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This paper presents an experimental study on the seismic behavior of PVC-CFRP confined reinforced concrete columns subjected to combined axial compression load and horizontal reversed cyclic loading. A total of six one-fourth scale PVC-CFRP confined reinforced concrete columns (PVC-CFRP) and two one-fourth scale PVC confined reinforced concrete columns (PVC) are fabricated. The flexural failure mode, which is characterized by the crushing of concrete and the yielding of longitudinal reinforcement, is found for all PVC-CFRP and PVC specimens. The effects of axial compression ratio and CFRP strip spacing on the secant stiffness, energy dissipation, etc. are investigated. The results show the secant stiffness decreases rapidly as the axial compression ratio increases, and the confining effect of the CFRP strip spacing on the secant stiffness evolution is more obvious for the specimen with higher axial compression ratio. The specimen with smaller CFRP strip spacing exhibits better energy dissipation capacity, and the strengthening effect of CFRP strips is more significant for the specimen with higher axial compression ratio. The effective CFRP strip spacing for the PVC-CFRP is suggested.

Temperature in the cutting zone during dry machining has a significant effect on the tool life and surface integrity of the workpiece. This paper describes a comprehensive research on the cutting temperature in dry machining of ball screw under whirling milling by using infrared imaging. The effects of tool parameter and geometric parameter of workpiece together with the cutting parameters on the maximum and average temperatures in the cutting zone were analyzed in full detail. The influencing degree of these parameters on the maximum and average temperatures was affected by the value ranges of the parameters. In addition, the regression model and back propagation (BP) neural network model were proposed for predicting the maximum and average temperatures in the cutting zone. The verification of the predictive models showed that compared to the regression model, BP neural network model could predict the cutting temperature with high precision. The R 2 of BP neural network model for predicting the maximum and average cutting temperatures in the cutting zone was higher than 99.8%, and the mean relative error and root mean square error were less than 4% and 19%, respectively.

Non-destructive detection of human foodborne pathogens is critical to ensuring food safety and public health. Here, we report a new method using a paper chromogenic array coupled with a machine learning neural network (PCA-NN) to detect viable pathogens in the presence of background microflora and spoilage microbe in seafood via volatile organic compounds sensing. Morganella morganii and Shewanella putrefaciens were used as the model pathogen and spoilage bacteria. The study evaluated microbial detection in monoculture and cocktail multiplex detection. The accuracy of PCA-NN detection was first assessed on standard media and later validated on cod and salmon as real seafood models with pathogenic and spoilage bacteria, as well as background microflora. In this study PCA-NN method successfully identified pathogenic microorganisms from microflora with or without the prevalent spoilage microbe, Shewanella putrefaciens in seafood, with accuracies ranging from 90% to 99%. This approach has the potential to advance smart packaging by achieving nondestructive pathogen surveillance on food without enrichment, incubation, or other sample preparation.

It's important to accurately and effectively estimate the overall machining energy consumption so as to compare and select the toolpath that uses the least amount of energy. To this end, a framework to model the energy consumption when machining a workpiece is proposed in this paper. This framework consists of a toolpath planning stage and an energy-modeling stage. The proposed framework is demonstrated using a sample workpiece. The results show that the framework is effective in estimating the machining energy consumption and identifying the optimal toolpath. The framework represents an important method for identifying robust decisions for energy efficiency improvement. © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/3.0/ ). Peer-review under responsibility of the scientific committee of the 27th CIRP Life Cycle Engineering (LCE) Conference.

The identification for crucial geometric errors of gear form grinding machine tools is essential for the later error accurate compensation, but complicated and difficult. To reveal the mapping rules between geometric errors and machining errors, the tool posture error model (TPEM) was first constructed but it failed to consider the effects of grinding process. Hence, the tooth surface posture error model (TSPEM) was established based on the discretization of the grinding trajectory and the calculation of the grinding contact lines. Then the improved Sobol method was performed to analyze the error sensitivity and identify the crucial errors and sensitive components by considering the stochastic and intercoupling characteristics of geometric errors. To validate the effectiveness of the proposed method, the identified results were compared with that of the Morris method and the modification experiment of crucial errors was conducted. It shows that the tooth surface errors in the δy-direction are reduced by 68.75% for the TSPEM and 43.36% for the TPEM and that the TSPEM is more effective for the identification of crucial geometric errors.

