Optimisation of slot entry hybrid gas bearings

Optimisation of hybrid gas bearings is considered from the view-point of minimum total power dissipation on load per unit power of the bearing. The optimisation techniques presented in this paper are derived on a varyin clearance basis. The selection of bearing design parameters for the optimum condition varies according to the restraints and constraints of the design. Therefore, the bearing design parameters are related and selected in order to achieve the optimum performance. The optimum design condition for minimum total power dissipation occurs when the friction power is three times the pumping power (K = 3).     

Fabricating micromilling tool using wire electrodischarge grinding and focused ion beam sputtering

In the present research, wire electrical discharge machining (WEDM) of γ titanium aluminide is studied. Selection of optimum machining parameter combinations for obtaining higher cutting efficiency and accuracy is a challenging task in WEDM due to the presence of a large number of process variables and complicated stochastic process mechanisms. In general, no perfect combination exists that can simultaneously result in both the best cutting speed and the best surface finish quality. This paper presents an attempt to develop an appropriate machining strategy for a maximum process criteria yield. A feed-forward back-propagation neural network is developed to model the machining process. The three most important parameters – cutting speed, surface roughness and wire offset – have been considered as measures of the process performance. The model is capable of predicting the response parameters as a function of six different control parameters, i.e. pulse on time, pulse off time, peak current, wire tension, dielectric flow rate and servo reference voltage. Experimental results demonstrate that the machining model is suitable and the optimisation strategy satisfies practical requirements.     

Optimal cutting condition determination for desired surface roughness in end milling

CNC end milling is a widely used cutting operation to produce surfaces with various profiles. The manufactured parts’ quality not only depends on their geometries but also on their surface texture, such as roughness. To meet the roughness specification, the selection of values for cutting conditions, such as feed rate, spindle speed, and depth of cut, is traditionally conducted by trial and error, experience, and machining handbooks. Such empirical processing is time consuming and laborious. Therefore, a combined approach for determining optimal cutting conditions for the desired surface roughness in end milling is clearly needed. The proposed methodology consists of two parts: roughness modeling and optimal cutting parameters selection. First, a machine learning technique called support vector machines (SVMs) is proposed for the first time to capture characteristics of roughness and its factors. This is possible due to the superior properties of well generalization and global optimum of SVMs. Next, they are incorporated in an optimization problem so that a relatively new, effective, and efficient optimization algorithm, particle swarm optimization (PSO), can be applied to find optimum process parameters. The cooperation between both techniques can achieve the desired surface roughness and also maximize productivity simultaneously.     

Optimization of multi-pass turning using particle swarm intelligence

This paper proposes a methodology for selecting optimum machining parameters in multi-pass turning using particle swarm intelligence. Often, multi-pass turning operations are designed to satisfy several practical cutting constraints in order to achieve the overall objective, such as production cost or machining time. Compared with the standard handbook approach, computer-aided optimization procedures provide rapid and accurate solutions in selecting the cutting parameters. In this paper, a non-conventional optimization technique known as particle swarm optimization (PSO) is implemented to obtain the set of cutting parameters that minimize unit production cost subject to practical constraints. The dynamic objective function approach adopted in the paper resolves a complex, multi-constrained, nonlinear turning model into a single, unconstrained objective problem. The best solution in each generation is obtained by comparing the unit production cost and the total non-dimensional constraint violation among all of the particles. The methodology is illustrated with examples of bar turning and a component of continuous form.     

Experimental evaluation on micro-texture parameters of carbide ball-nosed end mill in machining of titanium alloy

Parametric optimisation of micro-texture is important to improve the performance of cutting tool in metal cutting. In this study, the simulation models of micro-textured surface of cemented carbide are established by using ABAQUS platform, and fictitious stress distribution is analysed to select the optimum micro-texture structure. Then, many friction tests were carried out to compare the several different micro-textures. Based on the experimental data, the parametric optimisation model of micro-texture on cemented carbide surface is established, and its significant analysis is carried out. Moreover, the optimised parameters are applied to the ball-nose end mill in the machining of titanium alloy. The results show that the micro-texture can absorb stress and change the status of stress distribution on the surface. Among the micro-texture structures, the performance of micro-pit is the best one, i.e. surface stress micro-pit-texture is distributed uniformly, and the stress concentration micro-pit-texture is low, when the micro-pit-texture’s diameter is 46.6 μm, its depth is 27 μm, and its distance is 123 μm. The ball-nosed end mill with the optimised parameters has a stronger wear resistance than a normal tool.     

