Selection Of Cutting Parameters Research Articles

Genetic algorithm-based error correction algorithm for CNC turning machining of mechanical parts

This paper discusses how to improve the machining precision in the turning of slender shaft. The main cause of dimensional error in slender shaft machining is analyzed by establishing dimensional error model and using genetic algorithm to optimize cutting parameter selection. Based on this, the proportional-integral-differential control error compensation is proposed to reduce the error in the turning process of slender shaft. Through the simulation experiment, the machining size error of slender shaft under different cutting parameters is obtained. It is found that the increase of back blowing and feed rate will aggravate the dimensional error, while the increase of CS will reduce the dimensional error. The error after the proportional-integral-differential control error compensation is much smaller than that without the error compensation. The experimental results show that the method is reliable in reducing the errors in the turning of slender shaft, and can realize the machining mode with higher precision and efficiency. This is of great significance to the development of machinery manufacturing industry.

An updated tuning methodology of vibration absorber for machining chatter suppression

The Equal Real Troughs optimization criterion of vibration absorber improves unconditionally stable cutting depth of single-dimensional chatter vibration. However, the stability pockets within the lobes are decreased after tuning, which therefore causes the machining efficiency reduction at certain spindle speed. Noting that the spindle speed is preferred as a constant in the workshop once the assembly of machine tool/cutter/workpiece are determined, an updated optimization methodology is proposed to maximize the critical cutting depth within specified spindle speed range by utilizing the lobing effect. The optimization routine is formulated based on the boundary identification of stability lobe diagram (SLD), which is derived by the Zero-Order Analytical (ZOA) solution. The tuning methodology is validated on the design of vibration absorber in order to raise the machining stability of slender milling/turning cutter with large length-diameter ratio. The updated methodology enables the stability lobes relocation of single/two-dimensional chatter vibration for achieving higher critical cutting depth at specified spindle speed, and assures the stability width assignable considering the robustness of cutting parameter selection.

Cutting parameters influence on surface integrity of dry-turned Ti6Al4V alloy

Surface integrity is a high-interest characteristic of machined parts in the aeronautical industry. Numerous variables are related to the surface integrity and require to be studied as a whole. Generally, the cutting parameters affect these variables’ results, especially in sustainable machining operations. This work analyses the cutting speed and feed influence on different parameters such as the roughness, macrogeometrical deviations and microhardness of Ti6Al4 V turned in dry. All variables have been taken into account simultaneously, given the synergy between the different input and output variables. In general terms, feed seems to be the most influential variable in macrogeometrical and microgeometrical deviations, while cutting speed affects mainly the microhardness parameter. In addition, an optimisation process has been carried out. In the first step, the optimal cutting parameters were obtained for the lowest cutting speed and the lowest feed values due to the roughness is the predominant variable. Therefore, a second optimisation step was performed excluding this variable, optimising the macrogeometrical deviations and the microhardness. In this case, higher cutting speed and feed values seem to be the optimal cutting parameters. Finally, different decision maps were plotted to facilitate the cutting parameter selection according to the property to optimise.

Learning machining stability using a bayesian model

Instabilities in machining can be detrimental. Usually, analytical model-predicted stability charts guide selection of cutting parameters to ensure stable processes. However, since inputs to the model seldom account for the speed-dependent behaviour of the cutting process or the dynamics, models often fail to guide stable cutting parameter selection in real industrial settings. To address this issue, this paper discusses how real experimentally classified stable and unstable cutting data with all its vagaries and uncertainties can instead be used to learn the stability behaviour using a supervised Bayes' learning approach. We expand previously published work to systematically characterize how probability distributions, training data size, and thresholding influence the learning capacity of the Bayesian approach. Prediction accuracies of up to 95% are shown to be possible. We also show how the approach nicely extends itself to a continuous learning process. Results can hence inform further development towards self-optimizing and autonomous machining systems.

Relative Varying Dynamics Based Whole Cutting Process Optimization for Thin-walled Parts

Thin-walled parts are typically difficult-to-cut components due to the complex dynamics in cutting process. The dynamics is variant for part during machining, but invariant for machine tool. The variation of the relative dynamics results in the difference of cutting stage division and cutting parameter selection. This paper develops a novel method for whole cutting process optimization based on the relative varying dynamic characteristic of machining system. A new strategy to distinguish cutting stages depending on the dominated dynamics during machining process is proposed, and a thickness-dependent model to predict the dynamics of part is developed. Optimal cutting parameters change with stages, which can be divided by the critical thickness of part. Based on the dynamics comparison between machine tool and thickness-varying part, the critical thicknesses are predicted by an iterative algorithm. The proposed method is validated by the machining of three benchmarks. Good agreements have been obtained between prediction and experimental results in terms of stages identification, meanwhile, the optimized parameters perform well during the whole cutting process.

Experimental Studies on Fabricating Lenslet Array with Slow Tool Servo.

