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There have been high demands of high-quality, highly efficient processing methodologies on "difficult-to-cut" titanium alloys. The current methods for dealing with this kind of materials are mainly mechanical cutting ones. However, because of high processing costs, poor surface qualities, and restrictive machining operations, the costs of mechanical cutting methods are high. Electrical discharge machining (EDM), because of its flexibility, was considered as a supplement. However, serious difficulties arose while machining titanium alloys by EDM. Because of low thermo-conductivity of titanium alloys, the liquid temperature in gap between electrode and workpiece rose quickly after a series of pulse discharges. The high temperature of gap liquid usually led to gap liquid breakdown strength to decline. The consequence was discharging pulses tended out to be stable arc pulses or short pulses, burning workpiece surface and wearing electrode. The machining process became unstable. The low thermal conductivity of titanium alloys was the inherent property which could be hardly changed, and at present, the only way to settle the hard-to-cut problem of machining titanium alloys by EDM was to seek a way to keep gap liquid breakdown strength not go down so fast but still be suitable for effective pulse discharges. To solve this problem, this paper first listed three conditions to be met and analyzed the reasons to affect gap liquid breakdown strength in detail and concluded with three factors, gap distance, amount of chips left in gap, and gap liquid deionization after pulse discharges and then came up with a proposition to the problem. Technically, the proposition was accomplished by constructing a multiple-variable adaptive control system in which gap servo-voltage proportional to gap distance was in charge of discharging extent of pulses, electrode-discharging time decided the amount of chips produced in an electrode discharging cycle, and pulse-off time decided gap liquid deionization after discharges. These variables were timely regulated to keep the liquid breakdown strength suitable for discharging and meanwhile avoiding arcing in machining. The verification test demonstrated that the multivariable control system really helped electrical discharge machining titanium alloys in severe machining situations and proved its usefulness in applications. [ABSTRACT FROM AUTHOR]

Double-sided lapping process with fixed abrasive shows a strong capability of achieving high efficiency and high surface quality due to its controllable movement of the abrasive and determined material removal modes, and it can be as an alternative technology for fabricating ultra-precision optical components. However, the formation and evolution of the surface topography and roughness (Ra) were rarely clarified in the previous research due to the complicity of the double-sided lapping process. This paper established a new mathematical analytical model to provide a better scientific understanding of the formation and evolution of the surface topography and roughness, in which the spatial location and protrusion height of abrasive particles, each abrasive-material interaction, kinematics trajectory of abrasive particles, and force balance on the workpiece were considered. A series of lapping experiments on the Y3Al5O12 (YAG) wafer were conducted to verify the feasibility of the proposed model and analyze the influence of processing parameters on surface topography and roughness evolution. The results indicated that the proposed analytical model can be effectively used to predict surface topography and optimize the process parameters of double-sided planetary lapping for better surface roughness. The variation of processing time and rotational speed of the lapping plate (pad) possesses a significant influence on surface topography and Ra values. Better surface Ra values can be achieved by selecting the proper processing time and rotational speed of the lapping plate. [ABSTRACT FROM AUTHOR]

Because of the complexity of the measurement in a confined space the effect of workpiece rotational speed on surface quality, such as the uniformity of surface roughness (Ra) and peak-to-valley (PV) error of flatness, has not been revealed during the double-sided planetary lapping process. To resolve this concern, a method to measure the workpiece rotational speed is proposed, and the mathematical model for calculating the grit trajectory left on the workpiece in double-sided lapping considering the workpiece rotational speed is established. A variation coefficient for nonuniformity of the track point distribution (NUTPD) is introduced to evaluate the trajectory uniformity. Subsequently, the trajectory map of multiple grits and the value of NUTPD were simulated at different variations, which include the rotational speed ratio γ of the pad to the sun gear, and the rotational speed ratio η of the workpiece to the sun gear. The simulation result shows that the uniformity of the trajectory of the grits left on the workpiece surface is significantly affected by the rotational speed ratios γ and η. Furthermore, to verify the reliability of the simulation, corresponding processing tests were conducted by employing a double-sided planetary lapping machine with YAG wafers. The results show that the uniformity of the surface roughness and the peak-to-valley error of flatness can be reflected in the values of NUTPD. By selecting optimal rotational speed ratios η, better flatness and surface roughness of YAG can be achieved for the lower nonuniformity of trajectory distribution. The above analysis confirms that the method of measuring the rotational speed of the workpiece and trajectory analysis can significantly contribute to optimizing the rotational speed ratio γ. In addition, the above method can provide guidance for the optimization of rotational speed ratio η to obtain more uniform roughness and better flatness of the workpiece surface. [ABSTRACT FROM AUTHOR]

