Muhammad Arif Mahmood, Andrei C. Popescu, Mihai Oane, Carmen Ristoscu and Ion N. Mihailescu
This paper aims to develop efficient and simple models for thermal distribution, melt pool dimensions and controlled phase change in the laser additive manufacturing (AM) of bulk…
Abstract
Purpose
This paper aims to develop efficient and simple models for thermal distribution, melt pool dimensions and controlled phase change in the laser additive manufacturing (AM) of bulk and powder particles ceramic materials.
Design/methodology/approach
This paper proposes new analytical models for the AM of bulk and powder bed ceramic materials. A volumetric moving heat source, along with the complete melting of bulk and powder particle materials, is taken into account. Different values of laser absorption coefficient in solid and liquid states have been used to investigate the phase transformation. Furthermore, the pores and voids dimensions are also included in the modeling. Theoretical predictions have been compared with the experimental analyses and finite element simulations in laser to silicon nitride and laser to alumina interaction. The analysis focuses on the impact of laser power and scanning speed on the melt pool width and depth evolution into the bulk substrate and powder bed.
Findings
This study shows that the powder particles exhibit a higher thermal distribution value than the bulk substrate because of voids in the powder layer. The laser beam experiences multiple reflections in the presence of porosity/voids, thus increasing the surface absorption coefficient, which becomes relevant with the increment in the pore/void dimension. A direct relationship has been found between the laser power and melt pool dimensions, while the scanning speed displayed an inverse relationship for the melt pool width and length. Larger melt dimensions were inferred in the case of laser–powder particle interaction compared with laser–bulk substrate interaction. A close correlation was found between the analytical simulations, experimental investigations and numerical simulation results within the range of 4%–8%.
Originality/value
This paper fulfills an identified need to develop efficient and simplified models for ceramics laser AM by taking into account different laser absorption coefficients in solid and liquid form, voids and pores dimensions and controlled phase transformation to avoid vapors and plasma formation. The limitation of the finite element simulation model is that the solution is strongly dependent on the mesh quality and accuracy directly linked to the computation efficiency and time. A finer mesh requires a longer computing time than a coarse mesh. Finite element simulations require, however, specialized skills.
Details
Keywords
Muhammad Arif Mahmood, Chioibasu Diana, Uzair Sajjad, Sabin Mihai, Ion Tiseanu and Andrei C. Popescu
Porosity is a commonly analyzed defect in the laser-based additive manufacturing processes owing to the enormous thermal gradient caused by repeated melting and solidification…
Abstract
Purpose
Porosity is a commonly analyzed defect in the laser-based additive manufacturing processes owing to the enormous thermal gradient caused by repeated melting and solidification. Currently, the porosity estimation is limited to powder bed fusion. The porosity estimation needs to be explored in the laser melting deposition (LMD) process, particularly analytical models that provide cost- and time-effective solutions compared to finite element analysis. For this purpose, this study aims to formulate two mathematical models for deposited layer dimensions and corresponding porosity in the LMD process.
Design/methodology/approach
In this study, analytical models have been proposed. Initially, deposited layer dimensions, including layer height, width and depth, were calculated based on the operating parameters. These outputs were introduced in the second model to estimate the part porosity. The models were validated with experimental data for Ti6Al4V depositions on Ti6Al4V substrate. A calibration curve (CC) was also developed for Ti6Al4V material and characterized using X-ray computed tomography. The models were also validated with the experimental results adopted from literature. The validated models were linked with the deep neural network (DNN) for its training and testing using a total of 6,703 computations with 1,500 iterations. Here, laser power, laser scanning speed and powder feeding rate were selected inputs, whereas porosity was set as an output.
Findings
The computations indicate that owing to the simultaneous inclusion of powder particulates, the powder elements use a substantial percentage of the laser beam energy for their melting, resulting in laser beam energy attenuation and reducing thermal value at the substrate. The primary operating parameters are directly correlated with the number of layers and total height in CC. Through X-ray computed tomography analyses, the number of layers showed a straightforward correlation with mean sphericity, while a converse relation was identified with the number, mean volume and mean diameter of pores. DNN and analytical models showed 2%–3% and 7%–9% mean absolute deviations, respectively, compared to the experimental results.
Originality/value
This research provides a unique solution for LMD porosity estimation by linking the developed analytical computational models with artificial neural networking. The presented framework predicts the porosity in the LMD-ed parts efficiently.
Details
Keywords
Usman Tariq, Ranjit Joy, Sung-Heng Wu, Muhammad Arif Mahmood, Asad Waqar Malik and Frank Liou
This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive…
Abstract
Purpose
This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.
Design/methodology/approach
This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.
Findings
A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.
Originality/value
Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.