Advances in Manufacturing

Digital twin-based sustainable intelligent manufacturing: a review

Bin He, Kai-Jian Bai

2021, 9(1): 1-21. doi:10.1007/s40436-020-00302-5

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As the next-generation manufacturing system, intelligent manufacturing enables better quality, higher productivity, lower cost, and increased manufacturing flexibility. The concept of sustainability is receiving increasing attention, and sustainable manufacturing is evolving. The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures. Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications. To fully understand the intelligent manufacturing that provides the digital twin, this study reviews both technologies and discusses the sustainability of intelligent manufacturing. Firstly, the relevant content of intelligent manufacturing, including intelligent manufacturing equipment, systems, and services, is analyzed. In addition, the sustainability of intelligent manufacturing is discussed. Subsequently, a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology. Finally, combined with the current status, the future development direction of intelligent manufacturing is presented.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00302-5

Chatter identifi cation of thin-walled parts for intelligent manufacturing based on multi-signal processing

Dong-Dong Li, Wei-Min Zhang, Yuan-Shi Li, Feng Xue, Jürgen Fleischer

2021, 9(1): 22-33. doi:10.1007/s40436-020-00299-x

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Machine chatter is still an unresolved and challenging issue in the milling process, and developing an online chatter identification and process monitoring system towards smart manufacturing is an urgent requirement. In this paper, two indicators of chatter detection are investigated. One is the real-time variance of milling force signals in the time domain, and the other one is the wavelet energy ratio of acceleration signals based on wavelet packet decomposition in the frequency domain. Then, a novel classification concept for vibration condition, called slight chatter, is proposed and integrated successfully into the designed multi-classification support vector machine (SVM) model. Finally, a mapping model between image and chatter indicators is established via a distance threshold on the image. The multi-SVM model is trained by the results of three signals as an input. Experiment data and detection accuracy of the SVM model are verified in actual machining. The identification accuracy of 96.66% has proved that the proposed solution is feasible and effective. The presented method can be used to select optimized milling parameters to improve machining process stability and strengthen manufacturing system monitoring.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00299-x

Novel design and composition optimization of self-lubricating functionally graded cemented tungsten carbide cutting tool material for dry machining

Rityuj Singh Parihar, Raj Kumar Sahu, Srinivasu Gangi Setti

2021, 9(1): 34-46. doi:10.1007/s40436-020-00312-3

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The functionally graded cemented tungsten carbide (FGCC) is a suitable material choice for cutting tool applications due to balanced hardness and fracture toughness. The presence of cobalt and CaF₂ composition gradient in FGCC may enhance mechanical as well as antifriction properties. Therefore, structural design of selflubricating FGCC was proposed using Power law composition gradient model and thermal residual stresses (TRSs) as a key parameter. Wherein, S. Suresh and A. Mortensen model was adopted for estimation of TRS, and optimum composition gradient was identified at Power law exponent n=2. The designed material displayed compressive and tensile TRS at surface and core respectively; subsequently fabricated by spark plasma sintering and characterized via scanning electron microscope (SEM), indentation method. The agreement between experimental and analytical values of TRS demonstrated the effectiveness of intended design model in the composition optimization of self-lubricating FGCC. This work will be helpful in implementation of dry machining for clean and green manufacturing.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00312-3

Arduino-based low-cost electrical load tracking system with a long-range mesh network

Xin-Lin Wang, Bora Ha, Frank Andrew Manongi, Woo-Kyun Jung, Yusufu Abeid Chande Jande, Sung-Hoon Ahn

2021, 9(1): 47-63. doi:10.1007/s40436-020-00310-5

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A system that combines the advantage of the long-range (LoRa) communication method and the structural characteristics of a mesh network for an LoRa mesh network-based wireless electrical load tracking system is proposed. The system demonstrates considerable potential in reducing data loss due to environmental factors in farfield wireless energy monitoring. The proposed system can automatically control the function of each node by confirming the data source and eventually adjust the system structure according to real-time monitoring data without manual intervention. To further improve the sustainability of the system in outdoor environments, a standby equipment is designed to automatically ensure the normal operation of the system when the hardware of the base station fails. Our system is based on the Arduino board, which lowers the production cost and provides a simple manufacturing process. After conducting a long-term monitoring of a near-field smart manufacturing process in South Korea and the far-field energy consumption of rural households in Tanzania, we have proven that the system can be implemented in most regions, neither confined to a specific geographic location nor limited by the development of local infrastructure. This system comprises a smart framework that improves the quality of energy monitoring. Finally, the proposed big-data-technology-based power supply policy offers a new approach for prolonging the power supply time of off-grid power plants, thereby providing a guideline for more rural areas with limited power sources to utilize uninterrupted electricity.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00310-5

Pareto optimization of WEDM process parameters for machining a NiTi shape memory alloy using a combined approach of RSM and heat transfer search algorithm

Rakesh Chaudhari, Jay J. Vora, S. S. Mani Prabu, I. A. Palani, Vivek K. Patel, D. M. Parikh

