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In order to improve the measurement accuracy of three-dimensional coordinate measurement system based on dual-PSD, this paper proposes a three-dimensional coordinate measurement method based on back propagation (BP) neural network considering the high ability of the neural network to deal with the complex nonlinear mapping problem. This method can describe the mapping relationship between three-dimensional coordinates of space points in the world coordinate system and coordinates of light spots on dual-PSD well. Levenberg-Marquardt learning algorithm is used to train the network, and then trained BP neural network model is used to predict three-dimensional coordinates of space points. Experimental results show that the average measurement error of space points obtained by the method is low. It proves that the built BP neural network model can be used to predict three-dimensional coordinates of space points. [Submitted 9 July 2018; Accepted 30 October 2018]

The energy distribution of the incident light spot on position-sensitive detector (PSD) screen is uneven and the output coordinate value is the energy centre of the light spot. Measurement error caused by the deviation between the geometric and gravity centre of the light spot will occur. The deviation will increase with the increase of the diameter of the light spot. There is no quantitative research on the position error caused by the changes of the light spot diameter and the distance from the light spot to the PSD. To evaluate the magnitude of the error, the infrared spotlight is selected as the target light. The changing pattern that the spot diameter varies with the distance between the light source and lens is studied. Additionally, the effect of the spot size on the PSD measure error is studied. The research provides reference for error evaluation of the distance and position detection of PSD. [Submitted 5 July 2017; Accepted 24 December 2017]

Tool magazine and automatic tool changer (ATC) is one of the most important function units of a machining centre and its reliability directly influences the reliability of the machining centre. Failure analysis is the basic work in reliability study. So far, there are few studies on failure of the tool magazine and ATC. Based on the failure data obtained from the field tracing of a kind of circular tool magazine and ATC, the probability density function, the distribution function and failure function of time between failures (TBF) of the tool magazine and ATC are calculated. It is preliminarily assumed that the TBF of the circular tool magazine and ATC obey Weibull distribution. Parameter estimation and hypothesis testing of the Weibull distribution model are done. Finally, the effectiveness of the model is verified. The work can provide a reference for failure prediction of circular tool magazine and ATC.

In micro-milling of Inconel 718, work hardening occurs and the strain hardening properties as well as the relationship between strain and hardness of Inconel 718 remain unclear. The quasi-static tensile tests are conducted in this paper. Through the experiments, the strength coefficient K and the strain-hardening exponent n in Hollomon formula, which describes the relationship between strain and stress of a material, are calculated. After the measurements of the micro-hardness of Inconel 718 under different plastic deformations, the relationship between micro-hardness and strain of Inconel 718 is established. This relationship is valuable in computation of the hardness based on the stress state of Inconel 718 and it also extends the application of the commercial finite element analysis software such as ABAQUS, since it tracks stress state but cannot acquire the hardness of the machined surface. The proposed equation then calculates the hardness value based on the simulation output of stress in the software. [Submitted 18 July 2017; Accepted 28 November 2017]

In this paper, a typical crankshaft machining of piston compressor is taken as the research object. Based on system handing analysis (SHA), according to a series of problems existed in current actual production process, such as excessive materials handling time, work in process (WIP) overstock, unreasonable production layout, low utilisation rate of equipment, high labour cost, etc.., the process flow diagram method is adopted to research processing, handling, parking, waiting and checking processes in crankshaft production logistics. Reliable optimisation of the production process is achieved via collection and analyses of time data in process flow. Through the researches on process flow optimisation of crankshaft machining considering production logistics, the unreasonable processing technology of the crankshaft production and processing workshop is changed; through detailed analyses and optimisation, process flow time of crankshaft production and processing is saved, the production efficiency is improved and the cost is reduced. The work done in this paper can provide reference for other products in process flow optimisation of machining.

In recent years, micro-milling techniques have attracted great attention and interest from academia and industry. Inconel 718 is a nickel-based superalloy with good tensile, fatigue, creep and rupture strength and can find great application in nuclear and aerospace industry. In this paper, the response surface methodology (RSM) was applied to develop the model for predicting surface roughness in micro-milling Inconel 718. The magnitudes of cutting parameters affecting the surface roughness, which were depth of cut, spindle speed, and feed rate, were analysed by the analysis of variance (ANOVA). The validity of the surface roughness prediction model was proved due to the tiny error between the measured values and the prediction results. Then, genetic algorithm (GA) was used to determine the optimal cutting parameters achieving minimum surface roughness in micro-milling Inconel 718 process. All experiments show that the optimised results agree well with the test ones.

Inconel 718 is a kind of typical difficult-to-machine material. Micro-milling technology is an effective method for fabricating micro parts of Inconel 718. The change rules of micro-milling temperature differ from those of the traditional processing, which will affect the surface integrity of the workpiece and the tool life of the micro-milling cutter in different ways. To ascertain the effects of cutting parameters on cutting temperature during micro-milling Inconel 718 and achieve the prediction of cutting temperature, some micro-milling experiments are conducted based on the response surface method. The independent and interaction effects of the spindle speed, feed per tooth and axial cutting depth on cutting temperature are investigated. A micro-milling temperature prediction model is established based on the experiment results. The maximum prediction error is 5.3% and the average prediction error is 2.6%. Finally, the accuracy of the proposed model is validated through experiments of micro-milling Inconel 718.