High temperature often occurs at the local cutting area during lead-screw whirling milling, which significantly affects workpiece surface integrity as well as tool life. The cutting process of lead-screw whirling milling comprises several recurrent characteristics, such as the un-deformed chip thickness and un-deformed chip width. The combined effects of un-deformed chip thickness and width cause time-varying variation in the size of the heat source, thus further affect the temperature distribution in the cutting area. This paper proposes a transient thermal analytical model to analyze the temperature distribution in the cutting area for lead-screw whirling milling. According to the complex trajectory of the tool relative to the rotation workpiece, the un-deformed chip geometry has been studied in detail at the two cutting stages. The time-varying tool-chip contact area and boundary equations of frictional heat source are firstly formulated to describe the size of the time-varying heat source. Secondly, the transient thermal analytical model is constructed taking into account the heat liberation intensity of the time-varying heat source. A series of experiments of whirling milling for lead-screw shaft are carried out, and the theoretical results evaluated by the proposed analytical model agree well with the experimental results. Furthermore, the effects of cutting conditions and un-deformed chip geometry on the temperature distribution have been analyzed, which can be used for process designers to make decisions in choosing the cutting parameters with optimized surface integrity and tool life.

Summary Elevation of the levels of reactive oxygen species and other toxic radicals is an emerging strategy to treat certain cancers by modulating the redox status of cancer cells. The biocatalytic upregulation of singlet oxygen by neutrophilic leukocytes should utilize robust enzymes and design carriers with protective microenvironment. Here, we utilize GOx-CPO as integrated tandem enzymes to in situ generate singlet oxygen, which could be not only for oxidative cross-linking of injectable hydrogel carriers but also for continuous tumor treatment by adjustable bioconversion of blood oxygen, glucose, and chloride ion. The tandem enzymes self-restrained within peptide hydrogel exhibited superactivity for upregulating singlet oxygen relative to free enzymes, which also avoids the diffusion of enzymes from tumor. This work will not only deepen the study of enzymes in biocatalysis but also offer an enzyme therapeutic modality for treating cancers., Graphical Abstract, Highlights • Injectable peptide-based hydrogel was formed by tandem enzymatic 1O2 cross-linking • Enzymes restrained in gel matrix exhibited superactivity for upregulating singlet oxygen • The hydrogel restricted enzymes within tumor rather than diffusing into normal tissues • The continuous upregulated 1O2 gave the hydrogel significant antitumor efficacy, Biological Sciences; Medical Biochemistry; Cancer

A geometrical-mechanical-thermal interaction model was proposed to predict the thermal contact resistance (TCR) between the bearing inner ring and shaft journal. By introducing the randomness of asperity distribution, the rough surface of bearing rings was characterized by the 3D Weierstrass-Mandelbrot (W-M) function and the fractal parameters were identified by the structure function method. A multiscale contact mechanics model was then proposed to compute such contact parameters as the actual contact area and contact pressure. Then the predictive model of TCRs was proposed based on the characterization method of micromorphology and the multiscale contact mechanics model to predict the TCR between the bearing inner ring and shaft journal. The results obtained by the proposed model were compared with that without introducing the surface contact coefficient and a series of experimental data. The effects of such parameters as the interference fit, the surface roughness, and the asperity top curvature radius on the TCR were investigated. The results showed good agreement, demonstrating the validity of the model. The TCR decreased with the interference fit because the actual contact area was increased. Moreover, the TCR increased with the surface roughness because the actual contact area was reduced. The results obtained by assuming that asperities had different top curvature radii was greater than that assuming that asperities had mean top curvature radii.

To improve the simulation accuracy of the traditional transient thermal characteristics analysis model (TCAM) of the ball screw feed drive system (BSFDS), the optimization method of the thermal boundary conditions (TBCs), including the thermal loads, the convective heat transfer coefficient and the thermal contact resistance (TCR), was proposed. A multi-objective and multi-parameter optimization model was established based on the hybrid response surface (HRS) and a multi-objective genetic algorithm (MOGA) was developed to optimize the above TBCs. To simulate the thermal characteristics of the BSFDS under different working conditions, the generality of the method was extended to predict the TBCs under different feed rates. To validate the effectiveness of the method, the thermal characteristic experiments of BSFDS were conducted. The results showed that the simulation error for the temperature field was reduced from 25% to 10% and that the simulation error for the thermal elongation was reduced from 30% to 11%.

