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Plastic forming is one of enabling and fundamental technologies in advanced manufacturing chains. Design optimization is a critical way to improve the performance of the forming system, exploit the advantages of high productivity, high product quality, low production cost and short time to market and develop precise, accurate, green, and intelligent (smart) plastic forming technology. However, plastic forming is quite complicated, relating to multi-physics field coupling, multi-factor influence, multi-defect constraint, and triple nonlinear, etc., and the design optimization for plastic forming involves multi-objective, multi-parameter, multi-constraint, nonlinear, high-dimensionality, non-continuity, time-varying, and uncertainty, etc. Therefore, how to achieve accurate and efficient design optimization of products, equipment, tools/dies, and processing as well as materials characterization has always been the research frontier and focus in the field of engineering and manufacturing. In recent years, with the rapid development of computing science, data science and internet of things (IoT), the theories and technologies of design optimization have attracted more and more attention, and developed rapidly in forming process. Accordingly, this paper first introduced the framework of design optimization for plastic forming. Then, focusing on the key problems of design optimization, such as numerical model and optimization algorithm, this paper summarized the research progress on the development and application of the theories and technologies about design optimization in forming process, including deterministic and uncertain optimization. Moreover, the applicability of various modeling methods and optimization algorithms was elaborated in solving the design optimization problems of plastic forming. Finally, considering the development trends of forming technology, this paper discusses some challenges of design optimization that may need to be solved and faced in forming process.

Road extraction is an important part of the intelligent vehicle systems for automatic driving, navigation, and traffic warning. For the complicated road scene, we present a road detection method based on illumination invariant image and quadratic estimation. The algorithm firstly extracts the illumination invariant image, and a priori triangular road region is used as the color sample to analyze the illumination invariant image and obtain the probability maps. Next, based on the histogram analysis, the combined probability map is significantly resettled, and the road region is estimated for the first time. Then gradient images of the illumination invariant image and the probability map are extracted, and the gradient image is analyzed by the estimated road region. Finally, the effective road boundary is extracted, and the more accurate road region is obtained. The experimental results show that our method can adapt to the road image in a variety of environments. Compared with other algorithms, our algorithm is more stable, and the computational efficiency is improved obviously.

Accurate weather prediction, particularly accurate temperature prediction, is critical in decision-making for energy consumption, health risks, and economics. Regional numerical weather prediction (NWP) models produce operational-level temperature forecasts based on local atmospheric circulation conditions. They suffer from data- and computational intensity, resulting in low availability of high-spatiotemporal resolution. We embed a data fusion technique into the proposed framework to address this shortcoming, integrating measurements from the Internet of Things (IoT) with a high spatiotemporal resolution with weather stations observations. The framework utilizes a Long Short-Term Memory (LSTM) network to predict surface temperature from the fusion dataset for four major cities in the U.S. (Los Angeles, New York City, Atlanta, and Chicago). The predictive framework achieves an average RMSE of 1.72 °C and an average R2 of 0.97 using the past 24 hours to predict the future 12 hours for Los Angeles (RMSE of 2.13 °C, 2.05 °C, 2.59 °C, and R2 of 0.98, 0.98, 0.97 for New York City, Atlanta, and Chicago). The transfer learning is adopted to leverage the pre-trained model from regions with a higher number of observation stations to predict regions with fewer stations. The transferable model improved the predicting MAE for regions with data scarcity up to 26%.

Surface roughening (SR) is one of the most common defects in deformation-based processing worsening surface quality and service performance of forming components, and in-depth understanding of SR is still a non-trivial and challenging issue for defect free processes since this phenomenon is interactively influenced by so many intrinsic and extrinsic factors. This paper aims to provide a panoramic discussion on the origination and evolution mechanisms of SR considering both intrinsic and extrinsic factors. A macro‑meso scale modeling framework, including Voronoi tessellation based geometrical model and crystal plasticity finite element (CPFE) based constitutive model, is established to explore non-uniform plastic deformation at grain-scale, and then the above framework is numerically implemented and applied into the 5052-O Al-alloy tube bending. The coupling effects of intrinsic factors (i.e., grain size, grain orientation and abnormal large grain) and extrinsic factors (i.e., deformation conditions, strain accumulation and strain rate) on SR are investigated. The results show that SR in plastic deformation stems from the accumulated intergranular non-uniform deformation through thickness direction, indicating that SR is an inherent characteristic and cannot be eradicated. SR is controlled by several factors: grain size, intergranular misorientation, the abnormal large grain, strain accumulation and deformation conditions. In a practical view, these are two effective ways to improve the surface quality by reducing grain size and removing abnormal large grains. From the extrinsic factor, it also makes sense to design the processing parameters reasonably and reduce friction in order to avoid local large deformation.

