to the substrate and the longer side L2 contacts it and sample SLM-VB with the building orientation vertical to the substrate. Subsequently, the microstructure, grain information, densification, residual stress, micro-hardness, tensile properties and surface topography of different samples were analyzed and compared. In the results, SLM-PB-S exhibited denser microstructure and better mechanical properties than SLM-PB-L, including smaller grain size, stronger texture, higher density, micro-hardness, tensile strength, plasticity and better surface quality. It originates from a higher cooling rate and shorter scanning time between layers during SLM-PB-S fabrication, leading to finer grains, lower porosity and better interlayer metallurgical bonding, thus resulting in better material properties. This study can provide a reference to select the proper building orientation in SLM.