The contact stiffness of the spindle/tool holder is essential for the cutting stability of the spindle system because the elastic connection of the spindle/tool holder interface determines the dynamic characteristics of the spindle system. To predict the dynamic characteristics of the spindle system at the design stage, an analytical model of the contact stiffness of the spindle/tool holder interface was proposed based on the fractal theory and the multi-scale contact mechanics model to consider the combined effect of the cutting force and drawbar force on the contact stiffness. The fractal theory was used to characterize the micro-morphology of rough surfaces and the fractal parameters were identified by the power spectrum method. For the contact of two rough curved surfaces, the surface contact coefficient was introduced into the multi-scale contact mechanics model to correct the contact stiffness, the actual contact area and the contact stress. To verify the validity of the method, the experiments were conducted and the results showed that the modeling error of the contact stiffness was reduced from 25.13% to 4.72% by the proposed method. Finally, the effect of such factors as the cutting force, the mean roughness, the material properties, the preload and the temperature variation on the contact stiffness was discussed. The contact stiffness of spindle/tool holder interface increased with the cutting force and the drawbar force and that the contact stiffness decreased with the mean roughness. Moreover, the shear contact stiffness increased with the equivalent elastic modulus, the interference fit and the temperature. The conclusion was drawn that the model can be used to predict the contact stiffness of the spindle/tool holder interface at the design stage.

The neglection of thermal-structure interaction of high-speed spindle system may lead to the modeling error of the thermal characteristics. To improve the modeling accuracy, the thermal-structure interaction mechanism was analyzed and the closed-loop iterative modeling method of thermal characteristics was proposed to modify the heat sources and thermal boundary conditions of the spindle system in each substep. The heat generation of bearings and built-in motor, the convective coefficients and the thermal contact resistances (TCRs) of bearing joints were computed. The heat generation of bearings was calculated by the SKF bearing thermal model and the results were compared with that obtained by integral method. The heat generation of bearings was modified by considering the combined effect of the lubricant viscosity variation and thermally-induced preload of bearings. Moreover, a novel geometrical-mechanical-thermal model for TCRs was proposed by characterizing the rough surface morphology and by establishing the multi-scale contact mechanics of joint surfaces. To validate the effectiveness of the proposed method, the thermal characteristic experiments of the spindle system were conducted under different rotational speeds. The results showed that the modeling errors in the temperature field and thermal deformation are reduced significantly and that this model has the ability of thermal error compensation.

The ellipticity involved in the Deshpande–Fleck foam model describing the constitutive behaviour of cellular materials was usually considered to be constant, but some very different values were suggested in the literature. A cell-based finite element model of closed-cell foam under uni-/multi-axial compression is employed to verify the Deshpande–Fleck foam model. The ellipticity is determined by applying uniaxial and hydrostatic compression tests and it is found to vary with the equivalent plastic strain, i.e., the ellipticity decreases with the equivalent plastic strain and then increases sustainably before full densification. According to the understandings from the numerical results, a fitting relation between the ellipticity and the equivalent plastic strain is suggested. The ellipticities of an open-cell foam and a closed-cell foam studied experimentally in the literature are fitted well with this relation. A modification to the Deshpande–Fleck foam model with a variable ellipticity is thus proposed. The modified Deshpande–Fleck foam model brings much accurate predictions with using a rigid–plastic hardening (R-PH) idealisation model, which describes the stress–strain relation of cellular material under uniaxial compression. Good agreement is also observed between the experimentally measured stress–strain responses and the predictions of the modified Deshpande–Fleck foam model, especially when considering the effect of the plastic Poisson's ratio with a non-associated flow rule. The findings herein are helpful to improve the prediction accuracy of the Deshpande–Fleck foam model.

The gear profile grinder is important to ensure the geometric accuracy of tooth surfaces. However, its machining accuracy is significantly reduced by the geometric and thermal errors. So the geometric and thermal errors should be controlled simultaneously. The geometric error modeling and decoupling of the gear profile grinder are studied, and the mapping relationship between the geometric accuracy of tooth surfaces and machine errors is constructed. To establish a robust thermal error model, a physically-based model is established to prove the feasibility of using the bidirectional long short term memory network (Bi-LSTMN) to establish a self-learning error model. The high-frequency noises are filtered and singular values are reduced by the Savitzky–Golay (SG) filtering algorithm. An attention-gate-based Bi-LSTMN (AGBi-LSTMN) is proposed to reduce the time complexity. Then the mixed-variants whale optimization algorithm (MVWOA) is proposed to optimize the hyper-parameters, and SG-MVWOA-AGBi-LSTMN is proposed for the thermal error prediction. Finally, the simultaneous geometric and thermal error control is implemented. The results show that the total tooth profile precision is increased by ISO level 3, and the tooth profile tilt precision is increased by ISO level 5 with the simultaneous geometric and thermal error control.

