Compared to conventional machining (CM), ultrasonic vibration-assisted machining (UVAM) with high-frequency and small-amplitude has exhibited good cutting performances for advanced materials. In recent years, advances in ultrasonic generator, ultrasonic transducer, and horn structures have led to the rapid progress in the development of UVAM. Following this trend, numerous new design requirements and theoretical concepts have been proposed and studied successively, however, very few studies have been conducted from a comprehensive perspective. To address this gap in the literature and understanding the development trend of UVAM, a critical overview of UVAM is presented in this study, covering different vibration-assisted machining styles, device architectures, and theoretical analysis. This overview covers the evolution of typical hardware systems used to achieve vibratory motions from the one-dimensional UVAM to three-dimensional UVAM, the discussion of cutting characteristics with periodic separation between the tools and workpiece and the analysis of processing properties. Challenges for UVAM include ultrasonic vibration systems with high power, large amplitude, and high efficiency, as well as theoretical research on the dynamics and cutting characteristics of UVAM. Consequently, based on the current limitations and challenges, device improvement and theoretical breakthrough play a significant role in future research on UVAM.

Review of ultrasonic vibration-assisted machining in advanced materials

Additive manufacturing (AM) or 3D printing is growing rapidly in the manufacturing industry and has gained a lot of attention from various fields owing to its ability to fabricate parts with complex features. The reliability of the 3D printed parts has been the focus of the researchers to realize AM as an end-part production tool. Machine learning (ML) has been applied in various aspects of AM to improve the whole design and manufacturing workflow especially in the era of industry 4.0. In this review article, various types of ML techniques are first introduced. It is then followed by the discussion on their use in various aspects of AM such as design for 3D printing, material tuning, process optimization, in situ monitoring, cloud service, and cybersecurity. Potential applications in the biomedical, tissue engineering and building and construction will be highlighted. The challenges faced by ML in AM such as computational cost, standards for qualification and data acquisition techniques will also be discussed. In the authors’ perspective, in situ monitoring of AM processes will significantly benefit from the object detection ability of ML. As a large data set is crucial for ML, data sharing of AM would enable faster adoption of ML in AM. Standards for the shared data are needed to facilitate easy sharing of data. The use of ML in AM will become more mature and widely adopted as better data acquisition techniques and more powerful computer chips for ML are developed.

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A review on machine learning in 3D printing: applications, potential, and challenges

Additive manufacturing enables the printing of metallic parts, such as customized implants for patients, durable single-crystal parts for use in harsh environments, and the printing of parts with site-specific chemical compositions and properties from 3D designs. However, the selection of alloys, printing processes and process variables results in an exceptional diversity of microstructures, properties and defects that affect the serviceability of the printed parts. Control of these attributes using the rich knowledge base of metallurgy remains a challenge because of the complexity of the printing process. Transforming 3D designs created in the virtual world into high-quality products in the physical world needs a new methodology not commonly used in traditional manufacturing. Rapidly developing powerful digital tools such as mechanistic models and machine learning, when combined with the knowledge base of metallurgy, have the potential to shape the future of metal printing. Starting from product design to process planning and process monitoring and control, these tools can help improve microstructure and properties, mitigate defects, automate part inspection and accelerate part qualification. Here, we examine advances in metal printing focusing on metallurgy, as well as the use of mechanistic models and machine learning and the role they play in the expansion of the additive manufacturing of metals. Several key industries routinely use metal printing to make complex parts that are difficult to produce by conventional manufacturing. Here, we show that a synergistic combination of metallurgy, mechanistic models and machine learning is driving the continued growth of metal printing.

