•The effect of different process parameters on laser absorption ratio, meltpool temperature, and meltpool depth have been investigated.•The mechanism of irradiation and reflection in meltpool have ...been studied.•The mathematical model between absorption ratio as a function of scanning speed and laser power is developed.
The purpose of this research is to develop a hybrid experimental and computational model to estimate the absorption of the laser for Laser-Based Powder Bed Fusion (LB-PBF) of IN718. The research also aims to find an underlying knowledge on the effect of absorption ratio on meltpool morphology. This model helps to improve the accuracy of the prediction of the meltpool morphology and the rheology of the melt tracks as well as the temperature-related phenomena in the process. In this paper, two test sets for the initial test with constant and dynamic/(process parameters dependent) absorption ratios were simulated. Melt tracks with different process parameters are printed and simulated from low to high energy density. Then, the Absorption Ratio (AR) is changed for each simulation until the same meltpool depth as the experimental results is obtained. In the next step, the temperature of the meltpool in the interaction area of the laser and material is measured from the performed simulations. Based on the obtained temperature and process parameters, a mathematical model is developed to estimate the absorption ratio in different conditions. The model is validated in hybrid computational and experimental conditions. The investigation provides the first model to calculate the absorption ratio in LB-PBF based on the temperature of the meltpool and process parameters. Results show that the developed model significantly enhances the accuracy of estimating meltpool features such as temperature, rheological and thermophysical properties of the material in the melting state.
One problematic task in the laser-based powder bed fusion (LB-PBF) process is the estimation of meltpool depth, which is a function of the process parameters and thermophysical properties of the ...materials. In this research, the effective factors that drive the meltpool depth such as optical penetration depth, angle of incidence, the ratio of laser power to scan speed, surface properties and plasma formation are discussed. The model is useful to estimate the meltpool depth for various manufacturing conditions. A proposed methodology is based on the simulation of a set of process parameters to obtain the variation of meltpool depth and temperature, followed by validation with reference to experimental test data. Numerical simulation of the LB-PBF process was performed using the computational scientific tool “Flow3D Version 11.2” to obtain the meltpool features. The simulation data was then developed into a predictive analytical model for meltpool depth and temperature based on the thermophysical powder properties and associated parameters. The novelty and contribution of this research are characterising the fundamental governing factors on meltpool depth and developing an analytical model based on process parameters and powder properties. The predictor model helps to accurately estimate the meltpool depth which is important and has to be sufficient to effectively fuse the powder to the build plate or the previously solidified layers ensuring proper bonding quality. Results showed that the developed analytical model has a high accuracy to predict the meltpool depth. The model is useful to rapidly estimate the optimal process window before setting up the manufacturing tasks and can therefore save on lead-time and cost. This methodology is generally applied to Inconel 718 processing and is generalisable for any powder of interest. The discussions identified how the effective physical factors govern the induced heat versus meltpool depth which can affect the bonding and the quality of LB-PBF components.
The purpose of this work is to identify the principle of electron beam powder bed fusion (EB-PBF) and the performance of this AM method in the processing of copper components. This review details the ...experimentally reported properties, including microstructural, mechanical and physical properties of pure copper made by EB-PBF. The technical challenges and opportunities of EB-PBF are identified to provide insight into the influence of process parameters on observed mechanical properties as well as a roadmap for strategic research opportunities in this field. These insights allow optimisation of EB-PBF parameters, as well as comparison of the relative merits of EB-PBF over LB-PBF in the processing of copper components. This review details the microstructure and mechanical properties of EB-PBF of copper and identifies the technical opportunities and challenges. In addition, this report characterises the influence of process parameters, and subsequent energy density, on the associated mechanical properties. The discussions showed that the chance of pollution in copper processing by EB-PBF is less than laser-based powder bed fusion (LB-PBF) due to the high vacuum environment for electron beam. Oxygen content in the EB-PBF of copper powder is a vital factor and significantly affects the mechanical properties and quality of the specimen including physical density. The produced Cu
2
O due to the existence of oxygen content (in powder and bulk material) can improve the mechanical properties. However, if the Cu
2
O exceeds a certain percentage (0.0235%wt), cracks appear and negatively affect the mechanical properties. In copper printing by this method, the process parameters have to be tuned in such a way as to generate low build temperatures due to the high thermal conductivity of this alloy and the high sintering tendency of the powder.