Production lines are important for the high-accuracy and efficient machining of parts. The thermal error of key machining equipment in production lines has a significant effect on the geometric accuracy of machined parts. To improve the geometric accuracy of machined parts, the thermal error of key machining equipment in a production line should be controlled. Then the collection, storage, analysis, and calculation of the large-volume manufacturing data are essential. But the processing involving the large-volume manufacturing data is time-consuming and challenging, which leads to low executing efficiency. To solve the problem that the system is inefficient in the processing of the large-volume manufacturing data, a four-terminal-architecture cloud-edge-based digital twin system (CEDTS) is proposed with a reasonable functional division of four terminals, and thus the executing efficiency of CEDTS is expedited. Then the error mechanism is studied to prove the long-term memorizing behavior, and an improved seagull optimization algorithm (ISOA) is proposed based on the chaos thought to optimize the weights, thresholds, and the number of iterations of an improved long short term memory (ILSTM) network with the attention mechanism. The ISOA-ILSTM error model is embedded into the intelligent decision-making terminal of CEDTS to predict the thermal error. Moreover, a comprehensive machining error model is proposed and embedded into the intelligent decision-making terminal of CEDTS to control the thermal error. Finally, the effectiveness of CEDTS is verified on a production line. The results show that the reduction of the large-volume manufacturing data for the collection, storage, analysis, and calculation is significant. With the implementation of CEDTS, the fluctuation range of geometric errors of machined parts is reduced significantly. The executing time is reduced by more than half by CEDTS with the GPU acceleration.

The deformation characteristics and mechanical behavior of closed-cell polylactide (PLA) foams under quasi-static uniaxial compression are investigated. The PLA foam specimens are constructed numerically with 3D Voronoi technique and prepared with fused deposition modeling (FDM). The effects of meso‑structure, layer deposition strategy and relative density on the crushing and densification behaviors of PLA foams are analyzed. The experimental results indicate that PLA foams with regular meso‑structures are more likely to induce local collapse during compression, and those with random meso‑structures show good performances, such as high specific energy absorption, low stress fluctuation and stable deformation process. The experimental results further reveal that random PLA foams with the deposition layers perpendicular to the loading direction have higher strength and smaller lateral expansion than those with other orientations. The quasi-static compression stress-strain curves of random PLA foams exhibit three typical stages, namely linear elastic stage, plastic collapse stage and densification stage. A stress drop after reaching the initial peak stress is observed. To characterize these features, we propose a statistical constitutive model which takes into account the meso‑structural characteristics and deformation mechanism at the cell scale. This constitutive model contains six parameters and fits the experimental results very well. In particular, it can capture the peak stress at the onset of plastic collapse and the subsequent stress drop of cellular materials. Finally, the dependence of the six parameters in the constitutive model on the relative density is analyzed and the corresponding quantitative statistical relationships are determined.

Energy crisis, climate change, and stringent legislations are imposing great pressure on enterprises, especially manufacturing sectors, to improve their energy efficiency. To achieve higher energy efficiency in manufacturing, reliable energy consumption modelling is the prerequisite since it offers fundamental basis for any energy efficiency-related optimization. Although tool wear is inevitable, traditional energy consumption models fail to take tool wear effects into consideration. To address this issue, this study proposes an energy consumption model with tool wear progression for 3-axis milling process. Based on modern machining theory and recent achievements in energy consumption modelling, the proposed model is firstly derived as an expression with unknown coefficients. Subsequently, the involved coefficients are calibrated based on cutting experiments. With the explicit energy consumption model, power consumption with a given tool wear under new cutting conditions can be predicted with a high accuracy. In addition, as the model reveals a one-to-one correspondence between the power consumption and tool wear, the tool wear can also be effectively estimated from the measured power consumption. Compared with other tool wear monitoring methods such as acoustic emission and vibration, this power consumption-based tool wear estimation method is not only straightforward but also cost-effective. To the best of the authors' knowledge, the proposed energy consumption model with tool wear progression is the first model that was experimentally validated in terms of total power prediction and tool wear prediction, respectively. As such, the proposed model can be a significant supplement to existing energy consumption modelling in machining process, and may provide a more accurate and comprehensive platform for energy efficiency optimization.

