Selection of optimum cutting condition of cobalt-based superalloy with GONNS

Machining of new superalloys is challenging. Automated software environments for determining the optimal cutting conditions after reviewing a set of experimental results are very beneficial to obtain the desired surface quality and to use the machine tools effectively. The genetically optimized neural network system (GONNS) is proposed for the selection of optimal cutting conditions from the experimental data with minimal operator involvement. Genetic algorithm (GA) obtains the optimal operational condition by using the neural networks. A feed-forward backpropagation-type neural network was trained to represent the relationship between surface roughness, cutting force, and machining parameters of face-milling operation. Training data were collected at the symmetric and asymmetric milling operations by using different cutting speeds (V _c), feed rates (f), and depth of cuts (a _p) without using coolant. The surface roughness (Ra_asymt, Ra_symt) and cutting force (Fx_asymt, Fy_asymt, Fz_asymt, Fx_symt, Fy_symt, Fz_symt) were measured for each cutting condition. The surface roughness estimation accuracy of the neural network was better for the asymmetric milling operation with 0.4% and 5% for training and testing data, respectively. For the symmetric milling operations, slightly higher estimation errors were observed around 0.5% and 7% for the training and testing. One parameter was optimized by using the GONNS while all the other parameters, including the cutting forces and the surface roughness, were kept in the desired range.     

Optimal design of 3-DOF PKM module for friction stir welding

Friction stir welding (FSW) has recently emerged as a solid-joining technology for high-strength aluminum alloys and light metal welding. The large axial force to be maintained between the welding tool and workpiece is the primary requirement of FSW process, which has also been a great obstacle to the design and application of FSW in manufacturing. Further complicating the issue is the need to perform FSW over three-dimensional contours, which requires a mechanism dexterous enough to set the stir pin used in welding to track a predefined trajectory with prescribed poses. Apart from the position specification, the design of a dexterous mechanism to pose the orientation of stir pin is a great challenge. This paper proposed the application of 3-PRS (P, R, and S standing for prismatic, revolute, and spherical joint, respectively) parallel mechanism as a welding tool head and employed it to form a five-axis welding machine tool for FSW. In order to accommodate the orientation capability requirements, the kinematic feature has been analyzed. With the dimensionless treatment of the Jacobian matrix of 3-PRS manipulator, a global condition index is proposed to estimate the kinematic dexterity in the whole orientation workspace. Finally, by means of an optimal design method and performance atlas, optimal designs of the 3-PRS parallel mechanism is carried out. A preferable set of optimized geometric parameters are obtained to achieve a compact and dexterous design, and the optimization results are used in development of a prototype machine for FSW.     

Parameter optimization of friction stir welding of cryorolled AA2219 alloy using artificial neural network modeling with genetic algorithm

In this paper, parameter optimization of FSW of cryorolled AA2219 alloy was carried out to obtain defect free weld joint with maximum weld strength. To achieve this, artificial neural network (ANN) was used to model the relationship between the input parameters and the mechanical and corrosion properties (output) of the weld joints. The optimal FSW parameters were determined by genetic algorithm (GA). The feasible solution of the GA was tool rotational speed of 1005 rpm, tool travel speed of 20 mm/min and tool tilt angle of 3°. The feasible parameter was used to weld and check the ability of the parameter to produce better weld joint than the L₉ orthogonal array parameters. The weld, subjected to the confirmation test, was investigated by means of metallurgical, mechanical, and corrosion testing. This process reduces the costs associated with trial runs to obtain optimal parameters and also the production cost of the cryorolled (CR) plate which is high.     

