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1. Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook

   https://doi.org/10.3390/machines9010013  

   Yang, Yuhang; Dong, Zhiqiao; Meng, Yuquan; Shao, Chenhui (January 2021, Machines)

   null (Ed.)

   High-fidelity characterization and effective monitoring of spatial and spatiotemporal processes are crucial for high-performance quality control of many manufacturing processes and systems in the era of smart manufacturing. Although the recent development in measurement technologies has made it possible to acquire high-resolution three-dimensional (3D) surface measurement data, it is generally expensive and time-consuming to use such technologies in real-world production settings. Data-driven approaches that stem from statistics and machine learning can potentially enable intelligent, cost-effective surface measurement and thus allow manufacturers to use high-resolution surface data for better decision-making without introducing substantial production cost induced by data acquisition. Among these methods, spatial and spatiotemporal interpolation techniques can draw inferences about unmeasured locations on a surface using the measurement of other locations, thus decreasing the measurement cost and time. However, interpolation methods are very sensitive to the availability of measurement data, and their performances largely depend on the measurement scheme or the sampling design, i.e., how to allocate measurement efforts. As such, sampling design is considered to be another important field that enables intelligent surface measurement. This paper reviews and summarizes the state-of-the-art research in interpolation and sampling design for surface measurement in varied manufacturing applications. Research gaps and future research directions are also identified and can serve as a fundamental guideline to industrial practitioners and researchers for future studies in these areas. 

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2. Imaging systems and techniques for fusion-based metal additive manufacturing: a review

   https://doi.org/10.3389/fmtec.2023.1271190  

   Balhara, Himanshu; Karthikeyan, Adithyaa; Hanchate, Abhishek; Nakkina, Tapan Ganatma; Bukkapatnam, Satish T. (December 2023, Frontiers in Manufacturing Technology)

   This study presents an overview and a few case studies to explicate the transformative power of diverse imaging techniques for smart manufacturing, focusing largely on variousin-situandex-situimaging methods for monitoring fusion-based metal additive manufacturing (AM) processes such as directed energy deposition (DED), selective laser melting (SLM), electron beam melting (EBM).In-situimaging techniques, encompassing high-speed cameras, thermal cameras, and digital cameras, are becoming increasingly affordable, complementary, and are emerging as vital for real-time monitoring, enabling continuous assessment of build quality. For example, high-speed cameras capture dynamic laser-material interaction, swiftly detecting defects, while thermal cameras identify thermal distribution of the melt pool and potential anomalies. The data gathered fromin-situimaging are then utilized to extract pertinent features that facilitate effective control of process parameters, thereby optimizing the AM processes and minimizing defects. On the other hand,ex-situimaging techniques play a critical role in comprehensive component analysis. Scanning electron microscopy (SEM), optical microscopy, and 3D-profilometry enable detailed characterization of microstructural features, surface roughness, porosity, and dimensional accuracy. Employing a battery of Artificial Intelligence (AI) algorithms, information from diverse imaging and other multi-modal data sources can be fused, and thereby achieve a more comprehensive understanding of a manufacturing process. This integration enables informed decision-making for process optimization and quality assurance, as AI algorithms analyze the combined data to extract relevant insights and patterns. Ultimately, the power of imaging in additive manufacturing lies in its ability to deliver real-time monitoring, precise control, and comprehensive analysis, empowering manufacturers to achieve supreme levels of precision, reliability, and productivity in the production of components. 

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3. Brief Paper: Multi-Task Brain-Inspired Learning for Interlinking Machining Dynamics With Parts Geometrical Deviations

   https://doi.org/10.1115/MSEC2024-125435  

   Hoang, Danny; Chen, Hanning; Imani, Mohsen; Chen, Ruimin; Imani, Farhad (June 2024, American Society of Mechanical Engineers)

