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Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

Molecular dynamics is an emerging simulation technique in the field of machining in recent years. Many researchers have tried to simulate different processing methods of various materials with the theory of molecular dynamics (MD), and some preliminary conclusions have been obtained. However, the application of MD simulation is more limited compared with traditional finite element model (FEM) simulation technique due to the complex modeling approach and long computation time. Therefore, more studies on the MD simulations are required to provide a reliable theoretical basis for the nanoscale interpretation of grinding process. This study investigates the crystal structures, dislocations, force, temperature and subsurface damage (SSD) in the grinding of iron-nickel alloy using MD analysis.

In this study the simulation model is established on the basis of the workpiece and single cubic boron nitride (CBN) grit with embedded atom method and Morse potentials describing the forces and energies between different atoms. The effects of grinding parameters on the material microstructure are studied based on the simulation results.

When CBN grit goes through one of the grains, the arrangement of atoms within the grain will be disordered, but other grains will not be easily deformed due to the protection of the grain boundaries. Higher grinding speed and larger cutting depth can cause greater impact of grit on the atoms, and more body-centered cubic (BCC) structures will be destroyed. The dislocations will appear in grain boundaries due to the rearrangement of atoms in grinding. The increase of grinding speed results in the more transformation from BCC to amorphous structures.

This study is aimed to study the grinding of Fe-Ni alloy (maraging steel) with single grit through MD simulation method, and to reveal the microstructure evolution within the affected range of SSD layer in the workpiece. The simulation model of polycrystalline structure of Fe-Ni maraging steel and grinding process of single CBN grit is constructed based on the Voronoi algorithm. The atomic accumulation, transformation of crystal structures, evolution of dislocations as well as the generation of SSD are discussed according to the simulation results.

This paper seeks to investigate the roles of small and large projects in the development and evolution of a commercial company and why companies with a history of taking on large projects tend to eventually fail with large projects.

In terms of small and large projects, analytic models are established to: describe investors' behaviors; depict the dynamics between CEOs and their boards of directors; and reveal how profit ceilings exist for large projects.

After making the concepts of small and large projects precise, the paper establishes several analytic models for the investigation of the behaviors of various market participants. Then, it develops an explanation for why some decision makers like to take on large projects and why most new startups fail because of a lack of funds. A theory is given to show how investors value small projects more than large projects and why the current trend of moving manufacturing operations from industrialized nations to third world countries does not seem reversible in the foreseeable future, as long as international transportation costs stay low and the global economic system stays open and competitive. Among other results, it is also shown that: the higher the level the CEO's initial ability is, the more likely he would initiate and manage small projects, and the more labor effort he will devote to these projects; the CEO's additional effort spent on the small projects helps him gain non‐pecuniary benefits, which he can use to gain additional bargaining power over the board; to realistically maximize his private utility, the CEO would spend more of his time and effort on small projects; each large project has a glass ceiling for its maximum level of profits; companies taking on large projects cannot afford to devote much of their scarce resources to expand their market share and appearance; and to increase their profit potential, these companies have to control their spending so that their profit can be maximized by lowering their unit selling price p_s; for small projects, the profit potential for the company is unlimited.

This work is the first to employ models of human behaviors to research the interactions and dynamics between projects of different scales. It provides a theoretically reliable distinction between small and large projects.

The aim of this study is to first investigate the surface integrity of cylindrical rollers under grinding process and then design a reasonable superfinishing process that improve the anti-fatigue performance of cylindrical rollers by optimization of the surface integrity.

First, the white and dark layers produced by the grinding process is analyzed by microscope. Then, the influence of oilstone pressure on the stock removal, surface precision and crowned profile are explored. Finally, an optimal superfinishing process and a novel turnaround device are designed to improve surface integrity.

The experimental results show that as the oilstone pressure increases, the stock removal first increases and then remains stable. This hints that the stock removal of a single-time superfinishing process has an upper limit. In the current conditions, the maximum stock removal is 6 µm. Double-time superfinishing process and the turnover device can effectively eliminate the white and dark layers and improve the symmetric of roller profile. In addition, the surface precision is also improved.

The surface integrity of bearing rollers is very important to the application of industry field. The findings and the methods in the study can be helpful to improve the surface integrity of the bearing rollers.

The purpose of this paper is to propose a new hybrid algorithm, named improved plant growth simulation algorithm and genetic hybrid algorithm (PGSA-GA), for solving structural optimization problems.

PGSA-GA is based on PGSA and three improved strategies, namely, elitist strategy of morphactin concentration calculation, strategy of intelligent variable step size and strategy of initial growth point selection based on GA. After a detailed formulation and explanation of its implementation, PGSA-GA is verified using the examples of typical truss and single-layer lattice shell.

Improved PGSA-GA was implemented and optimization was carried out for two typical optimization problems; then, a comparison was made between the PGSA-GA and other methods. The results show that the method proposed in the paper has the advantages of high efficiency and rapid convergence, which enable it to be used for the optimization of various types of steel structures.

Through the examples of typical truss and single-layer lattice shell, it shows that the optimization efficiency and effect of PGSA-GA are better than those of other algorithms and methods, such as GA, secondary optimization method, etc. The results show that PGSA-GA is quite suitable for structural optimization.