铝-钢异种金属搅拌摩擦焊研究现状及展望

搅拌摩擦焊作为一种新型的固相连接技术,具有优质、高效、节能、环境友好等优点,广泛应用于航空航天、船舶、轨道交通、汽车等工业领域。为了实现车体轻量化,铝-钢异种金属的搅拌摩擦焊研究得到越来越多的重视。从搅拌摩擦焊的工艺、组织及性能三个方面,对铝-钢异种金属搅拌摩擦焊的国内外研究现状进行综述。研究现状表明,通过控制界面金属间化合物的生成量,可得到高质量的铝-钢异种金属的搅拌摩擦对焊、搭焊及点焊接头,优化工艺参数下其拉伸性能可与母材相当,然而在铝-钢界面的精细结构表征和复杂应力状态下接头的力学性能方面还需要进一步的深入研究。此外,对未来铝-钢异种金属搅拌摩擦焊的研究方向进行展望。     

Application of GONNS to predict constrained optimum surface roughness in face milling of high-silicon austenitic stainless steel

Surface roughness is a technical requirement for machined products and one of the main product quality specifications. In the present research, a genetically optimized neural network system (GONNS) is proposed for prediction of constrained optimal cutting conditions in face milling of a high-silicon austenitic stainless steel (UNS J93900) in order to minimize surface roughness. In order to attain minimum operation numbers and decrease the cost of machining, an experimental scheme was arranged by using Taguchi method. The considered parameters were cutting speed, feed, depth of cut, and engagement. Cutting force components and surface roughness were measured, and then analysis of variance is performed. The results show that the feed is the dominant factor affecting the surface roughness. Backpropagation artificial neural network was utilized to create predictive models of surface roughness and cutting forces exploiting the experimental data, and the genetic algorithm was employed to find the constrained optimum of surface roughness. Finally, in order to validate the method, an experiment with the obtained optimal cutting condition was carried out, and the results were compared with the predicted value of surface roughness. The corresponding results show the capability of GONNS to predict constrained surface roughness.     

Grey relational and neural network approach for multi-objective optimization in small scale resistance spot welding of titanium alloy

The prediction and optimization of weld quality characteristics in small scale resistance spot welding of TC2 titanium alloy were investigated. Grey relational analysis, neural network and genetic algorithm were applied separately. Quality characteristics were selected as nugget diameter, failure load, failure displacement and failure energy. Welding parameters to be optimized were set as electrode force, welding current and welding time. Grey relational analysis was conducted for a rough estimation of the optimum welding parameters. Results showed that welding current played a key role in weld quality improvement. Different back propagation neural network architectures were then arranged to predict multiple quality characteristics. Interaction effects of welding parameters were analyzed with the proposed neural network. Failure load was found more sensitive to the change of welding parameters than nugget diameter. Optimum welding parameters were determined by genetic algorithm. The predicted responses showed good agreement with confirmation experiments.

     

Optimization of friction stir welding process of AA7075 aluminum alloy to achieve desirable mechanical properties using ANFIS models and simulated annealing algorithm

Current work deals with experimental investigation, modeling, and optimization of friction stir welding process (FSW) to reach desirable mechanical properties of aluminum 7075 plates. Main factors of process were tool pin profile, tool rotary speed, welding speed, and welding axial force. Also, main responses were tensile strength, yield strength, and hardness of welded zone. Four factors and five levels of central composite design have been utilized to minimize the number of experimental observations. Then, adaptive neuro-fuzzy inference systems (ANFIS) have been used to generate mapping relationship between process factors and main response using experimental observations. Afterward, the developed models were applied as objective function to select optimal parameters, in which the process reaches to its desirable mechanical properties by using the simulated annealing algorithm. Results indicated that the tool with square pin profile, rotary speed of 1,400 RPM, welding speed of 1.75 mm/s, and axial force of 7.5 KN resulted in desirable mechanical properties in both cases of single response and multi-response optimization. Also, these solutions have been verified by confirmation tests and FSW process physical behavior. These verifications indicated that both ANFIS model and simulated annealing algorithm are appropriate tools for modeling and optimization of process.     

