Advances in Manufacturing

Precision micro-milling process: state of the art

Lorcan O'Toole, Cheng-Wei Kang, Feng-Zhou Fang

2021, 9(2): 173-205. doi:10.1007/s40436-020-00323-0

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Micro-milling is a precision manufacturing process with broad applications across the biomedical, electronics, aerospace, and aeronautical industries owing to its versatility, capability, economy, and efficiency in a wide range of materials. In particular, the micro-milling process is highly suitable for very precise and accurate machining of mold prototypes with high aspect ratios in the micro-domain, as well as for rapid micro-texturing and micro-patterning, which will have great importance in the near future in bio-implant manufacturing. This is particularly true for machining of typical difficult-to-machine materials commonly found in both the mold and orthopedic implant industries. However, inherent physical process constraints of machining arise as macro-milling is scaled down to the microdomain. This leads to some physical phenomena during micro-milling such as chip formation, size effect, and process instabilities. These dynamic physical process phenomena are introduced and discussed in detail. It is important to remember that these phenomena have multi-factor effects during micro-milling, which must be taken into consideration to maximize the performance of the process. The most recent research on the micro-milling process inputs is discussed in detail from a process output perspective to determine how the process as a whole can be improved. Additionally, newly developed processes that combine conventional micro-milling with other technologies, which have great prospects in reducing the issues related to the physical process phenomena, are also introduced. Finally, the major applications of this versatile precision machining process are discussed with important insights into how the application range may be further broadened.

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Prediction of product roughness, profile, and roundness using machine learning techniques for a hard turning process

Chunling Du, Choon Lim Ho, Jacek Kaminski

2021, 9(2): 206-215. doi:10.1007/s40436-021-00345-2

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High product quality is one of key demands of customers in the field of manufacturing such as computer numerical control (CNC) machining. Quality monitoring and prediction is of great importance to assure high-quality or zero defect production. In this work, we consider roughness parameter R_a, profile deviation P_t and roundness deviation R_ONt of the machined products by a lathe. Intrinsically, these three parameters are much related to the machine spindle parameters of preload, temperature, and rotations per minute (RPMs), while in this paper, spindle vibration and cutting force are taken as inputs and used to predict the three quality parameters. Power spectral density (PSD) based feature extraction, the method to generate compact and well-correlated features, is proposed in details in this paper. Using the efficient features, neural network based machine learning technique turns out to be able to result in high prediction accuracy with R² score of 0.92 for roughness, 0.86 for profile, and 0.95 for roundness.

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Multi-verse optimizer based parameters decision with considering tool life in dry hobbing process

Heng-Xin Ni, Chun-Ping Yan, Shen-Fu Ni, Huan Shu, Yu Zhang

2021, 9(2): 216-234. doi:10.1007/s40436-021-00349-y

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Dry hobbing has received extensive attention for its environmentally friendly processing pattern. Due to the absence of lubricants, hobbing process is highly dependent on process parameters combination since using unreasonable parameters tends to affect the machining performance. Besides, the consideration of tool life is frequently ignored in gear hobbing. Thus, to settle the above issues, a multi-objective parameters decision approach considering tool life is developed. Firstly, detailed quantitative analysis between process parameters and hobbing performance, i.e., machining time, production cost and tool life is introduced. Secondly, a multi-objective parameters decision-making model is constructed in search for optimum cutting parameters (cutting velocity v, axial feed rate f_a) and hob parameters (hob diameter d₀, threads z₀). Thirdly, a novel algorithm named multi-objective multi-verse optimizer (MOMVO) is utilized to solve the presented model. A case study is exhibited to show the feasibility and reliability of the proposed approach. The results reveal that (i) a balance can be achieved among machining time, production cost and tool life via appropriate process parameters determination; (ii) optimizing cutting parameters and hob parameters simultaneously contributes to optimal objectives; (iii) considering tool life provides usage precautions support and process parameters guidance for practical machining.

