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This paper aims to investigate a stereo vision-based hybrid (additive and subtractive) manufacturing process using direct laser metal deposition, computer numerical control (CNC) machining and in-process scanning to repair metallic components automatically. The focus of this work was to realize automated alignment and adaptive tool path generation that can repair metallic components after a single setup.

Stereo vision was used to detect the defect area for automated alignment. After the defect is located, a laser displacement sensor is used to scan the defect area before and after laser metal deposition. The scan is then processed by an adaptive algorithm to generate a tool path for repairing the defect.

The hybrid manufacturing processes for repairing metallic component combine the advantages of free-form fabrication from additive manufacturing with the high-accuracy offered by CNC machining. A Ti-6Al-4V component with a manufacturing defect was repaired by the proposed process. Compared to previous research on repairing worn components, introducing stereo vision and laser scanning dramatically simplifies the manual labor required to extract and reconstruct the defect area’s geometry.

This paper demonstrates an automated metallic component repair process by integrating stereo vision and a laser displacement sensor into a hybrid manufacturing system. Experimental results and microstructure analysis shows that the defect area could be repaired feasibly and efficiently with acceptable heat affected zone using the proposed approach.

This paper aims to achieve Ti-6Al-4V from Ti, Al and V elemental powder blends using direct laser deposition (DLD) and to understand the effects of laser transverse speed and laser power on the initial fabrication of deposit’s microstructure and Vickers hardness.

Two sets of powder blends with different weight percentage ratio for three elemental powder were used during DLD process. Five experiments with different processing parameters were performed to evaluate how microstructure and Vickers hardness change with laser power and laser transverse speed. Energy dispersive X-ray spectroscopy, optical microscopy and Vickers hardness test were used to analyze deposits’ properties.

This paper reveals that significant variance of elemental powder’s size and density would cause lack of weight percentage of certain elements in final part and using multiple coaxial powder nozzles design would be a solution. Also, higher laser power or slower laser transverse speed tend to benefit the formation of finer microstructures and increase Vickers hardness.

This paper demonstrates a new method to fabricate Ti-6Al-4V and gives out a possible weight percentage ratio 87:7:6 for Ti:Al:V at powder blends during DLD process. The relationship between microstructure and Vickers hardness with laser power and laser transverse speed would provide valuable reference for people working on tailoring material properties using elemental powder method.

This study aims to develop a holistic method that integrates finite element modeling, machine learning, and experimental validation to propose processing windows for optimizing the laser powder bed fusion (LPBF) process specific to the Al-357 alloy.

Validation of a 3D heat transfer simulation model was conducted to forecast melt pool dimensions, involving variations in laser power, laser scanning speed, powder bed thickness (PBT) and powder bed pre-heating (PHB). Using the validated model, a data set was compiled to establish a back-propagation-based machine learning capable of predicting melt pool dimensional ratios indicative of printing defects.

The study revealed that, apart from process parameters, PBT and PHB significantly influenced defect formation. Elevated PHBs were identified as contributors to increased lack of fusion and keyhole defects. Optimal combinations were pinpointed, such as 30.0 µm PBT with 90.0 and 120.0 °C PHBs and 50.0 µm PBT with 120.0 °C PHB.

The integrated process mapping approach showcased the potential to expedite the qualification of LPBF parameters for Al-357 alloy by minimizing the need for iterative physical testing.

The purpose of this research is to enhance the Laser Powder Bed Fusion (LPBF) additive manufacturing technique by addressing its susceptibility to defects, specifically lack of fusion. The primary goal is to optimize the LPBF process using a digital twin (DT) approach, integrating physics-based modeling and machine learning to predict the lack of fusion.

This research uses finite element modeling to simulate the physics of LPBF for an AISI 316L stainless steel alloy. Various process parameters are systematically varied to generate a comprehensive data set that captures the relationship between factors such as power and scan speed and the quality of fusion. A novel DT architecture is proposed, combining a classification model (recurrent neural network) with reinforcement learning. This DT model leverages real-time sensor data to predict the lack of fusion and adjusts process parameters through the reinforcement learning system, ensuring the system remains within a controllable zone.

This study's findings reveal that the proposed DT approach successfully predicts and mitigates the lack of fusion in the LPBF process. By using a combination of physics-based modeling and machine learning, the research establishes an efficient framework for optimizing fusion in metal LPBF processes. The DT's ability to adapt and control parameters in real time, guided by machine learning predictions, provides a promising solution to the challenges associated with lack of fusion, potentially overcoming the traditional and costly trial-and-error experimental approach.

Originality lies in the development of a novel DT architecture that integrates physics-based modeling with machine learning techniques, specifically a recurrent neural network and reinforcement learning.

This paper aims to manufacture Ti6Al4V/TiC functionally graded material (FGM) by direct laser deposition (DLD) using Ti6Al4V and TiC powder. The objective is to investigate the effect of process parameters and TiC composition on microstructure, Vickers hardness and mechanical properties.

Powder blends with three different volume percentages of Ti6Al4V and TiC were used as feed material for DLD process. Five experiments with different values of laser power and scan speed were conducted to investigate the effect on microstructure and Vickers hardness for different compositions of feed material. Mini-tensile tests were performed to evaluate the mechanical properties of the FGM samples. Digital image correlation (DIC) was applied to estimate Young’s modulus and ultimate tensile stress (UTS) of heterogeneous material.

