Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing

The degree of material anisotropy in additive manufacturing (AM) components is greatly influenced by the AM process parameters and machine setup. It is crucial to develop an effective technique for evaluating the material anisotropy in AM components to optimize AM process parameters and component design. This paper proposed a non-contact ultrasonic characterization method using the electromagnetic acoustic resonance technique to characterize the anisotropy of AM components. Various electromagnetic acoustic transducers (EMATs) were designed and utilized to characterize the material anisotropy and to determine the principal direction of the AM components. The degree of anisotropy in AM components was characterized using radial radiation EMATs. The relationship between the degree of anisotropy and the laser scanning angle was explored and further determined through the acoustic birefringence factor. Experimental results demonstrated that the anisotropy of AM components is intricately associated with the laser scanning angle, and specific angles can render the AM components isotropic. Moreover, understanding the principal directions is of significance for structural design and analyzing stress distribution in anisotropic components. Therefore, the principal directions of AM components were obtained by rotating the linear polarization EMAT. Changes in the resonance spectrum captured by the linear polarization EMAT while evaluating of principal directions were clearly illustrated, despite negligible alterations in linear ultrasonic features. Metallographic diagrams further validated the experimental findings. This investigation presented a highly accurate and reliable alternative for characterizing the anisotropy of AM components.

Multi-field coupling fatigue behavior of laser additively manufactured metallic materials: a review

While metal additive manufacturing (AM) promises substantial efficiency gains to the gas turbine manufacturing sector, uncertainty about the quality of parts produced via AM has been a significant hindrance to widespread implementation. Although high fidelity inspection techniques involving computed tomography (CT) and destructive testing have been effective for low volume development activities, new quality assurance solutions are needed that enable rapid, low-cost inspection of serial production AM components. Solar Turbines Incorporated is actively engaged in the development of inspection processes for high production volume AM part acceptance capability of combustion and turbine hot section components. Eddy current inspection (ECI) was identified as a potential non-destructive evaluation (NDE) solution. Based on the principles of electromagnetism, ECI has been successful on conventional materials for surface and near-surface crack detection. However, limited industry data is available regarding the effectiveness of ECI on AM material. The nature of AM-induced discontinuities, specifically for metal laser powder bed fusion (L-PBF) processing, demands high measurement resolution to detect fine features such as bulk porosity, lack of fusion and interlayer discontinuities. Development activities were thus executed to determine the suitability of ECI for detection of AM discontinuities. NDE training sets were printed with intentional variations in key L-PBF processing parameters to simulate the conditions which produce relevant AM material discontinuities. The training sets were then evaluated with a custom ECI system to determine the inspection capability and sensitivity. Inspections were conducted as a function of multiple input frequencies to determine the optimal tradeoff between measurement resolution and depth of penetration. Additional characterization of the training sets was conducted via metallographic analysis to establish correlations between the ECI results and AM material quality. An optimized multi-frequency inspection setting was identified to provide suitable measurement resolution for near surface AM material inspection. Correlations developed between ECI scan data and materials characterization results have enabled the ability to rapidly discriminate between varying discontinuity levels in AM components. Based on these efforts, ECI is considered a suitable inspection technique for materials produced via the L-PBF AM process.

The field of 3D printing is growing rapidly with the adaptation of disruptive technologies and modern materials. Additive Manufacturing (AM) component cannot be directly used for industrial application due its poor surface, mechanical, corrosion and tribology properties. Among those, surface finish and surface hardness play an important role on the wear and corrosion properties of AM component. So, it is important to address the aforementioned factors to further enhance the properties of the AM component. Hence, post-printing surface modification such as finishing and coatings are essential on the surface of AM components to improve the properties. Also, it is important to address the influence of various surface treatments, coatings, and surface oxidation on the surface quality of AM components, to provide a new insights, ideas, and recent innovations in this domain. Hence, the present chapter reviews the various advance coatings and treatments followed on the materials after the Additive Manufacturing process.KeywordsAdditive manufacturingSurface coatingsSurface modification techniquesSurface qualitySurface properties3D printing

Effect of heat treatments on microstructural evolution of additively manufactured and wrought 17-4PH stainless steel