CO2 laser high-speed crack-free cutting of thick-section alumina based on close-piercing lapping technique

The greatest obstacles encountered in laser cutting of thick-section ceramics are catastrophic fracture and low cutting speed. Close-piercing lapping (CPL) technique has provided a cutting strategy via suppressing the thermal stress development during cutting to achieve crack-free cutting. Although this technique provided a wide operating window, the low process efficiency limited it to further industrial applications. In order to improve the process efficiency, the mechanism of CPL crack-free cutting should be understood and hence, the corresponding process parameters can be optimised for high-speed crack-free cutting. Based on the numerical and experimental study in this work, it was found that the sufficient cooling effect during laser-off periods was crucial to develop a low thermal-stress distribution during CPL cutting, by which the crack-free cutting can be achieved. Based on this finding, a low pulse repetition rate with low pulse duty cycle cutting process was proposed. Also, a procedure for process parameters optimisation was presented, by which CO₂ laser high-speed crack-free profile cutting of 6 mm thick alumina was demonstrated. The corresponding cutting speed (i.e. 90 mm/min for straight line cutting and 80 mm/min for profile cutting) was significantly higher than the CPL technique (i.e. 12 mm/min).     

Optimisation for hot turning operations with multiple performance characteristics

This paper presents an investigation on the optimisation and the effect of cutting parameters on multiple performance characteristics (the tool life and the workpiece surface roughness) obtained by hot turning operations. A plan of experiments based on the Taguchi method was designed. M20 sintered carbide as tool and the high manganese steel as workpiece material were used in experiments. The workpiece material heated with liquid petroleum gas flame was machined under different settings of feed rate, depth of cut, cutting speed and workpiece temperature on a lathe. The results showed that cutting speed and feed rate were the dominant variables on multiple cutting performance characteristics. An optimum parameter combination was obtained by using statistical analysis.     

An Experimental Analysis and Optimisation of the CO2 Laser Cutting Process for Metallic Coated Sheet Steels

An experimental investigation is presented which analyses the CO2 laser cutting process for difficult-to-cut metallic coated sheet steels, which are called GALVABOND. It shows that by proper control of the cutting parameters, good quality cuts are possible at high cutting rates. Plausible trends of the energy efficiency (percentage of energy used in cutting) with respect to the various process parameters are analysed. Visual examination indicates that when increasing the cutting rate to up to 5000 mm min⁻¹ , kerfs of better quality than those produced using the parameters suggested in an early study can be achieved. Some kerf characteristics such as the width, heat affected zone and dross, in terms of the process parameters are also discussed. A statistical analysis has arrived at the relationships between the cutting speed, laser power and workpiece thickness, from which a recommendation is made for the selection of optimum cutting parameters for processing GALVABOND material.     

Towards the integration of process and production planning: an optimisation model for cutting parameters

This paper aims to contribute to process and production planning integration through the development of a new cutting parameters optimisation model. The developed model considers simultaneously the technology-related constraints and a shop floor constraint determined by the available time at each workstation. The latter, being a constraint related to the part machining time, is associated with the set of all elementary machining operations and implies the development of a new multi-operation optimisation model. In this approach, part machining time is a new variable for shop floor scheduling. Since the limiting factor of workstation available time at every scheduling date depends on the shop floor status, optimum part machining time can range from the time for minimum cost to the time for maximum production rate. The introduction of the available time in the optimisation process allows for the generation of improved schedules according to several performance measures. The proposed optimisation model is non-linear, uni-criterion and multi-variable. The search of the optimal solution is carried out using sequential quadratic programming.     

切削参数智能生成技术研究

为提高先进制造装备的应用水平,在已有切削数据基础上如何自动生成更多的切削参数是一个重要但难以获得满意解决方案的课题。本文提出一种以基本切削数据库为基础综合利用多种人工智能技术,通过学习修正、记忆、实时生成对实际加工环境具有广泛适应能力的最佳切削参数的新方法。以回归分析和BP神经网络为例进行了阐述,并给出了具体实例。