2021, 9(1): 64-80. doi:10.1007/s40436-019-00267-0

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Machining of shape memory alloys (SMAs) without losing the shape memory effect could immensely extend their applications. Herein, the wire electric discharge machining process was used to machine NiTi-a shape memory alloy. The experimental methodology was designed using a Box-Behnken design approach of the response surface methodology. The effects of input variables including pulse on time, pulse off time, and current were investigated on the material removal rate, surface roughness, and microhardness. ANOVA tests were performed to check the robustness of the generated empirical models. Optimization of the process parameters was performed using a newly formulated, highly efficient heat transfer search algorithm. Validation tests were conducted and extended for analyzing the retention of the shape memory effect of the machined surface by differential scanning calorimetry. In addition, 2D and 3D Pareto curves were generated that indicated the trade-offs between the selected output variables during the simultaneous output variables using the multi-objective heat transfer search algorithm. The optimization route yielded encouraging results. Single objective optimization yielded a maximum material removal rate of 1.49 mm³/s, maximum microhardness 462.52 HVN, and minimum surface roughness 0.11 lm. The Pareto curves showed conflicting effects during the wire electric discharge machining of the shape memory alloy and presented a set of optimal non-dominant solutions. The shape memory alloy machined using the optimized process parameters even indicated a shape memory effect similar to that of the starting base material.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-019-00267-0

Intelligent modular design with holonic fuzzy agents

Egon Ostrosi, Alain-Jérôme Fougères, Zai-Fang Zhang, Josip Stjepandić

2021, 9(1): 81-103. doi:10.1007/s40436-020-00331-0

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Presently, modular designs use various technologies accompanied by multiple models. Although no integral solution is known, a plethora of approaches is used to resolve this trans disciplinary challenge, often by using local intelligence. However, the effective utilization of multiple models requires proper integration for them to work together as a cohesive system. This requirement calls for the development of intelligent models and tools that can be used for the development of intelligent modular products. Modular design based on these intelligent models and tools is called intelligent modular design. Intelligent modular design requires to be considered both dynamically and holistically by combining customer requirements, product functions, solutions, service specifications, and their fuzziness in order to structure a product into intelligent modules. This paper proposes the use of holonic fuzzy agents to fulfill both the properties of intelligent models and the requirements of intelligent modular design. The set of fuzzy function agents and their corresponding fuzzy solution agents are found from customization of the product-service system in the fuzzy function agent-fuzzy solution agent sub-network. On the basis of attractor agent recognition, the fuzzy function and fuzzy solution agents interact to form the holonic fuzzy module agents. Selfembedding of holonic fuzzy module agents, which is the fundamental property of the holonic structure, is also characterized by vertical and horizontal communication. The flexibility and agility of the software agent make the holonic structure of intelligent modules adaptable. An application illustrates the proposed intelligent modular design.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00331-0

Improving maintenance effi ciency and safety through a human-centric approach

C. Y. Siew, S. K. Ong, A. Y. C. Nee

2021, 9(1): 104-114. doi:10.1007/s40436-020-00334-x

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This paper presents an adaptive human-machine interface (HMI) that can provide appropriate sets of digital maintenance information and guidance to an operator during maintenance. It takes into consideration the expertise level of the operator and the maintenance context and progress. The proposed human-centric methodology considers the heart rate, intention, and expertise level of the operator, which can be captured using sensors during maintenance. A set of rules is formulated based on the sensor data to infer the state of the operator during a maintenance task. Based on the operator state, the adaptive HMI can augment the operator's senses using a scheme that combines visual, audio, and haptic guidance cues during maintenance to enhance the operator's ability to perceive information and perform maintenance tasks. Various schemes of visual, audio, and haptic cues are developed based on a comparison of the best practices obtained from experienced operators.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00334-x

Modeling, analysis, and optimization of dimensional accuracy of FDM-fabricated parts using defi nitive screening design and deep learning feedforward artifi cial neural network

Omar Ahmed Mohamed, Syed Hasan Masood, Jahar Lal Bhowmik

2021, 9(1): 115-129. doi:10.1007/s40436-020-00336-9

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Additive manufacturing (AM) technologies such as fused deposition modeling (FDM) rely on the quality of manufactured products and the process capability. Currently, the dimensional accuracy and stability of any AM process is essential for ensuring that customer specifications are satisfied at the highest standard, and variations are controlled without significantly affecting the functioning of processes, machines, and product structures. This study aims to investigate the effects of FDM fabrication conditions on the dimensional accuracy of cylindrical parts. In this study, a new class of experimental design techniques for integrated second-order definitive screening design (DSD) and an artificial neural network (ANN) are proposed for designing experiments to evaluate and predict the effects of six important operating variables. By determining the optimum fabrication conditions to obtain better dimensional accuracies for cylindrical parts, the time consumption and number of complex experiments are reduced considerably in this study. The optimum fabrication conditions generated through a second-order DSD are verified with experimental measurements. The results indicate that the slice thickness, part print direction, and number of perimeters significantly affect the percentage of length difference, whereas the percentage of diameter difference is significantly affected by the raster-to-raster air gap, bead width, number of perimeters, and part print direction. Furthermore, the results demonstrate that a second-order DSD integrated with an ANN is a more attractive and promising methodology for AM applications.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00336-9