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Thermal error modeling based on BiLSTM deep learning for CNC machine tool

Pu-Ling Liu, Zheng-Chun Du, Hui-Min Li, Ming Deng, Xiao-Bing Feng, Jian-Guo Yang

2021, 9(2): 235-249. doi:10.1007/s40436-020-00342-x

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The machining accuracy of computer numerical control machine tools has always been a focus of the manufacturing industry. Among all errors, thermal error affects the machining accuracy considerably. Because of the significant impact of Industry 4.0 on machine tools, existing thermal error modeling methods have encountered unprecedented challenges in terms of model complexity and capability of dealing with a large number of time series data. A thermal error modeling method is proposed based on bidirectional long short-term memory (BiLSTM) deep learning, which has good learning ability and a strong capability to handle a large group of dynamic data. A four-layer model framework that includes BiLSTM, a feedfor-ward neural network, and the max pooling is constructed. An elaborately designed algorithm is proposed for better and faster model training. The window length of the input sequence is selected based on the phase space reconstruction of the time series. The model prediction accuracy and model robustness were verified experimentally by three validation tests in which thermal errors predicted by the proposed model were compensated for real workpiece cutting. The average depth variation of the workpiece was reduced from approximately 50 lm to less than 2 lm after compensation. The reduction in maximum depth variation was more than 85%. The proposed model was proved to be feasible and effective for improving machining accuracy significantly.

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The influence of ultrasonic vibration on parts properties during incremental sheet forming

Yan-Le Li, Zi-Jian Wang, Wei-Dong Zhai, Zi-Nan Cheng, Fang-Yi Li, Xiao-Qiang Li

2021, 9(2): 250-261. doi:10.1007/s40436-021-00347-0

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The integration of ultrasonic vibration into sheet forming process can significantly reduce the forming force and bring benefits including the enhancement of surface quality, the enhancement of formability and the reduction of spring-back. However, the influencing mechanisms of the high-frequency vibration on parts properties during the incremental sheet forming (ISF) process are not well known, preventing a more efficient forming system. This paper comprehensively investigates the effects of different process parameters (vibration amplitude, step-down size, rotation speed and forming angle) on the micro-hardness, minimum thickness, forming limit and residual stress of the formed parts. First, a series of truncated pyramids were formed with an experimental platform designed for the ultrasonic-assisted incremental sheet forming. Then, micro-hardness tests, minimum thickness measurements and residual stress tests were performed for the formed parts. The results showed that the surface micro-hardness of the formed part was reduced since the vibration stress induced by the ultrasonic vibration within the material which eliminated the original internal stress. The superimposed ultrasonic vibration can effectively uniform the residual stress and thickness distribution, and improve the forming limit in the case of the small deformation rate. In addition, through the tensile fracture analysis of the formed part, it is shown that the elongation of material is improved and the elastic modulus and hardening index are decreased. The findings of the present work lay the foundation for a better integration of the ultrasonic vibration system into the incremental sheet forming process.

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Towards understanding the microstructure and temperature rule in large strain extrusion machining

Yun-Yun Pi, Wen-Jun Deng, Jia-Yang Zhang, Xiao-Long Yin, Wei Xia

2021, 9(2): 262-272. doi:10.1007/s40436-020-00343-w

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Large strain extrusion machining (LSEM) is a typical process for preparing ultrafine or nanocrystalline strips. It is based on large plastic deformation. The processing parameters of LSEM in this study were optimized by experiments and simulations. Using the orthogonal array, signal-to-noise ratio, and analysis of variance, the influence and contribution of processing parameters on response variables were analyzed. Because of the difference in processing parameters between optimizing the average grain size and the maximum temperature, the response variables analyzed must be correctly selected. Furthermore, the optimal processing parameters for obtaining the minimum average grain size and the lowest maximum temperature are analyzed. The results show that the tool rake angle is the most important factor. However, the level of this factor required to achieve the minimum average grain size is different from that required to obtain the lowest maximum temperature. The validity of the method is verified through experiments and simulations.