Metallurgy, mechanistic models and machine learning in metal printing

Vegetable oil-based nanofluid minimum quantity lubrication turning: Academic review and perspectives

Additive manufacturing (AM) is the process of joining materials layer by layer to fabricate products based on 3D models Due to the layer-by-layer nature of AM, parts with complex geometries, integrated assemblies, customized geometry or multifunctional designs can now be manufactured more easily than traditional subtractive manufacturing Path planning in AM is an important step in the process of manufacturing products The final fabricated qualities, properties, etc, will be different when using different path strategies, even using the same AM machine and process parameters Currently, increasing research studies have been published on path planning strategies with different aims Due to the rapid development of path planning in AM and various newly proposed strategies, there is a lack of comprehensive reviews on this topic Therefore, this paper gives a comprehensive understanding of the current status and challenges of AM path planning This paper reviews and discusses path planning strategies in three categories: improving printed qualities, saving materials/time and achieving objective printed properties The main findings of this review include: new path planning strategies can be developed by combining some of the strategies in literature with better performance; a path planning platform can be developed to help select the most suitable path planning strategy with required properties; research on path planning considering energy consumption can be carried out in the future; a benchmark model for testing the performance of path planning strategies can be designed; the trade-off among different fabricated properties can be considered as a factor in future path planning design processes; and lastly, machine learning can be a powerful tool to further improve path planning strategies in the future

https://www.mdpi.com/2072-666X/11/7/633/pdf

Path Planning Strategies to Optimize Accuracy, Quality, Build Time and Material Use in Additive Manufacturing: A Review.

Structural coloration of metallic surfaces with micro/nano-structures induced by elliptical vibration texturing

Machine learning for continuous liquid interface production: Printing speed modelling

Diffraction gratings are capable of splitting and diffracting parallel white light into a wide diffractive spectrum containing light with different wavelengths travelling in different directions. The apparent angle-dependent color effect is a form of structural coloration. In this paper, a rendering strategy for grating-induced high-resolution image reproduction has been demonstrated based on tool path optimization. The grating structures are generated by ultrasonic modulation cutting due to the cutting depth modulation. The high-resolution image rendering is accomplished by tailoring the interaction between visible light and grating structures through the design and optimization of the distribution of machined grating arrays. Fabricating the desired grating arrays requires a complex objective tool path with ever-changing discontinuous step velocity profiles, which is not achievable for any existing machine tools considering the limited acceleration capability. Unlike conventional toolpath optimization methods, the proposed research aims to relate the cutting process to high-resolution image rendering performance and optimize the quality of machined images through optimally approximating the objective tool velocity-location curve with a series of parametric splines in terms of minimum overall velocity error. A recursive optimization method has been developed to ensure the global optimum of the tool velocity profile considering the physical limitation of motion axes. The fitting performance of proposed method is quantitatively analyzed and evaluated through the simulation and experiment by comparing the real-time velocity-location profiles to the objective curves. In addition, the reproduction quality of machined images is evaluated by measuring the similarity between the reproduced and original images with both simulation and experimental results.

Global tool path optimization of high-resolution image reproduction in ultrasonic modulation cutting for structural coloration

An ultrasonic elliptical vibration tool utilizing the coupled resonant vibration modes is the key component in the elliptical vibration cutting/texturing process, which has been successfully applied to ultra-precision machining and surface texturing. In this paper, an analytical approach is proposed to analyze the resonant frequency and mode shapes of the ultrasonic elliptical vibration tool. A new design of ultrasonic elliptical vibration tool based on a portal frame structure is presented and analyzed using the proposed model. The model assumes Euler-Bernoulli beams and utilizes the transfer matrix technique to reduce the order of the system to only six variables. The model can be utilized to provide a systematic approach for an optimal design and be extended to dynamic analysis of the tool for study of machine-tool dynamics. Finite element simulation results as well as experimental data based on the prototype design are presented to verify the model. An application of the proposed tool is also demonstrated in machining micro/nano-structured surfaces for structural coloration.

Yang Yang

Papers

Structural coloration of metallic surfaces with micro/nano-structures induced by elliptical vibration texturing

Machine learning for continuous liquid interface production: Printing speed modelling

Global tool path optimization of high-resolution image reproduction in ultrasonic modulation cutting for structural coloration

Vibration Analysis and Development of an Ultrasonic Elliptical Vibration Tool Based on a Portal Frame Structure

An ultrafast 2-D non-resonant cutting tool for texturing micro-structured surfaces