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The purpose of this paper is to achieve the trajectory tracking measurement of a moving target based on double position sensitive detectors (PSDs).

In this paper, first, a double PSD-based measurement system including hardware system and software system is built up. Then, the working principle is studied to calculate parameters, and calibration experience is conducted. Finally, this double PSD-based measurement system is used to test angular displacement and axial displacement on the tool magazine and automatic tool changer.

In the experiment, the maximum position error of a space point based on double PSD measurement system is 0.8566 mm, and the average error is 0.4716 mm. These results show that the built double PSD-based measurement system of trajectory tracking of a moving target is reasonable.

Combining the characteristics of the PSD and principles of binocular visual measurement, a non-contact three-dimensional measuring system based on double PSDs is developed. The designed double-based measurement system is quite suitable for measurement of a fast-changing illuminant or in the case that the tracking accuracy is not tight.

This paper aims to predict tool wear and reveal the relationship between feed per tooth and tool wear in micro-milling Inconel 718 process.

To study and solve the tool wear problem in micro-milling of Inconel 718 micro components, in this paper, the investigation of micro-milling Inconel 718 process was implemented based on DEFORM finite element simulation, and tool wear depth of micro-milling cutter acted as output.

Different from the traditional macro milling process, diameter reduction percentage and average flank wear length decreased with the increase of feed per tooth; tool wear depth decreased when the feed per tooth was less than the minimum chip thickness.

At present, research on the prediction of tool wear in micro-milling of Inconel 718 has never been publicly reported. This study is significant to reveal the relationship between cutting parameters (feed per tooth) and tool wear in micro-milling Inconel 718.

This paper aims to improve calibration and force measurement accuracy of multi-sensors’ piezoelectric dynamometer used in thrust measurement of rocket/air vehicle engine.

This paper presents a mapping solution method of sensors’ outputs based on the Kirchhoff thin plate theory, builds force-deformation differential equations with specific boundary conditions, uses finite difference (FD) method to solve the equations and analyzes outputs in offset loading forces in four-sensor square layout in main direction. The resultant force deviations calculated by the Kirchhoff theory are optimized with sequence quadratic program (SQP) method, and a calibration method of multiple loading points (MLP) based on the Kirchhoff theory is presented. Experiments of static calibration and verification are complemented to contrast the novel and single loading point (SLP) calibration method.

Experiments of static calibration and its verification show that at a loading force of 5,000N, the average resultant force deviations with MLP is 17.87N (0.35% FS) compared with single loading point method 26.45N (0.53% FS), improving calibration and measurement precision.

A novel calibration method with MLP is presented. Force distributions of multiple sensors of main direction in piezoelectric dynamometer with offset loading force are solved with the Kirchhoff theory. The resultant force deviations calculated by Kirchhoff theory are optimized with the SQP method.

Cutting tool wear is known to affect tool life, surface quality, cutting forces and production time. Micro-milling of difficult-to-cut materials like Inconel 718 leads to significant flank wear on the cutting tool. To ensure the respect of final part specifications and to study cutting forces and tool catastrophic failure, flank wear (VB) has to be controlled. This paper aims to achieve flank wear prediction during micro-milling process, which fills the void of the commercial finite element software.

Based on tool geometry structure and DEFORM finite element simulation, flank wear of the micro tool during micro-milling process is obtained. Finally, experiments of micro-milling Inconel 718 validate the accuracy of the proposed method for predicting flank wear of the micro tool during micro-milling Inconel 718.

A new prediction method for flank wear of the micro tool during micro-milling Inconel 718 based on the assumption that the wear volume can be assumed as a cone-shaped body is proposed. Compared with the existing experiment techniques for predicting tool wear during micro-milling process, the proposed method is simple to operate and is cost-effective. The existing finite element investigations on micro tool wear prediction mainly focus on micro tool axial wear depth, which affects size accuracy of machined workpiece seriously.

The research can provide significant knowledge on the usage of finite element method in predicting tool wear condition during micro-milling process. In addition, the method presented in this paper can provide support for studying the effect of tool flank wear on cutting forces during micro-milling process.

The paper aims to study the wear and breakage characteristics of coated carbide cutting tools through micro-milling slot experiments on superalloy Inconel 718.

During the micro-milling process, the wear and breakage appearance on the rake face and flank face of the cutting tools, as well as the failure mechanism, have been studied. Furthermore, the wear and breakage characteristics of the micro-cutting tools have been compared with the traditional milling on Inconel 718.

The main failure forms of the micro tool when micro-milling Inconel 718 were tool tip breakage and coating shed on the rake and flank faces of the cutting tool and micro-crack blade. The main causes of tool wear were synthetic action of adhesive abrasion, diffusion wear and oxidation wear, while the causes of abrasive wear were not obvious.