This review paper reveals the literature on ultrasonic, chemical-assisted ultrasonic and rotary ultrasonic machining (USM) of glass material. The purpose of this review paper is to understand and describe the working principle, mechanism of material removal, experimental investigation, applications and influence of input parameters on machining characteristics. The literature reveals that the ultrasonic machines have been generally preferred for the glass and brittle work materials. Some other non-traditional machining processes may thermally damage the work surface. Through these USM, neither thermal effects nor residual stresses have been generated on the machined surface.

Various input parameters have the significant role in machine performance characteristics. For the optimization of output response, several input parameters have been critically investigated by the various researcher.

Some advance types of glasses such as polycarbonate bulletproof glass, acrylic heat-resistant glass and glass-clad polycarbonate bulletproof glass still need some further investigation because these materials have vast applications in automobile, aerospace and space industries.

Review paper will be beneficial for industrial application and the various young researcher. Paper reveals the detail literature review on traditional ultrasonic, chemical assisted ultrasonic and rotary USM of glass and glass composite materials.

The present study investigates the impact of managerial psychological characteristics on the supply chain management efficiency (SCME) of companies listed in Tehran Stock Exchange.

To this aim, information about 215 companies was analyzed during 2014–2021. The sales per inventory ratio was used to calculate SCME. In the present study, the focus is on characteristics such as managerial entrenchment, managerial myopia, managerial overconfidence (MOC) and managerial narcissism, all considered as managerial attributes.

The present findings showed that managerial myopia/managerial entrenchment (MOC/managerial narcissism) have a negative (positive) effect on SCME. Hypothesis testing based on robustness checks confirmed these results. Moreover, the findings are presented separately for companies with high business strategy (first quarter) and low business strategy (third quarter). The results show that at low levels of differentiation strategy, managerial entrenchment does not have a significant effect on SCME while other managerial attributes have a significant effect on both high and low business strategy.

The present study contributes to the identification of managerial psychological characteristics influencing SCME to advance future studies and support practical efforts. The present findings can prove the significance of this research and fill the existing gap in research.

The discontinuous Galerkin finite element method (DGFEM) is very suited for realizing high order resolution approximations on unstructured grids for calculating the hyperbolic conservation law. However, it requires a significant amount of computing resources. Therefore, this paper aims to investigate how to solve the Euler equations in parallel systems and improve the parallel performance.

Discontinuous Galerkin discretization is used for the compressible inviscid Euler equations. The multi-level domain decomposition strategy was used to deal with the computational grids and ensure the calculation load balancing. The total variation diminishing (TVD) Runge–Kutta (RK) scheme coupled with the multigrid strategy was employed to further improve parallel efficiency. Moreover, the Newton Block Gauss–Seidel (GS) method was adopted to accelerate convergence and improve the iteration efficiency.

Numerical experiments were implemented for the compressible inviscid flow problems around NACA0012 airfoil, over M6 wing and DLR-F6 configuration. The parallel acceleration is near to a linear convergence. The results indicate that the present parallel algorithm can reduce computational time significantly and allocate memory reasonably, which has high parallel efficiency and speedup, and it is well-suited to large-scale scientific computational problems on multiple instruction stream multiple data stream model.

The parallel DGFEM coupled with TVD RK and the Newton Block GS methods was presented for hyperbolic conservation law on unstructured meshes.

The purpose of this paper is to analyze the influence of different types of winding configurations on the distribution of leakage field and branch currents for a 330-kVA high-temperature superconducting (HTS) transformer winding.

A three-dimensional electromagnetic model coupled with a circuit model validated by an experiment is developed to calculate the leakage field and current uniformity under four different types of secondary winding configurations. The four types of gaps between secondary windings are uniform gap, arithmetic progression (AP) gap, six sections with three different gaps and eight sections with four different gaps. A coefficient named as uneven degree is used to define the current nonuniformity.

The simulation results show that the currents and leakage field of double pancakes (DPs) on both sides are larger than those of the other DPs, and the currents of several middle DPs are smaller than the average rated current with an ISOB gap and larger than the average rated current with an IBOS gap. For any one of the four types of winding configurations, the type with the ISOB gap can prohibit the current nonuniformity more effectively, whereas the IBOS gap can decrease the leakage field more. The AP with the ISOB gap is a wise choice for decreasing the uneven degree and leakage field.

There is an optimal winding configuration for decreasing the leakage field and uneven degree of branch currents. The results and numerical model are very useful for the design of a HTS transformer.

The leakage field distribution and branch currents nonuniformity for 25 parallel DPs are investigated and optimized.

The purpose of this paper is to study the adaptability of the tracked robot in complex working environment. It proposes an angle-changeable tracked robot with human–robot interaction in unstructured environment. The study aims to present the mechanical structure and human–robot interaction control system of the tracked robot and analyze the static stability of the robot working in three terrains, i.e. rugged terrain, sloped terrain and stairs.

The paper presents the mechanical structure and human–robot interaction control system of the tracked robot. To prevent the detachment of the tracks during obstacle navigation, a new type of passively adaptive device based on the relationship between the track’s variable angle and the forces is presented. Then three types of rough terrain are chosen to analyze the static stability of the tracked robot, i.e. rugged terrain, sloped terrain and stairs.

This paper provides the design method of the tracked robot. Owing to its appropriate dimensions, good mass distribution and limited velocity, the tracked robot remains stable on the complex terrains. The experimental results verify the effectiveness of the design method.

The theoretical analysis of this paper provides basic reference for the structural design of tracked robots.