A cutting force prediction dynamic model for side milling of ceramic matrix composites C/SiC based on rotary ultrasonic machining

The relevance, importance and presence of industrial robots in manufacturing have increased over the years, with applications in diverse new and nontraditional manufacturing processes. This paper presents the complete concept and design of a novel friction stir welding (FSW) robotic platform for welding polymeric materials. It was conceived to have a number of advantages over common FSW machines: it is more flexible, cheaper, easier and faster to setup and easier to programme. The platform is composed by three major groups of hardware: a robotic manipulator, a FSW tool and a system that links the manipulator wrist to the FSW tool (support of the FSW tool). This system is also responsible for supporting a force/torque (F/T) sensor and a servo motor that transmits motion to the tool. During the process, a hybrid force/motion control system adjusts the robot trajectories to keep a given contact force between the tool and the welding surface. The platform is tested and optimized in the process of welding acrylonitrile butadiene styrene (ABS) plates. Experimental tests proved the versatility and validity of the proposed solution.     

Optimization of activated TIG welding parameters for improving weld joint strength of AISI 4135 PM steel by genetic algorithm and simulated annealing

Weld quality is a very important working aspect of the manufacturing and construction industries. In this research work, an attempt has been made to optimize the parameters of activated tungsten inert gas (A-TIG) welding of sintered hot-forged AISI 4135 steel produced through the powder metallurgy route. Experiments were performed based on Taguchi L9 orthogonal array. Response surface methodology was used to create regression equations, and process parameters were optimized using genetic algorithm (GA) and simulated annealing (SA). Process parameter optimization is multi-input to single output (tensile strength), in which the quality of output depends upon input parameters like current, voltage, welding speed, and gas flow rate. The present study was conducted to maximize the output of the A-TIG welding of sintered hot-forged AISI 4135 steel and fix the input parameters. The results indicate that the voltage and current have a maximum influence on tensile strength in A-TIG-welded joint. Confirmation experiments have also been conducted to validate the optimized parameters.     

Study on governing parameters of thermal history during underwater friction stir welding

Underwater friction stir welding (FSW) could widely extend the submarine applications of solid-state welding methods. Since, in the case of underwater FSW, the temperature field exhibits profound effects on the acquired weld properties, studying the corresponding governing parameters is of high priority. With this end in view, in order to explicate the heat generated by the FSW tool, the applied forces on the FSW tool, as the unknown parameters in the heat generation equation, are obtained. Subsequently, the heat transfer of the surrounding fluid, which dictates the heat transfer through the workpiece is investigated. The results reveal that upon comparison to FSW in air medium, both translational and axial forces considerably increase leading to greater heat generated by the underwater FSW tool. However, the peak temperature in each point during underwater welding declines dramatically (40 %) compared to the in-air welding, which can be attributed to the extreme boiling heat transfer of water on both the workpiece and FSW tool. This behavior may be the main reason for the acquired mechanical properties of the underwater-welded AA7075-T6 plates as a precipitating hardening alloy. The mentioned heat transfer is non-uniform over the workpiece and comprises different types including nucleation and transition boiling as well as free convection. Furthermore, the study of the mechanical characteristics revealed that underwater welding leads to joints with more strength and lower ductility compared to those obtained by in-air welding.     

铝合金车体非刚性支撑搅拌摩擦焊修复设备研制

现有搅拌摩擦焊修复设备多为固定龙门式结构,且需要背部刚性支撑,移动适应性较差。针对铝合金货车车体破损修复问题,提出一种采用搅拌摩擦焊对其进行在线修补作业的方案,设计了一种移动式非刚性支撑搅拌摩擦焊修复设备。为了解决非刚性支撑焊接修复过程中支撑力不足、大变形等问题,设计了旋转伸缩臂、花瓣式砧板等结构,为焊接提供足够的顶锻力,并利用有限元分析对花瓣式砧板结构进行优化设计,改善支撑效果;设计了铣焊半径自动调整结构,用于不同大小尺寸破损的修复焊接。该移动式非刚性支撑搅拌摩擦焊修复设备能够在不拆解车体板材的情况下,修复6 mm厚铝合金车体,现场实际应用表明：采用该非刚性支撑搅拌摩擦焊修复效果良好,焊接表面光滑无飞边,内部无沟槽隧道缺陷,焊缝抗拉性能达到母材的71.6%左右。     