On the demand of low-cost, lightweight, miniaturized, and integrated optical systems, precision lenslet arrays are widely used. Diamond turning is often used to fabricate lenslet arrays directly or molds that are used to mold lenslet arrays. In this paper, mainly by real-time monitoring position following error for slow tool servo, different fabrication parameters are quantitatively studied and optimized for actual fabrication, then by actual fabrication validation, uniform and high-fidelity surface topography across the actual whole lenslet array is achieved. The evaluated fabrication parameters include sampling strategy, inverse time feed, arc-length, etc. The study provides a quick, effective, and detailed reference for both convex and concave lenslet array cutting parameter selection. At the end, a smooth zonal machining strategy toolpath is demonstrated for fabricating concave lenslet arrays.

Crater wear prediction in turning Ti6Al4V considering cutting temperature effect

Tool wear has great impacts on tool life and cutting parameter selection in the machining process. As ceramic inserts are widely employed in efficient machining, it is of great significance to predict accurately the wear of ceramic tools. Crater wear is one of the most significant tool wear modes when turning difficult-to-cut materials at high speeds. The purpose of this research is to demonstrate the forming and influence mechanisms of crater wear in Ti6Al4V turning with ceramic inserts while considering cutting temperature at the tool-chip interface. An effort is made to describe quantitatively the appearance of crater wear from the perspective of temperature distribution based on crater wear experiments under various cutting conditions. In order to obtain the cutting temperature distribution efficiently, an analytical temperature prediction model is used in this paper. Then, a prediction model for crater wear depth and width considering the influence of the maximum temperature is developed. The maximum crater wear depth and width models are validated by a series of cutting experiments, and the outcomes prove that the proposed models are practical. Additionally, the influence of various cutting parameters on crater wear is discussed. The cutting speed has the greatest impact on crater wear, followed by the feed rate. Furthermore, this research can be used to improve cutting parameters for controlling crater wear.

A Compact Design of Underwater Mining Vehicle for the Cobalt-Rich Crust with General Support Vessel Part A: Prototype and Tests

This paper proposed a compact design of the subsea cobalt-rich crust mining vehicle with a general purpose support vessel for subsea resource exploration, sample collection, and research. The necessary functions were considered in the concept design, including walk, crushing/mining, sample collection, cutter head adaptation, vehicle orientation, crust texture measurement, awareness, positioning, and navigation. The prototype was tested in both tank and subsea environment. The sea trials were carried out with the support of a general purpose support vessel. The track design worked well in both the tank and subsea environment and the mining vehicle walked smoothly in the sea trial. The crust was crushed to the size of 2 mm and 10 mm with different cutting parameters and successfully collected by the jet pump, 6 kg in total. The crust texture was measured by the onboard sonar successfully and can be used for cutting parameter selection. The cameras captured the images of the subsea environment, but the actions of crushing and sample collection produced plumes, which blocked the camera vision. In the situation, the front image sonar can be used to keep the vehicle away from big rocks. The mining vehicle is not limited to the mining and sampling of subsea cobalt-rich crust. Most of the subsea solid resources on the seabed can be considered to use the compact mining vehicle for sampling and related research. The only issues to be considered are the crushing ability and sample size required.

Optimization of Milling Cutting Parameters under Carbon Efficiency Target

In order to improve the energy efficiency of the CNC milling process and reduce the carbon emissions during the milling process, this paper presents a method for cutting parameters selection by considering both materials remove rate and carbon emissions. First, a metric called carbon emissions efficiency is proposed with consideration of the materials remove rate and carbon emissions. Second, the carbon emissions evaluation model for NC milling process is built based on the input-output flows. Third, an optimization model for the parameters used in a milling operation is built where the spindle speed and feed rate are taken into account as optimization variables. Constraints are proposed based on the machine tool performance parameters and the quality requirements of the workpiece. A genetic algorithm is employed to solve the optimization problem. A case is studied to validate the model.

A cutting parameter-based model for cost and carbon emission optimisation in a NC turning process

ABSTRACTCutting parameter selection is significant for process planning which directly affects product quality, production efficiency and cost. However, most literature does not consider both the cost and the carbon emissions as optimisation objectives for process evaluation. This paper presents a method for cutting parameter selection by considering both cost and carbon emissions. First, a metric called profit per unit carbon emissions is proposed with consideration of the carbon emissions and processing cost. The relationships between cutting parameters，carbon emissions and processing cost are analysed. Second, an optimisation model for the parameters used in a turning process is built where the spindle speed, feed rate and cutting depth are taken into account as optimisation variables. Constraints are proposed based on machine parameters and processing quality requirements. A genetic algorithm (GA) is employed to solve the optimisation problem. Both experimental and industrial cases are studied to validate the model. In addition, the impacts of cutting parameters on carbon emissions are discussed.

Effects of Machining Parameters on the Quality in Machining of Aluminium Alloys Thin Plates

Nowadays, the industry looks for sustainable processes to ensure a more environmentally friendly production. For that reason, more and more aeronautical companies are replacing chemical milling in the manufacture of skin panels and thin plates components. This is a challenging operation that requires meeting tight dimensional tolerances and differs from a rigid body machining due to the low stiffness of the part. In order to fill the gap of literature research on this field, this work proposes an experimental study of the effect of the depth of cut, the feed rate and the cutting speed on the quality characteristics of the machined parts and on the cutting forces produced during the process. Whereas surface roughness values meet the specifications for all the machining conditions, an appropriate cutting parameters selection is likely to lead to a reduction of the final thickness deviation by up to 40% and the average cutting forces by up to a 20%, which consequently eases the clamping system and reduces machine consumption. Finally, an experimental model to control the process quality based on monitoring the machine power consumption is proposed.

