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This paper aims to present a set of processes for obtaining the global spraying trajectory of a cold spraying robot on a complex surface.

The complex workpiece surfaces in the project are first divided by triangular meshing. Then, the geodesic curve method is applied for local path planning. Finally, the subsurface trajectory combination optimization problem is modeled as a GTSP problem and solved by the ant colony algorithm, where the evaluation scores and the uniform design method are used to determine the optimal parameter combination of the algorithm. A global optimized spraying trajectory is thus obtained.

The simulation results show that the proposed processes can achieve the shortest global spraying trajectory. Moreover, the cold spraying experiment on the IRB4600 six-joint robot verifies that the spraying trajectory obtained by the processes can ensure a uniform coating thickness.

The proposed processes address the issue of different parameter combinations, leading to different results when using the ant colony algorithm. The two methods for obtaining the optimal parameter combinations can solve this problem quickly and effectively, and guarantee that the processes obtain the optimal global spraying trajectory.

This paper aims to overcome the problem that the single structure of the driving term of the grey prediction model is not adapted to the complexity and diversity of the actual modeling objects, which leads to poor modeling results.

Firstly, the nonlinear law between the raw data and time point is fully mined by expanding the nonlinear term and the range of order. Secondly, through the synchronous optimization of model structure and parameter, the dynamic adjustment of the model with the change of the modeled object is realized. Finally, the objective optimization of nonlinear driving term and cumulative order of the model is realized by particle swarm optimization PSO algorithm.

The model can achieve strong compatibility with multiple existing models through parameter transformation. The synchronous optimization of model structure and parameter has a significant improvement over the single optimization method. The new model has a wide range of applications and strong modeling capabilities.

A novel grey prediction model with structure variability and optimizing parameter synchronization is proposed.

The highlights of the paper are as follows:

1. A new grey prediction model with a unified nonlinear structure is proposed.

2. The new model can be fully compatible with multiple traditional grey models.

3. The new model solves the defect of poor adaptability of the traditional grey models.

4. The parameters of the new model are optimized by PSO algorithm.

5. Cases verify that the new model outperforms other models significantly.

A new grey prediction model with a unified nonlinear structure is proposed.

The new model can be fully compatible with multiple traditional grey models.

The new model solves the defect of poor adaptability of the traditional grey models.

The parameters of the new model are optimized by PSO algorithm.

Cases verify that the new model outperforms other models significantly.

This paper aims to propose a new ultrasonic diagnosis method for stainless steel weld defects based on multi-domain feature fusion to solve two problems in the ultrasonic diagnosis of austenitic stainless steel weld defects. These are insufficient feature extraction and subjective dependence of diagnosis model parameters.

To express the richness of the one-dimensional (1D) signal information, the 1D ultrasonic testing signal was derived to the two-dimensional (2D) time-frequency domain. Multi-scale depthwise separable convolution was also designed to optimize the MobileNetV3 network to obtain deep convolution feature information under different receptive fields. At the same time, the time/frequent-domain feature extraction of the defect signals was carried out based on statistical analysis. The defect sensitive features were screened out through visual analysis, and the defect feature set was constructed by cascading fusion with deep convolution feature information. To improve the adaptability and generalization of the diagnostic model, the authors designed and carried out research on the hyperparameter self-optimization of the diagnostic model based on the sparrow search strategy and constructed the optimal hyperparameter combination of the model. Finally, the performance of the ultrasonic diagnosis of stainless steel weld defects was improved comprehensively through the multi-domain feature characterization model of the defect data and diagnosis optimization model.

The experimental results show that the diagnostic accuracy of the lightweight diagnosis model constructed in this paper can reach 96.55% for the five types of stainless steel weld defects, including cracks, porosity, inclusion, lack of fusion and incomplete penetration. These can meet the needs of practical engineering applications.

This method provides a theoretical basis and technical reference for developing and applying intelligent, efficient and accurate ultrasonic defect diagnosis technology.