铝合金液冷冷板窄台阶搭接搅拌摩擦焊工艺研究

文中研究了铝合金液冷冷板窄台阶搭接搅拌摩擦焊工艺。冷板基底材料为6063 铝合金,盖板材料为3A21 铝合金。设计了窄搭接搅拌头,减小了轴肩宽度和焊接压力,增加了接头焊接时材料的流动性,针对不同焊深窄台阶冷板进行了窄搭接焊接工艺试验。研究表明,通过优化搅拌头形貌尺寸和工艺参数能够实现4-2（焊缝深度–台阶宽度,mm）、6-4 和9-6 的窄搭接搅拌摩擦焊焊接,焊接过程中进行定位预焊能有效避免产生焊缝S 型曲线,前进侧为6063 或3A21 时均能形成良好的焊缝。     

Microstructures and mechanical properties of friction stir welded dissimilar steel-copper joints

Welding dissimilar metals by fusion welding is challenging. It results in welding defects. Friction stir welding (FSW) as a solid-state joining method can overcome these problems. In this study, 304L stainless steel was joined to copper by FSW. The optimal values of the welding parameters traverse speed, rotational speed, and tilt angle were obtained through Response surface methodology (RSM). Under optimal welding conditions, the effects of welding pass number on the microstructures and mechanical properties of the welded joints were investigated. Results indicated that appropriate values of FSW parameters could be obtained by RSM and grain size refinement during FSW mainly affected the hardness in the weld regions. Furthermore, the heat from the FSW tool increased the grain size in the Heat-affected zones (HAZs), especially on the copper side. Therefore, the strength and ductility decreased as the welding pass number increased because of grain size enhancement in the HAZs as the welding pass number increased.     

铝合金搅拌摩擦焊技术研究

论述了搅拌摩擦焊的焊接方法、工艺过程和基本原理；通过大量的工艺参数优化，解决了焊缝中的孔洞问题，使ＬＦ６板材搅拌摩擦焊的焊缝达到与母材等强，ＬＹ１２焊后未经热处理，连接强度接近母材的８０（。由金相分析可以看出，搅拌摩擦焊属于固相连接，焊缝晶粒细小，无气孔、夹杂、裂纹等焊接缺陷。初步应力测试发现，搅拌摩擦焊焊缝比熔焊焊缝的残余应力低。试验研究结果表明，搅拌摩擦焊焊接过程稳定可靠，焊接接头性能良好，具有广泛的应用前景。     

Modeling and optimization of Nd:YAG laser micro-weld process using Taguchi Method and a neural network

The use of a pulsed Nd:YAG laser in the 0.1 mm- thick aluminum alloy lap micro-weld process was optimized. The welding parameters that influence the quality of the pulsed Nd:YAG laser lap micro-weld were evaluated by measuring of the tensile-shear strength. In this work, the Taguchi method was adopted to perform the initial optimization of the pulsed Nd:YAG laser micro-weld process parameters. A neural network with a Levenberg-Marquardt back-propagation (LMBP) algorithm was then adopted to develop the relationships between the welding process parameters and the tensile-shear strength of each weldment. The optimal parameters of the pulsed Nd:YAG laser micro-weld process were determined by simulating parameters using a well-trained back-propagation neural network model. Experimental results illustrate the proposed approach.     

Numerical modeling of friction stir welding process: a literature review

This survey presents a literature review on friction stir welding (FSW) modeling with a special focus on the heat generation due to the contact conditions between the FSW tool and the workpiece. The physical process is described and the main process parameters that are relevant to its modeling are highlighted. The contact conditions (sliding/sticking) are presented as well as an analytical model that allows estimating the associated heat generation. The modeling of the FSW process requires the knowledge of the heat loss mechanisms, which are discussed mainly considering the more commonly adopted formulations. Different approaches that have been used to investigate the material flow are presented and their advantages/drawbacks are discussed. A reliable FSW process modeling depends on the fine tuning of some process and material parameters. Usually, these parameters are achieved with base on experimental data. The numerical modeling of the FSW process can help to achieve such parameters with less effort and with economic advantages.     