In this paper, a surface roughness prediction model of micro-milling Inconel 718 by applying response surface methodology (RSM) is presented. The experiments based on centre composite rotatable design (CCRD) are designed to conduct the experiments. The cutting parameters considered are depth of cut, spindle speed and feed rate. Statistical methods, analysis of variance (ANOVA), are used to analyse the adequacy of the predictive model. The influence of each micro-milling parameter on surface roughness is analysed; also the magnitude order of parameters is determined. Depth of cut is found to be the critical influence factor. At last, the parameters interaction on surface roughness of micro-milling Inconel 718 is discussed by graphical means through MATLAB.

Due to its superior properties, nickel-based superalloy Inconel 718 can meet the requirements of micro parts with the high strength at high temperatures which have three-dimensional geometry structure like stepped surface, deep-hole, thin wall and so on. However, Inconel 718 is difficult to cut. Now, there are few researches on the cutting forces in micro-milling of Inconel 718, and the micro-milling mechanism of nickel-based superalloy is almost blank, while the prediction and control of micro-milling forces is important to reveal the micro-milling mechanism of nickel-based superalloy, to realise processing parameter optimisation, to reduce the tool wear, etc... To predict the cutting forces during micro-milling Inconel 718 process, coated carbide tools are used to micro-milling micro groove on Inconel 718, and the orthogonal type experiments are adopted. The influences of cutting parameters on cutting forces are studied. The micro-milling forces prediction model is built based on the experimental results, which can be used to predict the cutting forces during micro-milling of Inconel 718 nickel-based superalloy. To prove the validity of the built model, the significance test and fitting degree test are conducted.

During micro-milling Inconel 718, relationship between surface roughness and cutting parameters is studied. Taking the spindle speed, feed per tooth, axial depth of cut and cutting time into consideration, a prediction model, based on the orthogonal test, has been established to predict the surface roughness of nickel-base superalloy Inconel 718 by micro-milling. Neural network method is used to build surface roughness prediction model. As the cutting time changes, the surface roughness value of Inconel 718 under different cutting parameters changes, and the variation trend is able to provide reference for changing tools in time to ensure the surface quality of parts. The research on nickel-base superalloy micro milling, which could help us figure out the change regulation between micro groove surface roughness along with the cutting parameters and machining time, provides significant guidance for deep research on surface quality of micro-milling nickel-base superalloy Inconel 718 machining mechanism.

Nowadays, there are urgent demands of micro structure/parts which have high strength in high temperature environment in the fields such as aerospace, energy, power, bio-medical, etc. Nickel-based superalloy Inconel 718 is the suitable material for manufacturing the kind of micro parts. Micro-milling can be applied to manufacture Inconel 718 micro components. Different processing conditions lead to different residual stress on workpiece surface, which has a significant effect on reliability and working life of product. To study the residual stress problem in micro-milling of Inconel 718, the investigation of micro-milling Inconel 718 process was implemented based on ABAQUS simulation. The model based on the input of cutting parameters and the output of surface residual stress was developed. At last, the experiments of micro-milling Inconel 718 were conducted to verify the validity of model. The work offers reference for selecting cutting parameters in micro-milling of Inconel 718.

The paper mainly studied an open architecture control system for micro milling machine, including hardware construction and the software development of the control system. Taking programmable multi-axis controller (PMAC) as movement controller, together with industrial computer, high-speed spindle, precision feeding stage, etc., constitute the hardware of the control system for the micro milling machine. The software on the platform of windows was developed using Microsoft Visual C (VC). After the measurement of operation precision for the numerical control system, experiments of milling micro component have been carried out. The experimental results demonstrate the overall performance of the control system designed for the micro milling machine.

The main objective of this study is to investigate the feasibility of machining the copper layer for micro-strip antenna with micro-milling technology and to find the optimal machining parameters. The main properties of the micro-strip antenna are related to the geometry accuracy of the copper layer attached to the dielectric substrate surface. In this paper, micro-milling technology is utilised to machine the copper layer for microstrip antenna. The milling process is divided into two steps: roughing and finishing milling. The influence of the micro-milling cutting parameters on the geometry quality of copper is investigated. The results show that micro-milling of copper layer on polyimide is feasible, reasonable micro-milling cutting parameters can improve the processing efficiency, ameliorate the surface quality and extend the tools' life. The main contribution of this paper to the related literature is to provide a new method to machining the copper layer for micro-strip antenna.

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. Two prediction models are established by response surface method (RSM) and support vector machine regression (SVM) in this paper. Four cutting parameters are involved in the models (extended length of micro-milling tool, spindle speed, feed per tooth, and cutting depth in the axial direction). The models are established for material of brass. Experiments are carried out to verify the accuracy of the models. The results show that SVM prediction model has higher prediction accuracy, predict the variation law of micro-milling surface roughness better than RSM.