Novel combined shield design for eye and face protection from COVID-19

Xiu-Ling Huang, Jin-Rong Yang, Yu-Xiang Sun, Yi-Wen Chen, Xiu-Mei Wang, Shui-Miao Du, Zi-Kai Hua

2021, 9(1): 130-135. doi:10.1007/s40436-020-00333-y

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The World Health Organization emphasized the importance of goggles and face shields for protection of medical personnel at the outbreak of the COVID-19 pandemic. Unsurprisingly, almost all countries suffered from a critical supply shortage of goggles and face shields, as well as many other types of personal protective equipment (PPE), for a long period, owing to the lack of key medical material supplies and the inefficiency of existing fabrication methods arising from the need to avoid crowds during the outbreak of COVID-19. In this paper, we propose a novel combined shield design for eye and face protection that can be rapidly fabricated using three-dimensional printing technology. The designed prototype eye-face shield is accessible to the general public, offering more possibilities for yield improvement in PPE during emergent infectious disease events such as COVID-19.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00333-y

Investigation into keyhole-weld pool dynamic behaviors based on HDR vision sensing of real-time K-TIG welding process through a steel/glass sandwich

Yan-Xin Cui, Yong-Hua Shi, Qiang Ning, Yun-Ke Chen, Bao-Ri Zhang

2021, 9(1): 136-144. doi:10.1007/s40436-020-00335-w

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To obtain a deep insight into keyhole tungsten inert gas welding, it is necessary to observe the dynamic behavior of the weld pool and keyhole. In this study, based on the steel/glass sandwich and high dynamic range camera, a vision system is developed and the keyhole-weld pool profiles are captured during the real-time welding process. Then, to analyze the dynamic behavior of the weld pool and keyhole, an image processing algorithm is proposed to extract the compression depth of the weld pool and the geometric parameters of the keyhole from the captured images. After considering the variations of these parameters over time, it was found that the front and rear lengths of the keyhole were dynamically adjusted internally and had opposite trends according to the real-time welding status while the length of the keyhole was in a quasi-steady state. The proposed vision-based observation method lays a solid foundation for studying the weld forming process and improving keyhole tungsten inert gas welding.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00335-w

Modal parameter determination and chatter prediction for blade whirling: a comparative study based on symmetric and asymmetric FRF

Lu-Yi Han, Ri-Liang Liu, Xin-Feng Liu

2021, 9(1): 145-159. doi:10.1007/s40436-020-00337-8

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Whirling has been adopted for the cost-effective machining of blade-shape components in addition to traditional end milling and flank milling processes. To satisfy the requirements of rotary forming in the blade whirling process, the workpiece must be clamped at both ends in suspension and rotated slowly during machining, which complicates the dynamics. This study aims to identify the dynamic characteristics within the blade whirling operation and present strategies for stability prediction. In this study, the dynamic characteristics of a whirling system are modeled by assuming symmetric and asymmetric parameters. Theoretical prediction frequency response function (FRF) results are compared with experimental results. Moreover, semi-discretization stability lobe diagrams (SLDs) obtained using the dynamic parameters of these models are investigated experimentally. The results show that the asymmetric model is more suitable for describing the whirling system, whereas the symmetric model presents limitations associated with the frequency range and location of measuring points. Finally, a set of airfoil propeller blade whirling operations is conducted to verify the prediction accuracy.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00337-8

Effect of ultrasonic impact treatment on the surface integrity of nickel alloy 718

Zheng Zhou, Chang-Feng Yao, Yu Zhao, Yang Wang, Liang Tan

2021, 9(1): 160-171. doi:10.1007/s40436-020-00329-8

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Ultrasonic impact treatment (UIT) is a type of surface strengthening technology that can improve the fatigue properties of materials by improving the surface quality, residual stress, and other aspects. In this study, the influence of ultrasonic impact parameters on the surface integrity of nickel alloy 718 was studied. The micro stress concentration caused by the surface morphology was also explored. The cosine and exponential decay functions were used to fit and characterize the distribution of residual stress and work hardening in the surface material. The results showed that the feed rate had the greatest influence on surface roughness, stress concentration, and surface residual stress. It was not appropriate to evaluate the surface hardening effect only by the number of impacts per unit area, the ultrasonic impact parameters such as feed speed and pre extrusion depth should also be considered. The grain refinement was obvious after UIT. The multiobjective optimization of machining parameters was performed with the objective of surface stress concentration and residual stress. A surface with a smaller surface stress concentration factor and larger compressive residual stress can be obtained simultaneously using medium linear velocity, medium pre extrusion depth, and smaller feed rate.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00329-8

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