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The purpose of this study is to realize the multi-objective optimization for MRR and surface roughness in micro-milling of Inconel 718.

Taguchi method has been applied to conduct experiments, and the cutting parameters are spindle speed, feed per tooth and depth of cut. The first-order models used to predict surface roughness and MRR for micro-milling of Inconel 718 have been developed by regression analysis. Genetic algorithm has been utilized to implement multi-objective optimization between surface roughness and MRR for micro-milling of Inconel 718.

This paper implemented the multi-objective optimization between surface roughness and MRR for micro-milling of Inconel 718. And some conclusions can be summarized. Depth of cut is the major cutting parameter influencing surface roughness. Feed per tooth is the major cutting parameter influencing MRR. A number of cutting parameters have been obtained along with the set of pareto optimal solu-tions of MRR and surface roughness in micro-milling of Inconel 718.

There are a lot of cutting parameters affecting surface roughness and MRR in micro-milling, such as tool diameter, depth of cut, feed per tooth, spindle speed and workpiece material, etc. However, to the best our knowledge, there are no published literatures about the multi-objective optimization of surface roughness and MRR in micro-milling of Inconel 718.

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.

The purpose of this paper is to analyze the measurement error of a three-dimensional coordinate measurement system based on dual-position-sensitive detector (PSD) under different background light.

The mind evolutionary algorithm (MEA)-back propagation (BP) neural network is used to predict the three-dimensional coordinates of the points, and the influence of the background light on the measurement accuracy of the three-dimensional coordinates based on PSD is obtained.

The influence of the background light on the measurement accuracy of the system is quantitatively calculated. The background light has a significant influence on the prediction accuracy of the three-dimensional coordinate measurement system. The optical method, electrical method and photoelectric compensation method are proposed to improve the measurement accuracy.

BP neural network based on MEA is applied to the coordinate prediction of the three-dimensional coordinate measurement system based on dual-PSD, and the influence of background light on the measurement accuracy is quantitatively analyzed.

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.

The purpose of this paper is to solve the problem that the analytic solution model of spatial three-dimensional coordinate measuring system based on dual-position sensitive detector (PSD) is complex and its precision is not high.

A new three-dimensional coordinate measurement algorithm by optimizing back propagation (BP) neural network based on genetic algorithm (GA) is proposed. The mapping relation between three-dimensional coordinates of space points in the world coordinate system and light spot coordinates formed on dual-PSD has been built and applied to the prediction of three-dimensional coordinates of space points.

The average measurement error of three-dimensional coordinates of space points at three-dimensional coordinate measuring system based on dual-PSD based on GA-BP neural network is relatively small. This method does not require considering the lens distortion and the non-linearity of PSD. It has simple structure and high precision and is suitable for three-dimensional coordinate measurement of space points.

A new three-dimensional coordinate measurement algorithm by optimizing BP neural network based on GA is proposed to predict three-dimensional coordinates of space points formed on three-dimensional coordinate measuring system based on dual-PSD.

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 purpose of this paper is to establish a roughness prediction model of micro-milling Inconel718 with high precision.

A prediction model of micro-milling surface roughness of Inconel718 is established by SVM (support vector machine) in this paper. Three cutting parameters are involved in the model (spindle speed, cutting depth and feed speed). Experiments are carried out to verify the accuracy of the model.

The results show that the built SVM prediction model has high prediction accuracy and can predict the surface roughness value and variation law of micro-milling Inconel718.

Inconel718 with high strength and high hardness under high temperature is the suitable material for manufacturing micro parts which need a high strength at high temperature. Surface roughness is an important performance indication for micro-milling processing. Establishing a roughness prediction model with high precision is helpful to select the cutting parameters for micro-milling Inconel718.

The built SVM prediction model of micro-milling surface roughness of Inconel718 is verified by experiment for the first time. The test results show that the surface roughness prediction model can be used to predict the surface roughness during micro-milling Inconel718, and to provide a reference for selection of cutting parameters of micro-milling Inconel718.

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.

We examine whether firms’ multinationality leads to better performance and what the role of R&D investment is in the multinationality performance linkage. Unlike the previous studies, we employ both accounting‐ and market‐based measures of firm performance for a large sample of U.S. manufacturing firms. Our results show that the empirical relation between multinationality and performance is not monotonic but varies with the phase of a firm’s multinationality, starting with a negative relation initially, followed by a positive one, and then again a negative one. This horizontal S‐shaped curvilinear relation of multinationality is more pronounced for the market‐based performance measure and is supportive of the three‐stage theory of internationalization. We also find that a firm’s multinationality is related to greater firm performance when the firm possesses R&D investment, and that the effect of R&D increases with the extent of a firm’s multinationality. These results lend strong support for the Internalization theory and the resource‐based view of firms’ international expansion. Our results are robust to different model specifications with an alternative measure of multinationality.