Purpose
This paper reviews the synergy of Industry 4.0 and additive manufacturing (AM) and discusses the integration of data-driven manufacturing systems and product service systems as a key ...component of the Industry 4.0 revolution. This paper aims to highlight the potential effects of Industry 4.0 on AM via tools such as digitalisation, data transfer, tagging technology, information in Industry 4.0 and intelligent features.
Design/methodology/approach
In successive phases of industrialisation, there has been a rise in the use of, and dependence on, data in manufacturing. In this review of Industry 4.0 and AM, the five pillars of success that could see the Internet of Things (IoT), artificial intelligence, robotics and materials science enabling new levels of interactivity and interdependence between suppliers, producers and users are discussed. The unique effects of AM capabilities, in particular mass customisation and light-weighting, combined with the integration of data and IoT in Industry 4.0, are studied for their potential to support higher efficiencies, greater utility and more ecologically friendly production. This research also illustrates how the digitalisation of manufacturing for Industry 4.0, through the use of IoT and AM, enables new business models and production practices.
Findings
The discussion illustrates the potential of combining IoT and AM to provide an escape from the constraints and limitations of conventional mass production whilst achieving economic and ecological savings. It should also be noted that this extends to the agile design and fabrication of increasingly complex parts enabled by simulations of complex production processes and operating systems. This paper also discusses the relationship between Industry 4.0 and AM with respect to improving the quality and robustness of product outcomes, based on real-time data/feedback.
Originality/value
This research shows how a combined approach to research into IoT and AM can create a step change in practice that alters the production and supply paradigm, potentially reducing the ecological impact of industrial systems and product life cycle. This paper demonstrates how the integration of Industry 4.0 and AM could reshape the future of manufacturing and discusses the challenges involved.
•This study proposes a set of novel benchmarks to detect conduction and keyhole modes in Laser-Based Powder Bed Fusion.•The proposed benchmarks provide an accurate criterion for the transition from ...conduction to keyhole power transmission mode.•Process temperature and thermophysical properties in LB-PBF strongly drive the observed meltpool features.
This study proposes a set of novel benchmarks to detect conduction and keyhole modes in Laser-Based Powder Bed Fusion (LB-PBF) of Inconel 718. These proposed detection benchmarks for power transition mode in LB-PBF helps to establish a process window to obtain desirable part quality and improve the mechanical properties. The benchmark identifies the range of process parameters for obtaining and optimising a meltpool depth with fewer defects such as keyholes to ensure that the LB-PBF process is running in a steady state.
In this study, four distinct test cases were simulated and compared with the experimental test data to compare existing and novel benchmarks for the prediction of keyhole conduction modes. Then six different test cases that produce low to high melting temperature were selected to form a shallow to deep meltpool. Numerical CFD simulation (Flow-3D V12) was completed for these scenarios and simulated depth and width of the meltpool are calculated. These simulation results were verified according to the measurement of experimentally fabricated test coupons with three repetitions.
The proposed benchmarks provide an accurate criterion for the transition from conduction to keyhole transition mode. Results showed that the process temperature and thermophysical properties in LB-PBF strongly drive the observed meltpool features such as depth. By controlling the transition from conduction to keyhole mode the bonding quality can be controlled leading to enhanced quality of the printed components.
The original contribution of this paper is to assess the predictive capability of existing transition benchmarks and to provide novel and coherent benchmarks based on a detailed description of the process parameters and the thermophysical properties of the feedstock. The novel benchmarks perform better than the current benchmarks and can be applied for a wide range of process parameters and different materials.
Protective Titanium and Wolfranium coatings on industrial tools are very common today. Measurement of Metal layers properties contain layer to layer adhesive, surface and volume density also ...hardening and corrosion (include corrosion of adhesive, pressure, laminating, rising and chemical corrosion). Also due to friction protective layers erosion in industrial tools and so on has been considered. The aim of this research is density evaluation and comparison of source density and hardness of protective layers. In this study different wear mechanism for cutting tools and the rate of wear, control has been investigated. Also, we analyzed the value of material resistance versus impact loads to determine the toughness, hardness, and abrasion rate. The results show than using uncoated materials results in a higher value of adhesion, wear, and abrasion rate and to solve the mentioned problems thin film coating is recommended.
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