Design of experiments is one of the many problem‐solving quality tools that can be used for various investigations such as finding the significant factors in a process, the effect of each factor on the outcome, the variance in the process, troubleshooting the machine problems, screening the parameters, and modeling the processes. Many industries use this tool to stay competitive worldwide by designing robust products as well as improving quality and reliability of a product. By using strategically designed and statistically performed experiments, it is possible to study the effect of several variables at one time, and to study inter‐relationships and interactions. Proposes a strategy to apply the design of experiments to study and optimize the performance of a process. Additionally, the formulation and solution to a multi‐objective optimization problem have been presented. As a case study, experimental design technique used by the authors to study the performance of a wire electrical discharge machining process for machining beryllium copper alloys is presented.

Gears are prone to instantaneous failure when operating under extreme conditions, affecting the machinery’s service life. With numerous types of gear meshing and complex operating conditions, this study focuses on the gear–rack mechanism. This study aims to analyze the effects and optimization of biomimetic texture parameters on the line contact tribological behavior of gear–rack mechanisms under starvation lubrication conditions.

Inspired by the microstructure of shark skin surface, a diamond-shaped biomimetic texture was designed to improve the tribological performance of gear–rack mechanism under starved lubrication conditions. The line contact meshing process of gear–rack mechanisms under lubrication-deficient conditions was simulated by using a block-on-ring test. Using the response surface method, this paper analyzed the effects of bionic texture parameters (width, depth and spacing) on the tribological performance (friction coefficient and wear amount) of tested samples under line contact and starved lubrication conditions.

The experimental results show an optimal proportional relationship between the texture parameters, which made the tribological performance of the tested samples the best. The texture parameters were optimized by using the main objective function method, and the preferred combination of parameters was a width of 69 µm, depth of 24 µm and spacing of 1,162 µm.

The research results have practical guiding significance for designing line contact motion pairs surface texture and provide a theoretical basis for optimizing line contact motion pairs tribological performance under extreme working conditions.

The transverse flux heating (TFH) concept offers very high electrical efficiency in combination with unique technological flexibility. Numerous advantages make this method beyond competition to be applied in e.g. processing lines. However, all potential advantages of TFH can be realized in practice only by optimal design of the inductor shape using numerical modelling and optimization techniques. This paper aims to describe a hierarchical approach to the optimal design of a one-sided induction coil, which will be used for one-sided TFH of continuous moving thin metal strip to achieve a homogeneous temperature distribution along the strip width.

Depending on the design step, 2D or 3D FEM simulations using ANSYS® Mechanical including the electromagnetics package are used. The harmonic electromagnetic solution is coupled to a transient thermal model which takes the strip movement into account. All models use the symmetries of the inductor workpiece arrangement to keep the calculation times as low as possible.

Due to the geometry of a TFH coil, the models can image a quarter or half of the arrangement. Preliminary investigations of different inductor head shapes can be carried out quickly and then further improved on more complex models in combination with the use of optimization algorithms.

Using hierarchical structure for designing a one-sided TFH coil, offers an efficient and quick way to create a coil which is adapted to the application.

The one-sided inductor design is considered, and the results are generally valid.

The capability to predict and evaluate various configurations’ performance during the conceptual design phase using multidisciplinary design analysis and optimization can significantly increase the preliminary design process’s efficiency and reduce design and development costs. This research paper aims to perform multidisciplinary design and optimization for an expendable microsatellite launch vehicle (MSLV) comprising three solid-propellant stages, capable of delivering micro-payloads in the low earth orbit. The methodology’s primary purpose is to increase the conceptual and preliminary design process’s efficiency by reducing both the design and development costs.

Multidiscipline feasible architecture is applied for the multidisciplinary design and optimization of an expendable MSLV at the conceptual level to accommodate interdisciplinary interactions during the optimization process. The multidisciplinary design and optimization framework developed and implemented in this research effort encompasses coupled analysis disciplines of vehicle geometry, mass calculations, aerodynamics, propulsion and trajectory. Nineteen design variables were selected to optimize expendable MSLV to launch a 100 kg satellite at an altitude of 600 km in the low earth orbit. Modern heuristic optimization methods such as genetic algorithm (GA), particle swarm optimization (PSO) and SA are applied and compared to obtain the optimal configurations. The initial population is created by passing the upper and lower bounds of design variables to the optimizer. The optimizer then searches for the best possible combination of design variables to obtain the objective function while satisfying the constraints.

All of the applied heuristic methods were able to optimize the design problem. Optimized design variables from these methods lie within the lower and upper bounds. This research successfully achieves the desired altitude and final injection velocity while satisfying all the constraints. In this research effort, multiple runs of heuristic algorithms reduce the fundamental stochastic error.