Metal Additive Manufacturing (AM) has made significant strides in improving the mechanical characteristics and microstructures of fabricated materials. AM components often require post-processing operations, such as heat treatment to induce material homogenization and improve materials properties. The martensitic stainless-steel family is widely recognized for its excellent corrosion resistance, stiffness, and high tensile strength. It can be found in extensive applications such as aerospace, automotive, chemical, and food processing industries. However, given the inherent surface roughness of AM components, additional surface treatment processes are often required. The incorporation of laser remelting as a cutting-edge method in laser-based AM is geared toward reducing porosity and refining surface roughness. Yet, there is a significant gap in understanding how laser remelting influences mechanical properties and microstructure. Laser Powder Bed Fusion (LPBF) presents an opportunity for laser re-melting after each layer is deposited, thereby enhancing both surface quality and mechanical properties. The laser path can be programmed to traverse each fused layer with varying laser power and speed. This approach results in remelting and improved sintering, ensuring that all powder particles are effectively melted and enhancing the overall integrity of the fabricated materials. This study focuses on optimizing the process parameters for laser remelting to improve surface roughness and hardness. Surface roughness measurements are performed using digital microscopy, while Vickers hardness tests are conducted. Twenty-seven cubes were fabricated with three processing parameters factors and three levels. The factors are laser power, laser speed, and number of laser remelting passes, and their levels are 150W, 175W, and 200W; 900mm/s, 1200mm/s, 1500mm/s; one pass, two passes, and three passes, respectively. The findings reveal that laser re-melting significantly enhances hardness, and the outcome is contingent upon laser power and laser speed settings. The combination of a laser power of 150W, a laser speed of 1200 mm/s, and a single pass for laser re-melting results in the highest hardness, achieving 401HV, compared to the initial as-built value of 368HV. The main effect plot revealed that the number of laser remelting passes was not significant in terms of hardness but was significant in terms of surface roughness. The laser remelting process also leads to a substantial improvement in surface roughness when contrasted with the as-built sample. The most favorable surface roughness results are achieved using a laser power of 175W, a laser speed of 900 mm/s, and three passes of laser re-melting, yielding a surface roughness of 7.1 μm, as opposed to the initial as-built value of 45 μm.

The porosity of additive manufacturing (AM) components exerts a profound influence on their mechanical properties, thereby impeding their widespread engineering adoption. Current porosity evaluation methodologies predominantly rely on destructive testing approaches, underscoring the pressing demand for a non-destructive testing (NDT) method enabling precise and in-situ porosity evaluation. In this paper, we propose a novel method for porosity evaluation of AM components by deep learning (DL)-assisted nonlinear ultrasonic technique. Experimental investigations revealed that ultrasonic nonlinear responses exhibit pronounced sensitivity to subtle variations in AM component porosity. However, the complex mapping between porosity and the nonlinear response of ultrasonic waves, stemming from the intricate interplay of multiple printing parameters, poses a significant challenge for traditional modeling approaches relying solely on the nonlinear coefficient. To address this challenge and enable high-precision porosity evaluation, we established a convolutional neural network bidirectional long short-term memory attention network. The nonlinear response of ultrasonic waves was extracted using the phase-reversal method and employed as the proposed network’s input. The trained network achieved high-precision prediction of porosity. Comparative analyzes between the predicted results obtained by using phase-reversed signals and conventional evaluation signals highlighted the indispensable role of nonlinear ultrasonic responses in capturing porosity-dependent features, providing a explanation for the established DL method. The proposed method reveals the complementary integration of nonlinear ultrasonic waves and the DL method: the former provides physical sensitivity to porosity evolution, while the latter resolves the ill-posed nature of inverse problems. This synergistic framework establishes a novel solution for high-precision NDT of AM components.

Additively manufactured (AM) components’ surface finishing is crucial in adopting them for intended applications in challenging environments involving fatigue, corrosion, high temperature, and nuclear radiation. In our prior research, chempolishing (C) was utilized as an electroless etching process that uniformly smoothens complex AM components’ accessible interior and exterior surfaces (Tyagi et al. Additive Manufacturing, 25:32–38, 22). A wide range of electropolishing (E) has been demonstrated for AM surface finishing. However, electropolishing can impact a surface that can be juxtaposed to counter electrode and can yield a very smooth surface to sub-micrometer level roughness. However, a knowledge gap exists about the impact of applying both approaches on the same surface one after another and what new advantages may arise because of combining two methods. This paper uses dual-stage liquid-based surface finishing strategies produced by alternating the chempolishing (C) and electropolishing (E) steps. Two dual-stage surface finishing approaches, i.e., chempolishing followed by electropolishing (CE) and electropolishing followed by chempolishing (EC), were performed on the 316 stainless AM steel component. Impacts of EC and CE approaches were compared with single-stage C and E surface finishing approaches. An optical microscope and mechanical profilometer were utilized to investigate the wide range of surface roughness parameters. CE and EC produced Ra ~ 1.4 µm and ~ 1.6 µm, respectively. Surface roughness on CE- and EC-treated AM samples was lower than those individually treated by C and E approaches. Scanning electron microscopy provided further insights into the microstructural difference between CE- and EC-treated AM samples. This paper reports a liquid contact angle study on CE- and EC-treated AM samples to provide insights into the relative difference in surface energy that is crucial for making coatings on AM parts. A spectroscopic reflectance study was also employed to register the difference in physical properties of AM components treated with CE and EC approaches. This study reveals industrially practicable interior and exterior surface finishing approaches for complex AM metal components that require minimum tooling and real-time process monitoring.