Joining by elastomeric swaging (ES) is a deformation-based way to manufacture tubular form-fit joints composed of aluminum alloy (AA) tubes and high-strength steel sleeve-fittings. To explore the relationship among the joined tubular material properties, joining behavior and performance of tubular form-fit joints fabricated by elastomeric swaging (ES), this paper took 6061-T4 and 5052-O AA tubes as material cases and carried out a comprehensive investigation by modeling and experiments. In terms of these two AA tubes, their microstructure was characterized by means of EBSD; their mechanical properties were characterized by tension tests based on digital image correlation and Visco-plasticity Self Consistent modeling. The characterization results demonstrate that the 6061-T4 tube has precipitation strengthening mechanism, texture unfavorable to shear, no Portevin–Le Chatelier (PLC) effect, larger strain hardening exponent and higher strength. In view of process, based on the material modeling for anisotropic AA tubes, hyperelastic polyurethane elastomer hose and 15-5PH sleeve, implicit axisymmetric numerical models of ES joining process with loading and unloading were established and validated. The simulation results show that there is a larger normal deformation in the 5052-O tube under the same extrusion depth and rate, and at the same bulging heights as 5052-O, the 6061-T4 tubular joints have larger residual contact shear stress at the undercuts. As for service performance, the ES joining tests and joining performance verification experiments were carried out. The experimental results show that the 6061-T4 tubular joining components have higher pressure resistance, larger pull-out force and better fatigue endurance. In conclusion, the joined tubes with texture unfavorable to shear, higher strength, larger strain hardening exponent and no PLC effect are beneficial to forming form-fit joints.

Deformation-based joining presents efficient, reliable and environmentally friendly advantages widely applying in the pipeline system. Internal roller swaging (IRS), one of deformation-based joining ways, is an inner expanding spinning process with characteristics of sheet-bulk forming, and it has the potential to fabricate high-strength tubular joining components with better high-pressure resistance and sealing performance. Taking a high-strength Ti-3Al-2.5V titanium alloy (TA) tube as the case material, this study combines explicit and implicit finite element (FE) modeling with experimental validating to explore the deformation mechanisms of the IRS process. By using the digital image correlation method (DICM) based uniaxial tension, the contractile strain ratio (CSR) of the TA tube was obtained to describe its anisotropy. By analyzing the mechanism of IRS, the motion equations of three rollers were deduced in the forming process, and a simplified 3D FE model for the IRS process was developed. Based on the FE simulation and analysis, the inhomogeneous flow deformation behaviors of TA tube in the IRS process were investigated by subdividing the deformation areas of the tube. Then, the material flow features of TA tube in the IRS process were explored by analyzing the axial displacements of the tube and observing its flow localization by means of optical microscope. Finally, the forming quality of the tubular joining components can be accurately designed and controlled by revealing the relationship between the swaging depth and the axial elongation and the relationship between the swaging depth and the contact shear stress. It is concluded that this incremental deformation-based joining process has obvious superiority to fabricate high-strength TA tubular joining components, and their reliability can be effectively enhanced by accurately controlling the swaging depth.

Pollution weather may cause serious damages to human life and property. Meteorological conditions like large-scale circulation can affect the formation and dissipation of pollution weather. We apply the hierarchical clustering method to classify circulation patterns based on the National Centers for Environmental Prediction (NCEP) Final Operational Global Analysis (FNL) sea level pressure (SLP) daily data and investigate their features during the formation and dissipation process of the defined 31 continuous pollution weather (CPW) cases in the Sichuan Basin using the meteorological data from 104 surface observation stations during 2007–2017. We find that the Sichuan Basin during the formation process is controlled by the large scale high-pressure circulation at sea level, and the SLP can be divided into high-pressure front type FC1 (32%) with the high pressure located in the west of the Basin, weak high-pressure type FC2 (35%) with the weak high pressure located in the Basin, and uniform pressure field type FC3 (33%) with the near homogeneous pressure distributed in the Basin. The Sichuan Basin during the dissipation process is dominated by the low-pressure circulation at sea level, and the SLP can be classified as low-pressure type DC1 (35%) with the low pressure centralize in the Basin, low-pressure front type DC2 (38%) with the low pressure center in the west of the Basin, and low-pressure bottom type DC3 (27%) with the low pressure in the north of the Basin. Besides, the wind, relative humidity, geopotential height, and temperature data are used to explore the formation and dissipation mechanisms of the CPW. The wind speed and temperature are lower during the formation phase of the CPW compared to those from the dissipation phase. Furthermore, we estimate the effects of the CPW on pollutants based on the air quality index and Particulate Matter (PM10 and PM2.5) concentration from environmental protection monitoring data during 2013–2017. The FC2 type shows the strongest enhancement of pollutants (36%), and the DC3 circumstance generates the most efficient scavenging mechanism to dispel pollutants (−57%).

In this study, high-quality thermochromic vanadium dioxide (VO2) thin films were grown on ZrO2/glass by pulsed laser deposition and were investigated using X-ray diffraction (XRD), atomic force microscopy (AFM), double beam spectrophotometry and Hall Effect measurement system. Based on XRD results, it can be inferred that the crystal structure of ZrO2 buffer layer can be transformed between monoclinic and tetragonal structures with the increase of buffer layer thickness. Photoelectric performance tests showed that the introduction of ZrO2 buffer layer considerably reduced the phase transition temperature (Tc) and hysteresis loop width (ΔH) of the VO2 films and allowed the visible light transmittance (Tlum) and solar modulation ability (ΔTsol) of the films to be tuned effectively. Especially, when the thickness of the ZrO2 buffer layer was 50 nm, the Tc of the VO2 films reached the minimum value of 48 °C. When the ZrO2 buffer layer thickness increased to 125 nm, the sheet resistance change of the VO2 film attained two orders of magnitude, and their Tlum-90°C and ΔTsol were as high as 50.01% and 7.88%, respectively. These results showed that the ZrO2 buffer layer considerably affected the growth of VO2 films on amorphous glass substrates and was beneficial for the application of VO2 in energy-saving smart windows.