A study on mechanical characteristics of the friction stir welded A6005-T5 extrusion

In this paper, A6005-T5 extruded aluminum alloy sheets which are used for floor, roof or wall panels of railroad vehicles were welded by the friction stir welding (FSW) and gas metal arc welding (GMAW) techniques. The mechanical characteristics including the tensile strength, micro-hardness and fatigue strength of the FSW joint were compared to those of the base metal and GMAW joints. In order to determine the relationship between the welding variables of FSW and the mechanical characteristics of the joint, the response function was derived using the least square method and the sensitivity analysis was performed. The rotational speed, welding speed and tilting angle of the welding tool were chosen as design variables. On the basis of the Plackett-Burman design table, eight different FSW experiments were done, and then the effects of design variables on the mechanical characteristics of the FSW joint were analyzed. The result showed that the welding speed has a most significant effect on the tensile and fatigue strength. In the case of the micro-hardness, the effect of the tilting angle was the biggest.     

Kinematic Sensitivity Analysis and Dimensional Synthesis of a Redundantly Actuated Parallel Robot for Friction Stir Welding

Friction stir welding(FSW) has been widely applied in many fields as an alternative to traditional fusion welding. Although serial robots can provide the orientation capability required to weld along curved surfaces, they cannot adequately support the huge axial downward forces that FSW generates. Available parallel mechanism architectures, particularly redundantly actuated architectures for FSW, are still very limited. In this paper, a redundantly actuated 2 UPR-2 RPU parallel robot for FSW is proposed, where U denotes a universal joint, R denotes a revolute joint and P denotes a prismatic pair. First, its semi-symmetric structure is described. Next, inverse kinematics analysis involving an analytical representation of rotational axes is implemented. Velocity analysis is also conducted, which leads to the formation of a Jacobian matrix. Sensitivity performance is evaluated utilizing level set and convex optimization methods, where the local sensitivity indices are unit consistent, coordinate free, and of definite physical significance. Furthermore, global and hierarchical sensitivity indices are proposed for the design process. Finally, dimension synthesis is conducted based on the sensitivity indices and the optimal link parameters of the parallel robot are obtained. In summary, this paper proposes a dimensional synthesis method for a redundantly actuated parallel robot for FSW based on sensitivity indices.     

On the material flow in FSW of T-joints: Influence of geometrical and tecnological parameters

Friction stir welding (FSW) now definitively reached a large interest in the scientific community and what is more in the industrial environment, due to the advantages of such solid state welding process with respect to the classic ones. The latter aspects are relevant also with reference to joints characterized by a complex geometry. What is more, advanced FEM tools permit to develop effective engineering of the processes; quantitative results can be acquired from numerical simulations once basic information, as the process mechanics and the material flow, are certain. Material flow plays a fundamental role in FSW since it determines the effectiveness of the joints or, in turn, the insurgence of defects. In the paper, the material flow in the FSW of aluminum alloys T-joints is investigated at the varying of the most relevant technological and geometrical parameters with numerical simulations and experiments. In particular, to investigate the metal flow, a wide campaign of experimental tests and observations was developed utilizing a thin foil of brass as marker, placed at the interface of the two blanks to be welded. Some relevant conclusions on the process mechanics and on the actual material flow determining the material bonding are outlined, permitting an insight of the FSW of T-joints.     

Mode I failure modeling of friction stir welding joints

This paper analyzes mechanical response by finite element method up to the decohesion failure in fracture mode I for joints of friction stir welding (FSW) of an aluminum alloy. It first describes experimental investigations on specimens with FSW embedded, subjected to uniform traction and local punch tests used to characterize local elastic and plastic material parameters. The heterogeneity of the mechanical properties induced by the FSW process is taken into account for the elastic-plastic finite element simulation. The growing damage and the opening failure of the welding zone are described by the adoption of a cohesive interface model with specific mechanical properties.