   Abstract Increasing complexity, and requirements for the precise creation of parts, necessitate the use of computer numerical control (CNC) manufacturing. This process involves programmed instructions to remove material from a workpiece through operations such as milling, turning, and drilling. This manufacturing technique incorporates various process parameters (e.g., tools, spindle speed, feed rate, cut depth), leading to a highly complex operation. Additionally, interacting phenomena between the workpiece, tools, and environmental conditions further add to complexity which can lead to defects and poor product quality. Two main areas are of focus for an efficient automated system: monitoring and swift quality assessment. Within these areas, the critical aspects ascertaining the quality of a CNC manufacturing operation are: 1) Tool wear: the inherent deterioration of machine components caused by prolonged utilization, 2) Chatter: vibration that occurs during the machining process, and 3) Surface finish: the final product’s surface roughness. Many research domains tend to focus on just one of these areas while neglecting the interconnected influences of all three. Therefore, to capture a more holistic and comprehensive assessment of a manufacturing process, the overall product quality should be considered, as that’s what ultimately counts. The integration of CNC systems with in-situ monitoring devices such as acoustic sensors, high-speed cameras, and thermal cameras is aimed at understanding the underlying physical aspects of the CNC machining process, including tool wear, chatter, and surface roughness. The incorporation of these monitoring devices has allowed the use of artificial intelligence and machine learning (ML) in smart CNC systems with hopes of increasing productivity, minimizing downtime, and ensuring product quality. By capturing the underlying phenomena that occur during the manufacturing process, users hope to understand the interlinking dynamics for zero-defect automated manufacturing. However, even though the use of ML methods has yielded noteworthy results in analyzing in-situ process data for CNC manufacturing, the black-box nature of these models and their tendency to focus predominantly on single-task objectives rather than multi-task scenarios pose challenges. In real-world part creation and manufacturing scenarios, there is often a need to address multiple interconnected tasks simultaneously which demands models that can multitask effectively. Yet, many ML models designed and trained for singular objectives are limited in their applicability and efficiency in more complex multi-faceted environments. Addressing these challenges, we introduce MTaskHD, a novel multi-task framework, that leverages hyperdimensional computing (HDC) to effortlessly fuse data from various channels and process signals while characterizing quality within a multi-task manufacturing operation. Moreover, it yields interpretable outcomes, allowing users to understand the process behind predictions. In a real-world experiment conducted on a hybrid 5-axis CNC Deckel-Maho-Gildemeister, MTaskHD was implemented to forecast the quality of three distinct features: left 25.4 mm counterbore diameter, right 25.4 mm counterbore diameter, and 2.54 mm milled radius. Demonstrating remarkable performance, the model excelled in predicting the quality levels of all three features in its multi-task configuration with an F1-Score of 95.3%, outperforming alternative machine learning approaches, including support vector machines, Naïve Bayes, multi-layer perceptron, convolutional neural network, and time-LeNet. The inherent multi-task capability, robustness, and interpretability of HDC collectively offer a solution for comprehending intricate manufacturing dynamics and operations. 

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4. Digital Twinning and Optimization of Manufacturing Process Flows

   https://doi.org/10.1115/1.4063234  

   Lee, Hankang; Yang, Hui (November 2023, Journal of Manufacturing Science and Engineering)

   Abstract The new wave of Industry 4.0 is transforming manufacturing factories into data-rich environments. This provides an unprecedented opportunity to feed large amounts of sensing data collected from the physical factory into the construction of digital twin (DT) in cyberspace. However, little has been done to fully utilize the DT technology to improve the smartness and autonomous levels of small and medium-sized manufacturing factories. Indeed, only a small fraction of small and medium-sized manufacturers (SMMs) has considered implementing DT technology. There is an urgent need to exploit the full potential of data analytics and simulation-enabled DTs for advanced manufacturing. Hence, this paper presents the design and development of DT models for simulation optimization of manufacturing process flows. First, we develop a multi-agent simulation model that describes nonlinear and stochastic dynamics among a network of interactive manufacturing things, including customers, machines, automated guided vehicles (AGVs), queues, and jobs. Second, we propose a statistical metamodeling approach to design sequential computer experiments to optimize the utilization of AGV under uncertainty. Third, we construct two new graph models—job flow graph and AGV traveling graph—to track and monitor the real-time performance of manufacturing jobshops. The proposed simulation-enabled DT approach is evaluated and validated with experimental studies for the representation of a real-world manufacturing factory. Experimental results show that the proposed methodology effectively transforms a manufacturing jobshop into a new generation of DT-enabled smart factories. The sequential design of experiments effectively reduces the computation overhead of expensive simulations while optimally scheduling the AGV to achieve production throughput cost-effectively. This research is strongly promised to help SMMs fully utilize big data and DT technology for gaining competitive advantages in the global marketplace. 

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5. Photopolymerization of Stainless Steel 420 Metal Suspension: Printing System and Process Development of Additive Manufacturing Technology toward High-Volume Production

   https://doi.org/10.3390/jmmp8050191  

   Nguyen, Hoa Xuan; Poudel, Bibek; Qu, Zhiyuan; Kwon, Patrick; Chung, Haseung (October 2024, Journal of Manufacturing and Materials Processing)

   As the metal additive manufacturing (AM) field evolves with an increasing demand for highly complex and customizable products, there is a critical need to close the gap in productivity between metal AM and traditional manufacturing (TM) processes such as continuous casting, machining, etc., designed for mass production. This paper presents the development of the scalable and expeditious additive manufacturing (SEAM) process, which hybridizes binder jet printing and stereolithography principles, and capitalizes on their advantages to improve productivity. The proposed SEAM process was applied to stainless steel 420 (SS420) and the processing conditions (green part printing, debinding, and sintering) were optimized. Finally, an SS420 turbine fabricated using these conditions successfully reached a relative density of 99.7%. The SEAM process is not only suitable for a high-volume production environment but is also capable of fabricating components with excellent accuracy and resolution. Once fully developed, the process is well-suited to bridge the productivity gap between metal AM and TM processes, making it an attractive candidate for further development and future commercialization as a feasible solution to high-volume production AM. 

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