The changing trend in tool wear during the micro-milling process and the main reasons of the tool wear are studied. The findings will facilitate slowing down the tool wear and prolonging the tool life during micro-milling Inconel718.

The results of this paper can help slow down the tool wear and realize high efficiency, high precision and economical processing of small workpiece or structure of the nickel-based superalloy.

The purpose of this paper is to summarize the advances in grey system theory research and various application achievements in science and engineering. At the same time, it commemorates the 40th anniversary of the birth of grey system theory and the 10th anniversary of Grey Systems–Theory and Application.

Firstly, the innovations of theoretical research in grey system theory were summarized and some of the widely recognized new results are briefly described. By searching and combing the research results of grey system theory in China national knowledge infrastructure (CNKI) database and Web of Science by Institute for Scientific Information (ISI), this paper shows the rapid development trend of grey system theory in the past 40 years, and the successful applications of grey system theory in the fields of social sciences, natural sciences and engineering technologies.

More than 227 thousands literature were found by input 10 phrases such as grey system, grey number and sequence operator etc. in CNKI database. After entering the new century, the number of grey system papers included in CNKI database is increasing rapidly. Since 2008, more than 10 thousands papers have been included per year and more than 15 thousands papers have been included per year since 2014. Grey system method and model are widely used in physics, chemistry, biology and other fields of natural science, as well as transportation, electric power, machinery and other fields of engineering technology, and a large number of valuable results have been achieved.

It can be seen that the grey system theory plays an important role in promoting China’s scientific and technological progress, innovation and development and high-level talent training from tens of thousands of literatures marked with important national science and technology projects and a large number of grey system literatures published by China’s double first-class universities and double first-class discipline construction universities.

Both innovations of theoretical research and practical application play important role in the growth of new theory. The innovations of theoretical research provide methods and tools for practical application, which is conducive to improve application efficiency and broaden application fields. A large number of practical applications needs have become the source of theoretical innovation and the solid background for the birth of theoretical innovation achievements.

Class imbalance learning, which exists in many domain problem datasets, is an important research topic in data mining and machine learning. One-class classification techniques, which aim to identify anomalies as the minority class from the normal data as the majority class, are one representative solution for class imbalanced datasets. Since one-class classifiers are trained using only normal data to create a decision boundary for later anomaly detection, the quality of the training set, i.e. the majority class, is one key factor that affects the performance of one-class classifiers.

In this paper, we focus on two data cleaning or preprocessing methods to address class imbalanced datasets. The first method examines whether performing instance selection to remove some noisy data from the majority class can improve the performance of one-class classifiers. The second method combines instance selection and missing value imputation, where the latter is used to handle incomplete datasets that contain missing values.

The experimental results are based on 44 class imbalanced datasets; three instance selection algorithms, including IB3, DROP3 and the GA, the CART decision tree for missing value imputation, and three one-class classifiers, which include OCSVM, IFOREST and LOF, show that if the instance selection algorithm is carefully chosen, performing this step could improve the quality of the training data, which makes one-class classifiers outperform the baselines without instance selection. Moreover, when class imbalanced datasets contain some missing values, combining missing value imputation and instance selection, regardless of which step is first performed, can maintain similar data quality as datasets without missing values.

The novelty of this paper is to investigate the effect of performing instance selection on the performance of one-class classifiers, which has never been done before. Moreover, this study is the first attempt to consider the scenario of missing values that exist in the training set for training one-class classifiers. In this case, performing missing value imputation and instance selection with different orders are compared.

This study investigates the effect of authentic leadership on service employees’ emotional labour strategies, surface acting and deep acting, from a human energy perspective.

A three-wave survey was conducted in a hotel chain in China, and 347 valid responses were obtained. Mplus software was used for structural equation modelling and bootstrapping analysis.

This study finds the following: authentic leadership predicts service employees’ emotional labour strategies; job insecurity mediates the influence of authentic leadership on surface acting but not on deep acting; relational energy mediates both surface and deep acting; and relational energy has more negative (positive) indirect effects than job insecurity.

The findings provide hospitality managers with insights into how to improve service employees’ capacity for emotional regulation. Hospitality managers should show more authenticity, pay attention to subordinates’ energy level and select and recruit candidates with positive energy traits. Hospitality organisations should encourage, select and train managers to behave as authentic leaders.

This study links authentic leadership with service employees’ emotional management in the hospitality industry. Moreover, it demonstrates the energising function of authentic leadership and introduces the new perspective of human energy to emotional labour research.