The use of multiple heuristics optimization methods such as GA, PSO and SA in the conceptual design phase owing to the exclusivity of their search approach provides a unique opportunity for exploration of the feasible design space and helps in obtaining alternative configurations capable of meeting the mission objectives, which is not possible when using any of the single optimization algorithm.

The optimized configurations can be further used as baseline configurations in the microsatellite launch missions’ conceptual and preliminary design phases.

Satellite launch vehicle design and optimization is a complex multidisciplinary problem, and it is dealt with effectively in the multidisciplinary design and optimization domain. It integrates several interlinked disciplines and gives the optimum result that satisfies these disciplines’ requirements. This research effort provides the multidisciplinary design and optimization-based simulation framework to predict and evaluate various expendable satellite launch vehicle configurations’ performance. This framework significantly increases the conceptual and preliminary design process’s efficiency by reducing design and development costs.

This study was conducted to analyze the effects of machining parameters on the specific energy consumption in the computerized numerical control lathe turning operation of a hardened alloy steel roll at low cutting speeds. The aim was to minimize its consumption.

The design matrix was based on three variable factors at three levels. Response surface methodology was used for the analysis of experimental results. Optimization was carried out by using the desirability function and genetic algorithm. A multiple regression model was used for relationship build-up.

According to desirability function, genetic algorithm and multiple regression analysis, optimal machining parameters were cutting speed 40 m/min, feed 0.2 mm/rev and depth of cut 0.50 mm, which resulted in minimal specific energy consumption of 0.78, 0.772 and 0.78 kJ/mm³, respectively. Correlation analysis and multiple regression model found a quadratic relationship between specific energy consumption with power consumption and material removal rate.

In the past, many researchers have developed mathematical models for specific energy consumption, but these models were developed at high cutting speed, and a majority of the models were based on the material removal rate as the independent variable. This research work developed a mathematical model based on the machining parameters as an independent variable at low cutting speeds, for a new type of large-sized hardened alloy steel roll. A multiple regression model was developed to build a quadratic relationship of specific energy consumption with power consumption and material removal rate. This work has a practical application in hot rolling industry.

Microtextured surfaces can reduce friction in tribological systems under certain contact conditions. Because it is very time-consuming to determine suitable texture patterns experimentally, numerical approaches to the design of microtextures are increasingly gaining acceptance. The purpose of this paper is to investigate to what extent the selected modeling approach affects optimized texturing.

Using the cam/tappet contact as an application-oriented example, a simplified 2D and a full 3D model are developed for determining the best possible texturing via a design study. The study explores elongated Gaussian-shaped texture elements for this purpose. The optima of the simplified 2D simulation model and the full 3D model are compared with each other to draw conclusions about the influence of the modeling strategy. The target value here is the solid body friction in contact.

For the elongated texture elements used, both the simplified 2D model and the full model result in very similar optimal texture patterns. In the selected application, the simplified simulation model can significantly reduce the computational effort without affecting the optimization result.

Depending on the selected use case, the simulation effort required for microtexture optimization can be significantly reduced by comparing different models first. Therefore, an exact physical replica of the real contact is not necessarily the primary goal when it comes to texture selection based on numerical simulations.

This study aims to investigate the influence of process parameters of wire electrical discharge machining (WEDM) of Nimonic75. Nimonic75 is a Nickel-based alloy mostly used in the aerospace industry for its strength at high temperature.

One factor at a time (OFAT) approach has been used to perform the experiments. Pulse on time, pulse off time, peak current and servo voltage were chosen as input process parameters. Cutting speed, material removal rate and surface roughness (Ra) were selected as output performance characteristics.

Through experimental work, the effect of process parameters on the response characteristics has been found. Results identified the most important parameters to maximize the cutting speed and material removal rate and minimize Ra.

Very limited research work has been done on WEDM of Nickel-based alloy Nimonic75. Therefore, the aim of this paper to conduct preliminary experimentation for identifying the parameters, which influence the response characteristics such as material removal rate, cutting speed, Ra, etc. during WEDM of Nickel-based alloy (Nimonic75) using OFAT approach and found the machinability of Nimonic75 for further exhaustive experimentation work.