Additive manufacturing (AM) can produce highly complex engineering components that are either extremely challenging for the conventional subtractive manufacturing route or not possible otherwise. High surface roughness can make an AM component highly vulnerable to premature failure during fatigue loading. Post-processing aiming to reduce surface roughness is essential to make as produced AM parts functional. We have explored electropolishing route to achieve optimum surface roughness and surface chemistry. We have performed electropolishing treatment on the steel AM parts around 70 °C in an electrolyte comprising the phosphoric acid and sulfuric acid. Profilometry and scanning electron microscopy were performed to study the electropolished and unpolished areas. Optical profilometry study showed that one needs to remove nearly ∼200 μm material from the surface to achieve very smooth surface. Electropolishing was effective in reducing the surface Ra roughness from ∼2 μm rms to ∼0.07 μm rms. Such low rms roughness makes an AM component suitable for almost every engineering application for which a smooth surface is required. Scanning electron microscopy revealed that electropolished area on AM component possessed distinctively different microstructure as compared to the untreated surface of an AM component. We also conducted the compositional analysis of the electropolished area to investigate the possibility of residual contamination from the electropolishing process. Our study revealed that electropolishing is a highly promising route for improving the surface finishing of AM components.

6 - Additive manufacturing: process and microstructure

Hydrodynamic flexible spindle (HydroFlex) polishing for internal surfaces of complex channels with high aspect ratio

Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of artificial intelligence (AI) in the manufacturing sector has been ignored in the literature. Therefore, this review provides comprehensive information on smart mechanisms and systems emphasizing additive, subtractive and/or hybrid manufacturing processes in a collaborative, predictive, decisive, and intelligent environment. Relevant electronic databases were searched, and 248 articles were selected for qualitative synthesis. Our review suggests that significant improvements are required in connectivity, data sensing, and collection to enhance both subtractive and additive technologies, though the pervasive use of AI by machines and software helps to automate processes. An intelligent system is highly recommended in both conventional and non-conventional subtractive manufacturing (SM) methods to monitor and inspect the workpiece conditions for defect detection and to control the machining strategies in response to instantaneous output. Similarly, AM product quality can be improved through the online monitoring of melt pool and defect formation using suitable sensing devices followed by process control using machine learning (ML) algorithms. Challenges in implementing intelligent additive and subtractive manufacturing systems are also discussed in the article. The challenges comprise difficulty in self-optimizing CNC systems considering real-time material property and tool condition, defect detections by in-situ AM process monitoring, issues of overfitting and underfitting data in ML models and expensive and complicated set-ups in hybrid manufacturing processes.

Chapter 12 - In situ monitoring of metal additive manufacturing process: a review

Complex geometry, variable macrostructure, anisotropy, distributed defects, surface roughness, and residual stresses of additively manufactured (AM) components challenge nondestructive evaluation processes developed and validated for conventional manufacturing. Electromagnetic and eddy current (EC) technologies were identified as promising for in-process, real-time monitoring, and post-process examination of AM components. Powder and solid specimens were prepared for electromagnetic property measurements and technique development and optimization. The post-process specimens were fabricated with roughness representative of surface conditions during fabrication inside the AM machine. Artificial and natural discontinuities were fabricated with the electrical discharge machining process and by varying the laser fusion parameters. Extensive computed modeling was conducted to develop an optimized array EC (AEC) sensor and technique for AM real-time monitoring. The AEC sensor could withstand temperatures up to 100ºC. Computational modeling was conducted to investigate the likelihood of metal powder disturbance by the EC sensor electromagnetic field. A real-time data acquisition system was assembled and integrated with an open-architecture laser powder bed fusion (L-PBF) test bed. The EC system performance for real-time monitoring of the L-PBF process was demonstrated through layer-by-layer testing of Inconel 625 coupons, with AM built discontinuities and lack of fusion. The L-PBF process was representative of a typical AM process used for super alloys and steels with preheat of substrate plate and AM test specimens to 100ºC. Existing AEC equipment and techniques were used for post-process examination of AM solid specimens at room temperatures. The AEC technology demonstrated excellent sensitivity to seeded and natural surface and near-surface subsurface discontinuities and surface topography during real-time monitoring and post-processing. Electromagnetic and EC techniques were demonstrated to examine powder before fabrication. The application of modeling tools reduced developmental time and improved efficiency. Further research and development was recommended to improve data acquisition process and transition AEC monitoring technology to other AM processes.

The ability of additive manufacturing (AM) processes to produce components with virtually any geometry presents a unique challenge in terms of quantifying the dimensional quality of the part. In this paper, a novel spectral graph theory (SGT) approach is proposed for resolving the following critical quality assurance concern in the AM: how to quantify the relative deviation in dimensional integrity of complex AM components. Here, the SGT approach is demonstrated for classifying the dimensional integrity of standardized test components. The SGT-based topological invariant Fiedler number (λ2) was calculated from 3D point cloud coordinate measurements and used to quantify the dimensional integrity of test components. The Fiedler number was found to differ significantly for parts originating from different AM processes (statistical significance p-value <1%). By comparison, prevalent dimensional integrity assessment techniques, such as traditional statistical quantifiers (e.g., mean and standard deviation) and examination of specific facets/landmarks failed to capture part-to-part variations, proved incapable of ranking the quality of test AM components in a consistent manner. In contrast, the SGT approach was able to consistently rank the quality of the AM components with a high degree of statistical confidence independent of sampling technique used. Consequently, from a practical standpoint, the SGT approach can be a powerful tool for assessing the dimensional integrity of the AM components, and thus encourage wider adoption of the AM capabilities.