The thermal error is a dominant factor that seriously hinders the high-accuracy machining of complex parts. The weak robustness and low predictive accuracy have always been barriers to the wide use of data-based models. To improve the robustness, the transfer learning-based error control method is proposed in this study. The error mechanism modeling is conducted to demonstrate the memory behavior of thermal errors, and the applicability of a long short-term memory network (LSTMN) for the error prediction is proven. Then an improved least mean square (ILMS) is proposed to filter the high-frequency noises and remove singular values. A pre-activated residual block is designed, and is embedded into the deep residual LSTMN (DRLSTMN). The differential spotted hyenas optimization algorithm (DSHOA) is proposed based on the chaos initialization strategy, differential mutation operator, and nonlinear control factor to optimize the hyper-parameters of DRLSTMN. Then the ILMS-DSHOA-DRLSTMN error prediction model is proposed for machine tool #1. The transfer learning model is established for machine tool #2 based on ILMS-DSHOA-DRLSTMN to enhance the robustness. The predictive abilities of the transfer learning models of ILMS-DSHOA-DRLSTMN, ILMS-DRLSTMN, ILMS-DSHOA-LSTMN, ILMS-back propagation network (ILMS-BP), ILMS-multiple linear regression analysis (ILMS-MLRA), ILMS-least squared support vector machine (ILMS-LSSVM), ILMS-CNNs-LSTM (ILMS-CL), and ILMS-deep calibration (ILMS-DC) are 98.37%, 97.95%, 97.60%, 94.51%, 95.41%, 96.02%, 96.43%, and 96.06%, respectively. Finally, the actual machining experiments were performed. When the thermal error is controlled with the transfer learning model, the fluctuation ranges for the geometric errors for D1 and D2 are [− 4 μ m , 4 μ m ] and [− 3 μ m , 3 μ m ], respectively.

Process parameters have a significant effect on surface integrity, which determines the service performance of the parts. To improve surface integrity, the process parameters are determined: 1) by experienced engineers directly, 2) based on the Pareto frontier automatically constructed by swarm intelligence algorithms. However, as the Pareto frontier contains many non-dominated solutions, the final parameters are still determined by experienced engineers, which reduces the intelligence level. Therefore, an intelligent process parameters determination method based on multi-algorithm fusion is proposed towards minimal surface residual stress in feed or transverse direction (Rsf, Rst) and surface roughness (Ra) in five-axis milling. Firstly, the Improved Generalized Regression Neural Network (IGRNN), which enhances the nonlinear mapping capability even in dealing with a small batch of experiments, is proposed to predict the Rsf, Rst, and Ra with certain inputs (including lead angle, tilt angle, cutting depth, feed speed, and spindle speed). Then based on the proposed model, the Improved Non-dominated Sorted Genetic Algorithm-II (INSGA-II), which improves the uniformity of the Pareto frontier, is used to obtain a series of non-dominated process parameters. Finally, the optimal parameters are determined by the Principal Component Analysis (PCA) without manual weight assignment for Rs and Ra. By comparing with the second-best one, although the Rsf decreases by 0.33%, which is still able to obtain negative residual stress, the Rst and Ra are greatly improved by 9.3% and 47.94%, respectively. The proposed method could improve the intelligent level of process parameters determination and the service performance of the parts. Furthermore, it lays a foundation for the realization of intelligent manufacturing.

Proper design of cellular structure-based composite could well improve the mechanical properties of the constituent members acting separately. In this study, the novel foamed concrete-filled honeycombs (FCFHs) with tailored geometrical architectures of additively manufactured polymeric honeycombs were developed. The in-plane crushing performance of FCFHs subjected to quasi-static and impact loadings was investigated experimentally and numerically. It was demonstrated that the existence of foamed concrete within honeycomb cells greatly strengthens the collapse resistance of cell walls, and the premature failure of foamed concretes in FCFH is effectively prevented. The shear-dominated failure mode of FCFHs with honeycombs of relatively large cell size or regular configuration or low relative density gradually transforms to a global compression deformation by decreasing cell size or increasing cell irregularity or relative density of honeycomb. Benefiting from the interaction-induced reinforcement between the involved components, the mechanical properties of FCFHs are highly dependent on the mesostructures of honeycomb skeleton. Specially, a decrease of cell size from 5 mm to 3.5 mm yields an increase in the mean crushing strength and the specific energy absorption of FCFHs up to 49.6% and 69.0%, respectively. Further, a numerical model, which was validated against experimental tests, was adopted to study the dynamic behavior of FCFHs. It transpires that the contribution of each component to the energy absorption, relating to the mesoscale deformation mechanism, largely depends on impact velocity. This study aims to provide a promising route to design and optimize lightweight composite structures with high performance.