Purpose: In recent years, 3D printing technologies have been widely used in the construction industry. 3D printing in construction is very attractive because of its capability of process automation and the possibility of saving labor, waste materials, construction time and hazardous procedures for humans. Significant researches were conducted to identify the performance of the materials, while some researches focused on the development of novel techniques and methods, such as building information modeling. This paper aims to provide a detailed overview of the state-of-the-art of currently used 3D printing technologies in the construction areas and global acceptance in its applications. Design/methodology/approach: The working principle of additive manufacturing in construction engineering (CE) is presented in terms of structural design, materials used and theoretical background of the leading technologies that are used to construct buildings and structures as well as their distinctive features. Findings: The trends of 3D printing processes in CE are very promising, as well as the development of novel materials, will gain further momentum. The findings also indicate that the digital twin (DT) in construction technology would bring the industry a step forward toward achieving the goal of Industry 5.0. Originality/value: This review highlights the prospects of digital manufacturing and the DT in construction engineering. It also indicates the future research direction of 3D printing in various constriction sectors.

Considering the significance of improving the energy efficiency, surface quality and material removal quantity of machining processes, the present study is conducted in the form of an experimental investigation and a multi-objective optimization. The experiments were conducted by face milling AISI 1045 steel on a Computer Numerical Controlled (CNC) milling machine using a carbide cutting tool. The Cu-nano-fluid, dispersed in distilled water, was impinged in small quantity cooling lubrication (SQCL) spray applied to the cutting zone. The data of surface roughness and active cutting energy were measured while the material removal rate was calculated. A multi-objective optimization was performed by the integration of the Taguchi method, Grey Relational Analysis (GRA), and the Non-Dominated Sorting Genetic Algorithm (NSGA-II). The optimum results calculated were a cutting speed of 1200 rev/min, a feed rate of 320 mm/min, a depth of cut of 0.5 mm, and a width of cut of 15 mm. It was also endowed with a 20.7% reduction in energy consumption. Furthermore, the use of SQCL promoted sustainable manufacturing. The novelty of the work is in reducing energy consumption under nano fluid assisted machining while paying adequate attention to material removal quantity and the product’s surface quality.

Gas tungsten arc welding (GTAW) technology is widely used in industry and has advantages, including high precision, excellent welding quality, and low equipment cost. However, the inclusion of a large number of process parameters hinders its application on a wider scale. Therefore, there is a need to implement the prediction and optimization models that effectively enhance the process performance of the GTAW process in different applications. In this study, a five-factor five-level central composite design (CCD) matrix was used to conduct GTAW experiments. AISI 1020 steel blank was used as a substrate; UTP AF Ledurit 60 and UTP AF Ledurit 68 were used as the materials of two tubular wires. Further, an artificial neural network (ANN) was used to simulate the GTAW process and then combined with a genetic algorithm (GA) to determine welding parameters that can provide an optimal weld. In welding experiments, five different welding current levels, welding speed, distance to the nozzle, angle of movement, and frequency of the wire feed pulses were used. Using GA, optimal welding parameters were determined: welding current = 222 A, welding speed = 25 cm/min, nozzle deflection distance = 8 mm, travel angle = 25°, wire feed pulse frequency = 8 Hz. The determination coefficient (R²) and RMSE value of all response parameters are satisfactory, and the R² of all the data remained higher than 0.65.

Diesinker electric discharge machining is widely used non-conventional technique for making high precision and complex shaped parts. Dielectrics and electrical parameters were considered as the main factors for EDM performance. In this paper, the effects of pulse-on-time (μs) and current (ampere) were evaluated for performance measures using kerosene and water as dielectrics. A comparison was performed for both dielectrics in terms of material removal rate (mm3/min), electrode wear rate (mm3/min), and microstructures. Aluminum 6061 T6 alloy was used as material for this research due to its extensive use in aerospace and automotive industries. Experiments were designed using Taguchi L9 orthogonal array (OA). Time series graphs were plotted to compare material removal rate and electrode wear rate. Microstructures were taken by scanning electron microscope to analyze the surface produced in terms of cracks, globules and micro-holes. Higher material removal rate and lower electrode wear were achieved with kerosene dielectric. The novelty of this research work, apart from its practical application, is that Aluminum 6061 T6 alloy is used as work material to compare the performance of dielectrics (kerosene and distilled water). Paper presented at: Complex Systems Engineering and Development Proceedings of the 27th CIRP Design Conference Cranfield University, UK 10th – 12th May 2017

Cost-effective and environmentally responsible ways of carbon fiber-reinforced composite (CFRP) recycling are increasingly important, owing to the rapidly increasing use of these materials in many industries such as the aerospace, automotive and energy sectors. Product designers need to consider the costs associated with manufacturing and the end-of-life stage of such materials to make informed decisions. They also need to understand the current methods of composite recycling and disposal and their impact on the end-of-life costs. A comprehensive literature review indicated that there is no such tool to estimate CFRP recycling costs without any prior knowledge and expertise. Therefore, this research paper proposed a novel knowledge-based system for the cost modelling of recycling CFRP that does not require in-depth knowledge from a user. A prototype of a cost estimation system has been developed based on existing CFRP recycling techniques such as mechanical recycling, pyrolysis, fluidized bed, and supercritical water. The proposed system has the ability to select the appropriate recycling techniques based on a user’s needs with the help of an optimization module based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Estimating recycling costs has taken into consideration various factors such as different material types in different industries, transportation, and dismantling costs. The developed system can be employed to support early-stage designers and decision-making stakeholders in terms of understanding and predicting recycling costs easily and quickly.