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A two-stage restoration of distorted underwater images using compressive sensing and image registration

Zhen Zhang, Yu-Gui Tang, Kuo Yang

2021, 9(2): 273-285. doi:10.1007/s40436-020-00340-z

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Imaging through a time-varying water surface exhibits severe non-rigid geometric distortions and motion blur. Theoretically, although the water surface possesses smoothness and temporal periodicity, random fluctuations are inevitable in an actual video sequence. Meanwhile, considering the distribution of information, the image structure contributes more to the restoration. In this paper, a new two-stage restoration method for distorted underwater video sequences is presented. During the first stage, salient feature points, which are selected through multiple methods, are tracked across the frames, and the motion fields at all pixels are estimated using a compressive sensing solver to remove the periodic distortions. During the second stage, the combination of a guided filter algorithm and an image registration method is applied to remove the structural-information-oriented residual distortions. Finally, the experiment results show that the method outperforms other state-of-the-art approaches in terms of the recovery effect and time.

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Experimental and numerical investigation of the abrasive waterjet machining of aluminum-7075-T6 for aerospace applications

Joseck Nyaboro, Mahmoud Ahmed, Hassan El-Hofy, Mohamed El-Hofy

2021, 9(2): 286-303. doi:10.1007/s40436-020-00338-7

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The machining of hard-to-cut materials with a high degree of precision and high surface quality is one of the most critical considerations when fabricating various state-of-the-art engineered components. In this investigation, a comprehensive three-dimensional model was developed and numerically simulated to predict kerf profiles and material removal rates while drilling the aluminum-7075-T6 aerospace alloy. Kerf profile and material removal prediction involved three stages:jet dynamic flow modeling, abrasive particle tracking, and erosion rate prediction. Experimental investigations were conducted to validate the developed model. The results indicate that the jet dynamic characteristics and flow of abrasive particles alter the kerf profiles, where the top kerf diameter increases with increasing jet pressure and standoff distance. The kerf depth and hole aspect ratio increase with jet pressure, but decrease with standoff distance and machining time. Cross-sectional profiles were characterized by progressive edge rounding and parabolic shapes. Defects can be minimized by utilizing high jet pressure and small standoff distance. The material removal rate increases with increasing jet pressure, abrasive particle size, and exposure time, but decreases with increasing standoff distance.

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Analysis and optimization of sustainable machining of AISI O1 tool steel by the wire-EDM process

Carmita Camposeco-Negrete

2021, 9(2): 304-317. doi:10.1007/s40436-021-00353-2

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Wire electrical discharge machining (wire-EDM) is an energy-intensive process, and its success relies on a correct selection of cutting parameters. It is vital to optimize energy consumption, along with productivity and quality. This experimental study optimized three parameters in wire-EDM:pulse-on time, servo voltage, and voltage concerning machining time, electric power, total energy consumption, surface roughness, and material removal rate. Two different plate thicknesses (15.88 mm and 25.4 mm) were machined. An orthogonal array, signalto-noise ratio, and means graphs, and an analysis of variance (ANOVA), determine the effects and contribution of cutting parameters on responses. Pulse-on time is the most significant factor for almost all variables, with a percentage of contribution higher than 50%. Multi-objective optimization is conducted to accomplish a concurrent decrease in all variables. A case study is proposed to compute carbon dioxide (CO₂) tons and electricity cost in wire-EDM, using cutting parameters from multi-objective optimization and starting values commonly employed to cut that tool steel. A sustainable manufacturing approach reduced 5.91% of the electricity cost and CO₂ tons when machining the thin plate, and these responses were diminished by 14.09% for the thicker plate. Therefore, it is possible to enhance the sustainability of the process without decreasing its productivity and quality.

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