Energy efficient cutting parameter optimization

Mechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energy efficient technologies for reducing energy consumption and improving energy efficiency of their machining processes. In a practical machining process, cutting parameters are vital variables set by manufacturers in accordance with machining requirements of workpiece and machining condition. Proper selection of cutting parameters with energy consideration can effectively reduce energy consumption and improve energy efficiency of the machining process. Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimization, and a large amount of literature have been published. This paper conducts a comprehensive literature review of current studies on energy efficient cutting parameter optimization to fully understand the recent advances in this research area. The energy consumption characteristics of machining process are analyzed by decomposing total energy consumption into electrical energy consumption of machine tool and embodied energy of cutting tool and cutting fluid. Current studies on energy efficient cutting parameter optimization by using experimental design method and energy models are reviewed in a comprehensive manner. Combined with the current status, future research directions of energy efficient cutting parameter optimization are presented.

Cutting Parameter Optimization in Finishing Milling of Ti-6Al-4V Titanium Alloy under MQL Condition using TOPSIS and ANOVA Analysis

Titanium and its alloys give immense specific strength, imparting properties such as corrosion and fracture resistance, making them the right candidate for medical and aerospace applications. There is a wide range of engineering applications that use titanium alloys in a variety of forms. The cost of these alloys is slightly higher in comparison to other variants due to the problematic extraction of the molten process. To reduce costs, titanium alloy products could be made by casting, isothermal forging, radial swaging, or powder metallurgy, although these techniques require some kind of finishing machining process. Titanium and its alloys are difficult to machine due to skinny chips leading to a small cutting tool-workpiece contact area. The thermal conductivity of titanium alloys is too low and the stress produced is too large due to the small contact area, which results in very high cutting temperatures. This paper deals with the experimental study of the influence of the Minimum Quantity Lubricant (MQL) environment in the milling of Ti-6Al-4V alloy considering the optimization of surface roughness and production rate. Taguchi-based TOPSIS and ANOVA were used to analyze the results. The experimental results show that MQL with vegetable oil is successfully applied in the milling of Ti-6Al-4V. The research confirms the suitability of TOPSIS in solving the Multiple Criteria Decision Making (MCDM) issue, by choosing the best alternative at Vc=120m/min, fz=0.065mm/tooth, and ap=0.2mm, where the surface roughness and material removal rate are 0.41µm and 44.1492cm3/min respectively. Besides, ANOVA can be used to predict the best parameters set in the milling process based on the regression model. The parameters predicted by ANOVA analysis do not coincide with any implemented parameters

A Review on the Abrasive Water-Jet Machining of Metal–Carbon Fiber Hybrid Materials

The bonding of metallic alloys and composite materials in the form of a hybrid structure is a line of great interest for the current industry. The different machinability of both materials requires a specific machining process. Abrasive water-jet machining (AWJM) is an excellent technology for the simultaneous machining of both materials. However, defects at the micro and macro-geometric level have been detected in several scientific articles. In this review, a detailed study of the two main defects in metals, composite materials and hybrid structures has been developed. The conclusions of several scientific articles have been exposed for a better understanding of the topic in articles between 1984 and 2020. The influence of the cutting parameters on the reduction in kinetic energy of the water jet and the order of stacking of the materials in the hybrid structure is the main objective in order to minimize these defects. Cutting parameter optimization studies, predictive model proposals, process-associated defects and evaluation methodologies have been discussed. The aim of this article is to set a solid background on AWJM machining in hybrid structures and on the influence of cutting parameters on generated defects and machining strategies to obtain the best results at a macro and micro-geometric level.

Process Parameters Optimization of Thin-Wall Machining for Wire Arc Additive Manufactured Parts

Additive manufacturing (AM) is an arising production process due to the possibility to produce monolithic components with complex shapes with one single process and without the need for special tooling. AM-produced parts still often require a machining phase, since their surface finish is not compliant with the strict requirements of the most advanced markets, such as aerospace, energy, and defense. Since reduced weight is a key requirement for these parts, they feature thin walls and webs, usually characterized by low stiffness, requiring the usage of low productivity machining parameters. The idea of this paper is to set up an approach which is able to predict the dynamics of a thin-walled part produced using AM. The knowledge of the workpiece dynamics evolution throughout the machining process can be used to carry out cutting parameter optimization with different objectives (e.g., chatter avoidance, force vibrations reduction). The developed approach exploits finite element (FE) analysis to predict the workpiece dynamics during the machining process, updating its changing geometry. The developed solution can automatically optimize the toolpath for the machining operation, generated by any Computer Aided Manufacturing (CAM) software updating spindle speed in accordance with the selected optimization strategies. The developed approach was tested using as a test case an airfoil.

Force Prediction and Cutting-Parameter Optimization in Micro-Milling Al7075-T6 Based on Response Surface Method

Optimization of cutting parameters in micro-milling is an important measure to improve surface quality and machining efficiency of the workpiece. Investigation of micro-milling forces prediction plays a positive role in improving machining capacity. To predict micro-milling forces and optimize micro-milling cutting parameters (per-feed tooth (fz), axial cutting depth (ap), spindle speed (n) and tool extended length (l)), a rotatable center composite experiment of micro-milling straight micro-groove in the workpiece of Al7075-T6 were designed, based on second-order response surface methods. According to the experiment results, the least square method was used to estimate the regression coefficient corresponding to the cutting parameters. Simultaneously, the response prediction model of micro-milling was established and successfully coincide the predicted values with the experiment values. The significance of the regression equation was tested by analysis of variance, and the influence of micro-milling cutting parameters on force and top burrs morphology was studied. The experiment results show that in a specific range of cutting parameters, ap and fz have a significant linear relation with the micro-milling force and the top burrs width. According to the optimal response value, the optimized cutting parameters for micro-milling obtained as: n is 11,393 r/min, fz is 6 µm/z, ap is 11 μm and l is 20.8 mm. The research results provide a useful reference for the selection of cutting parameters for micro-milling.