This paper indicates that primary carbide, eutectic carbide and un-melted carbide phases are formed in the FGM deposit. As the energy density was increased, the primary and secondary dendrite arm spacing was found to increase. As TiC composition was increased, Young’s modulus increased and UTS decreased. The dendritic morphology of primary TiC growth was expected to cause low resistance for crack propagation, causing lower UTS values. Tensile specimens cut in vertical orientation were observed to possess higher values of Young’s modulus in comparison with specimens cut horizontally at low carbon content.

Current work presents unique and original contributions from the study of miniature FGM tensile specimens using DIC method. It investigates the effect of specimen orientation and TiC content on Young’s modulus and UTS. The relationship between energy density and dendritic arm spacing was evaluated. The relationship between laser power and scan speed with microstructure and Vickers hardness was investigated.

During the powder bed fusion process, thermal distortion is one big problem owing to the thermal stress caused by the high cooling rate and temperature gradient. For the purpose of avoiding distortion caused by internal residual stresses, support structures are used in most selective laser melting (SLM) process especially for cantilever beams because they can assist the heat dissipation. Support structures can also help to hold the work piece in its place and reduce volume of the printing materials. The mitigation of high thermal gradients during the manufacturing process helps to reduce thermal distortion and thus alleviate cracking, curling, delamination and shrinkage. Therefore, this paper aims to study the displacement and residual stress evolution of SLMed parts.

The objective of this study was to examine and compare the distortion and residual stress properties of two cantilever structures, using both numerical and experimental methods. The part-scale finite element analysis modeling technique was applied to numerically analyze the overhang distortions, using the layer-by-layer model for predicting a part scale model. The validation experiments of these two samples were built in a SLM platform. Then average displacement of the four tip corners and residual stress on top surface of cantilever beams were tested to validate the model.

The validation experiments results of average displacement of the four tip corners and residual stress on top surface of cantilever beams were tested to validate the model. It was found that they matched well with each other. From displacement and residual stress standpoint, by introducing two different support structure, two samples with the same cantilever beam can be successfully printed. In terms of reducing wasted support materials, print time and high surface quality, sample with less support will need less post-processing and waste energy.

Numerical modeling in this work can be a very useful tool to parametrically study the feasibility of support structures of SLM parts in terms of residual stresses and deformations. It has the capability for fast prediction in the SLMed parts.

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.

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.

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.

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.

This paper aims to summarize the microstructure characterization of parts that were produced using a hybrid manufacturing process consisting of laser metal deposition (LMD) and friction stir processing (FSP). This research was conducted to investigate the evolution of the microstructure following FSP and LMD and to study the possibility of producing or repairing parts with a forged-like microstructure using this hybrid technique.

The microstructure of the nugget regions obtained in the substrate weld, stir over deposit and deposit over stir experiments was investigated.

Highly refined grain size in the order of 1-2 μm was observed where FSP was performed over laser-deposited Ti–6Al–4V. Large equiaxed grains were observed in the experiment where subsequent deposition was carried over the stir. A decreasing grain size was also observed in the dilution zone (DZ) inside the nugget from the stir surface to the bottom of the DZ.

A highly refined microstructure formed from FSP is able to increase the fatigue life by delaying the fatigue crack initiation. Peters et al. (1980) reported that reducing the grain size from 12-15 μm to 1-2 μm in an equiaxed Ti–6Al–4V alloy corresponded with about 25 per cent increase in fatigue strengths at 10,000,000 cycles.

This proposed technical approach is a novel and effective method to produce forged-like parts using a metal additive manufacturing process.

This paper sets out to summarize the current research, development, and integration of a hybrid process to produce high‐temperature metallic materials. It seeks to present the issues and solutions, including the understanding of the direct laser deposition process, and automated process planning.

Research in simulation and modeling, process development, integration, and actual part building for hybrid processing are discussed.

Coupling additive and subtractive processes into a single workstation, the integrated process, or hybrid process, can produce metal parts with machining accuracy and surface finish. Therefore, the hybrid process is potentially a very competitive process to fabricate metallic structures.

Rapid prototyping technology has been of interest to various industries that are looking for a process to produce/build a part directly from a CAD model in a short time. Among them, the direct laser deposition process is one of the few processes which directly manufacture a fully dense metal part without intermediate steps. Presented in this paper is the research, development, and system integration to resolve the challenges of the direct metal deposition process including building overhang structures, producing precision surfaces, and making parts with complex structures.

The melt pool created by a laser is one of the most important factors affecting the quality of the deposit in a laser metal deposition (LMD) process. The high‐intensity infrared (IR) radiation emitted by the melt pool saturates a conventional camera sensor preventing useful data acquisition. The purpose of this paper is to discuss the development of a low‐cost vision system to monitor the size of the melt pool for in‐process quality control of the deposit.

According to the black body radiation theory, there is no radiation emitted in the ultraviolet (UV) region from the melt pool created in the LMD process. IR radiation and visible light are the only radiations inherent to the LMD process. UV illumination is utilized along with narrow band pass filters on a USB camera to achieve a clear image of the melt pool while IR radiation of the process is blocked out. The melt pool size and shape were closely monitored during the deposition process.

A clear image of the melt pool was obtained using a relatively low‐cost imaging system during laser deposition process.

Traditional approaches to vision systems in high‐intensity processes use a high‐speed video camera fitted with IR filters to prevent saturation of the camera sensor. Such systems are usually complex and expensive to run and maintain. This paper demonstrates an alternative and lower cost method to achieve in process monitoring in an LMD process.