Prediction Model for Net Cutting Specific Energy in CNC Turning

A prediction model for net cutting specific energy in computer numerical control(CNC)turning based on turning parameters and tool wear is developed.The model can predict the net cutting energy consumption before turning.The prediction accuracy of the model is verified in AISI 1045 steel turning.The comparative experimental results show that the prediction accuracy of the model is significantly improved because the influence of tool wear is taken into account.Finally,the influences of turning parameters and tool wear on net cutting specific energy are studied.With the increase of cutting depth,the net cutting specific energy decreases.With the increase of spindle speed,the additional load loss power of spindle drive system increases,so the net cutting specific energy increases.The net cutting specific energy increases approximately linearly with tool wear.The results are helpful to formulate efficient and energy-saving CNC turning schemes and realize low‑carbon manufacturing.

Milling Parameters Optimization of Al-Li Alloy Thin-Wall Workpieces Using Response Surface Methodology and Particle Swarm Optimization

To improve the milling surface quality of the Al-Li alloy thin-wall workpieces and reduce the cutting energy consumption.Experimental research on the milling processing of AA2195 Al-Li alloy thin-wall workpieces based on Response Surface Methodology was carried out.The single factor and interaction of milling parameters on surface roughness and specific cutting energy were analyzed,and the multi-objective optimization model was constructed.The Multiobjective Particle Swarm Optimization algorithm introducing the Chaos Local Search algorithm and the adaptive inertial weight was applied to determine the optimal combination of milling parameters.It was observed that surface roughness was mainly influenced by feed per tooth,and specific cutting energy was negatively correlated with feed per tooth,radial cutting depth and axial cutting depth,while cutting speed has a non-significant influence on specific cutting energy.The optimal combination of milling parameters with different priorities was obtained.The experimental results showed that the maximum relative error of measured and predicted values was 8.05%,and the model had high reliability,which ensured the low surface roughness and cutting energy consumption.It was of great guiding significance for the success of Al-Li alloy thin-wall milling with a high precision and energy efficiency.

Analysis of Shear and Firiction Behaviors in End Milling Process

As a new approach to analyze shear behaviors in the shear plane and chip-tool friction behaviors in the chip-toolcontact region during an end milling process,this paper introduces a method to transform an up-end milling processto an equivalent oblique cutting process.In this approach,varying undeformed chip thicknesses and cutting forcesin the up-end milling process are replaced with the equivalent of oblique cutting ones.Experimental investigationsfor Inconel 718 were performed to verify the presented model.

Development of a drill energy utilization index for aiding selection of drill machines in surface mines

Drill machines used in surface mines, particularly in coal, is characterized by a very poor utilization （around 40%） and low availability （around 60%）. The main purpose of this study is to develop a drill selec- tion methodology and simultaneously a performance evaluation technique based on drill cuttings produced and drilling rate achieved. In all 28 blast drilled through were investigated. The drilling was accomplished by 5 different drill machines of Ingersoll-Rand and Revathi working in coal mines of Sonepur Bazari （SECL） and Block-II （BCCL）. The drills are Rotary and Rotary Percussive type using tri- cone rock roller bits. Drill cuttings were collected and sieve analysis was done in the laboratory. Using Rosin Ramler Diagram, coarseness index （CI）, mean chip size （d）, specific-st trace area （SSA） and charac- teristic particle size distribution curves for all the holes drilled were plotted. The predictor equation for drill penetration rate established through multiple regressions was found to have a very good correlation with an index of determination of 0.85. A comparative analysis of particle size distribution curves was used to evaluate the drill efficiency. The suggested approach utilises the area under the curve, after the point of trend reversal and brittleness ratio of the respective bench to arrive at drill energy utilization index （DEUI）, for mapping of drill machine to bench, The developed DEU1 can aid in selecting or mapping a right machine to right bench for achieving higher penetration rate and utilizations.

Experimental technique to analyze the influence of cutting conditions on specific energy consumption during abrasive metal cutting with thin discs

Specific energy consumption is an important indicatorfora better understanding of the machinability of materials.The present study aims to estimate the specific energy consumption for abrasive metal cutting with ultrathin discs at comparatively low and medium feed rates.Using an experimental technique,the cutting power was measured at four predefined feed rates for S235JR,intermetallic Fe-Al(40%),and C45K with different thermal treatments.The variation in the specific energy consumption with the material removal rate was analyzed through an empirical model,which enabled us to distinguish three phenomena of energy dissipation during material removal.The thermal treatment and mechanical properties of materials have a significant impact on the energy consumption pattern,its corresponding components,and cutting power.Ductile materials consume more specific cutting energy than brttle materials.The specific cutting energy is the minimum energy required to remove the material,and plowing energy is found to be the most significant phenomenon of energy dissipation.