Milling force during machining process has strong time-varying and multi-frequency characteristic, which is easy to cause tool vibration, wear, and cutting chatter, and even lead the machining accuracy of parts to be out of tolerance. The paper proposed a milling force prediction method for a general helix cutter during peripheral milling. In this model, the geometric parameters of milling cutter are parametric defined, and then an analytical instantaneous uncut chip thickness (IUCT) model is presented with taking cutter runout (offset and inclination) and geometric entity shape of workpiece into account. A series of milling experiments using aluminum alloy were carried out with six kinds of milling cutters in a high-speed machining center. The analysis results show that the predicted value of milling force can better reflect the change trend of the actual measured one, and well prove the validity of the presented method. Furthermore, the influences of milling parameters, cutter runout, milling type, cutter tooth number, and unequal spacing angle on the time domain and frequency domain excitation characteristics of milling force are systematically analyzed, which can provide useful guidance for the tool design and machining parameter selection in the future. [ABSTRACT FROM AUTHOR]

Tool breakage is a prominent concern during the rough machining process, which is essential to be monitored online to prevent this phenomenon and maintain the surface quality. In this study, a tool breakage monitoring method is proposed based on the mechanism model and machining data using the acceleration signal. This method takes care of the whole process with the transition from a fresh tool to its full breakage. The mechanism of the tool breakage is obtained by this method, which is then implemented into a single synthesis parameter based on the frequency domain feature for the analysis of tool condition, and the data of fused features are statistically processed to carry out the breakage monitoring. The parameter 3 σ ¯ (three times the mean of standard deviation) is taken as the threshold value to determine the breakage condition in real time. The experimental results are in good agreement with the theoretical model, which proves the efficiency and simplicity of online tool breakage monitoring based on the proposed method. [ABSTRACT FROM AUTHOR]

Aiming at the problem of complex path planning in the processing of curved surface workpieces of body-in-white, a hybrid path planning method based on a memetic algorithm is proposed. The method is divided into two parts: welding sequence planning and welding path planning between weld points. By establishing the kinematic model of a spot welding robot based on the pipper criterion and z-y-z Euler angle solution method, the motion constraints of path optimization are analyzed. Under the framework of the memetic algorithm, the improved A-star algorithm with redundant node deletion and a post-smoothing process is used to obtain the smooth collision-free optimal path set between weld points and to construct the objective function of travelling all weld points with the shortest path length and highest smoothness. The multiobjective elitist-simulated annealing genetic algorithm (MESAGA) is used to achieve the welding sequence planning of all weld points. The variable neighborhood search method improves the mutation operator; the elitist strategy is introduced to improve the probability of elitist individual crossover and mutation operation, and a simulated annealing algorithm is used to jump out of local search to obtain the global optimal solution. According to the motion constraints, the joint space path is obtained by the optimal path in Cartesian space. Simulation analysis results demonstrate that the hybrid path planning method based on the memetic algorithm can effectively optimize the path of spot welding robots and lay the foundation for control and trajectory planning during welding processes. [ABSTRACT FROM AUTHOR]

As the core machining process element in the overall manufacturing process of a part, the machining process route plays an important role in improving final manufacturing quality. In the current CAPP system, the decision-making of the process route still depends on human-computer interaction and essentially depends on human intelligence. In the past decade, deep learning technology architecture has been gradually improved, which provides a new enabling technology for intelligent process planning. Recently, some researchers have applied deep learning to process route decision-making. However, due to the challenges of data representation and deep learning network construction, this promising solution is still at infancy. To address the two challenges, this paper presents a novel process route generation approach based on deep learning. First, we propose a fourth-order tensor model to represent the geometry and technological requirements of a part. And the relation matrix is constructed to represent the relationships among machining features. The process route is represented as a sequential set of one-hot vectors. Then, we construct an encoder-decoder neural architecture to automatically generate the machining process route for the part. The 3D convolution neuron network-based encoder converts the geometry, technological requirements, and the information of the relationships among machining features into a higher layer of vector representation, and the long short-term memory network-based decoder maps this representation to the process route. The whole neural architecture including the encoder and decoder is jointly trained to maximize the conditional probability of the target process route given the training part. Finally, the paper takes slot cavity parts as examples to verify the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence and intelligent manufacturing, the traditional teaching-playback mode and the off-line programming (OLP) mode cannot meet the flexible and fast modern manufacturing mode. Therefore, the intelligent welding robots have been widely developed and applied into the industrial production lines to improve the manufacturing efficiency. The sensing system of welding robots is one of the key technologies to realize the intelligent robot welding. Due to its unique characteristics of good robustness and high precision, the structured light sensor has been widely developed in the intelligent welding robots. To adapt to different measurement tasks of the welding robots, many researchers have designed different structured light sensors and integrated them into the intelligent welding robots. Therefore, the latest research and application work about the structured light sensors in the intelligent welding robots is analyzed and summarized, such as initial weld position identification, parameter extraction, seam tracking, monitoring of welding pool, and welding quality detection, to provide a comprehensive reference for researchers engaged in these related research work as much as possible. [ABSTRACT FROM AUTHOR]

In this paper, an initial point alignment method of narrow weld using laser vision sensor is presented on the basis of the relationship between the feature point of laser stripe and initial point. The whole initial point alignment process contains two stages. At the first stage, the initial point image is captured, and the image coordinates of the feature point of laser stripe and initial point are obtained. At the second stage, according to the relationship between the feature point of laser stripe and initial point, the three-dimensional (3D) coordinates of initial point could be determined to achieve initial point alignment. The initial point alignment method mainly includes vision sensing and motion control two parts. Firstly, a new laser vision sensor with a uniform LED surface light source is developed to capture the high signal-to-noise ratio (SNR) image including narrow weld, and the feature point of laser stripe and initial point are detected using the image processing method. Secondly, initial point alignment control system including feature verification and controller is designed to achieve initial point alignment control. Finally, a series of initial point alignment experiments of straight and curve narrow weld are conducted to test the performance of the proposed method. Experimental results indicate the alignment error is less than previous methods, which could be used in automatic welding process. [ABSTRACT FROM AUTHOR]

To improve the automation level of the welding robots, the automatic extraction and identification of the weld seam is the pre-requisite of the intelligent welding robots. During the real industrial environment, the welding environment often has the characteristics of weak texture, poor contrast, reflections from metallic surfaces, and imperfections on the work piece. These characteristics will seriously affect the accurate extraction of weld seam. To realize the automatic path planning of welding robots, a new method of automatic extraction and identification of narrow butt joint under weak contrast is realized to serve the 3D path teaching of welding robots. To achieve accurate extraction of narrow butt joint under weak contrast, this paper designs a new seam extraction operator to achieve accurate and fast extraction of weld seam with various shapes and sizes. And the shape feature of weld seam is used to construct feature vector. Finally, to get optimal classification performance, the adaptive network-based fuzzy inference system (ANFIS) is adopted in this paper to finish identification of narrow butt joints. The experimental results show that the proposed algorithm could quickly and accurately realize the extraction and identification of the narrow butt joints under weak contrast. [ABSTRACT FROM AUTHOR]

In the modern manufacturing industry, the welding quality is one of the key factors which affect the structural strength and the comprehensive quality of the products. It is an important part to establish the standard of welding quality detection and evaluation in the process of production management. At present, the detection technologies of welding quality are mainly performed based on the 2D image features. However, due to the influence of environmental factors and illumination conditions, the welding quality detection results based on grey images are not robust. In this paper, a novel welding detection system is established based on the 3D reconstruct technology for the arc welding robot. The shape from shading (SFS) algorithm is used to reconstruct the 3D shapes of the welding seam and the curvature information is extracted as the feature vector of the welds. Furthermore, the SVM classification method is adopted to perform the evaluation task of welding quality. The experimental results show that the system can quickly and efficiently fulfill the detection task of welding quality, especially with good robustness for environmental influence cases. Meanwhile, the method proposed in this paper can well solve the weakness issues of conventional welding quality detection technologies. [ABSTRACT FROM AUTHOR]

Net-shape forging is a high-efficiency and low-wastage process. However, it presents numerous disadvantages, such as low precision and surface quality of hot forging, as well as high load and harsh die quality of cold forging. Cold finishing is applied as a post-forging operation of forged gears. A hot-forged helical gear is processed by ironing finishing and compressing finishing that is introduced in this paper. A tool set is designed to compare compressing finishing with ironing finishing through experiments and finite element simulation. This study reveals that the load of ironing finishing is not related to interference. Moreover, the difference in tooth shape and surface roughness of both tooth surfaces of each gear is due to the helix angle. Excessive finishing leads to surface quality degradation. The load of compressing finishing increases linearly. Surface roughness evidently improves, and it is not related to the amount of compression. The helical line of the gear by ironing finishing exhibits a crown shape, which is the result of end-effect of compressing finishing. [ABSTRACT FROM AUTHOR]