FEM simulation-based cutting parameters optimization in machining aluminum-silicon piston alloy ZL109 with PCD tool

The Johnson-Cook (JC) constitutive model was utilized to simulate the processing of Al-Si piston alloy ZL109 to obtain the optimal finishing parameters in polycrystalline diamond (PCD) tool, and studied the machining quality of ordinary-turning under the optimized cutting parameters. PCD is brittle, so it is difficult to manufacture orthogonal blades and is easy to break and high cost. We have adopted ordinary-turning quality to verify simulation results. Effect of cutting parameters on residual stress, cutting force and temperature was investigated through simulation, meanwhile, effect of cutting parameters on surface roughness and residual stress was analyzed by experiments. It indicates the verification method is practicable, and residual stress, cutting force and temperature are the smallest in simulation, and the experimental values of surface roughness and residual stress are also the smallest under the optimal cutting parameters, which can guide the cutting parameters selection to obtain higher machining quality.

Optimization of cutting parameters considering tool wear conditions in low-carbon manufacturing environment

Carbon emissions have drawn widely attention due to the worsening climate changes. In a machining process, a reasonable selection of cutting parameters can not only save the production cost and time, but also reduce the production carbon emissions. In addition, the wear conditions of different cutting tools also have great influence on cutting parameters selection as they could cause huge difference on carbon emissions. However, in traditional optimization methods of cutting parameters, the tool wear conditions are always ignored. Thus, in order to overcome this limitation, an optimization method of cutting parameters considering the tool wear conditions is developed. Firstly, the quantified relationships among cutting parameters, tool wear and production indexes (production carbon emissions, cost and time) are analysed. Then, a multi-objective cutting parameters optimization model is established based on the above production indexes to determine the optimal cutting parameters and tools. Thirdly, a modified NSGA-II algorithm is used to resolve the proposed model. Finally, a case study is designed to demonstrate the advantages and feasibility of the proposed approach. The results show that (i) the optimal cutting parameters change with the tool wear conditions; (ii) For the same type of cutting tools with different wear conditions, the optimal values of production carbon emissions, cost and time increase along with the raise of the tool wear conditions; (iii) For different available cutting tools with different tool wear conditions, it is necessary to apply a multi-objective optimization method to decide the optimal production carbon emissions, cost and time.

Using model based analytic cutting force prediction in CAM toolpath generation

Apart from approaches based on experience and expert knowledge in process design as well as the use of CAM software, simulation models have increasingly been used lately to analyse machining conditions in detail. Regarding process planning, the knowledge of resultant cutting forces is essential in order to identify critical points along the tool path. An analytical cutting model makes it possible to calculate the resultant forces along a complete tool path without the need for extensive testing and parameter identification beforehand. Never the less material data and friction parameters of the materials in use must be determined. Material and friction testing has been carried out for this purpose. The presented experimental data and simulation results show the potential of a force-based path generation in path planning and cutting parameter selection.

Statistical analysis of cutting forces and hole accuracy in reaming an Al–Si–Mg alloy (6351) with different copper contents

The main purpose of this work is to study the influence of copper content on Al–Si–Mg aluminum alloy’s (6351) machinability. This alloy’s machinability was evaluated through cutting tool torque and thrust force measurements, holes’ surface roughness, diameter, roundness, cylindricity and subsurface modifications during the reaming process. The Al–Si–Mg alloy samples were produced with five levels of copper (0.07%, 0.23%, 0.94%, 1.43% and 1.93%) and have a chemical composition of the other alloying elements practically constant within the recommended range by the Aluminum Association for this alloy. The main mechanical properties of the 6351 aluminum alloy samples were obtained by microstructural analysis and microhardness, hardness and tensile tests. Major amounts of copper resulted in a significant increase in microhardness, hardness and ultimate tensile strength of the aluminum alloy, while elongation after fracture decreased. A 23 factorial design was used to verify the effect of copper content and cutting parameters cutting speed and feed rate in machinability responses. Furthermore, it was developed an adjusted model to optimize the cutting parameters selection to machine the alloy with the best copper content for each response parameter of the reaming process. The results showed a significant influence of copper content in this aluminum alloy’s machinability. Lower values of torque and thrust forces, better surface finish, lower roundness and cylindricity deviation were obtained increasing copper content in Al–Si–Mg aluminum alloy (6351).

Cutting Parameter Selection for Efficient and Sustainable Repair of Holes Made in Hybrid Mg⁻Ti⁻Mg Component Stacks by Dry Drilling Operations.

Drilling is one of the most common machining operations in the aeronautic and aerospace industries. For assembling parts, a large number of holes are usually drilled into the parts so that they can be joined later by rivets. As these holes are subjected to fatigue cycles, they have to be checked regularly for maintenance or repair, since small cracks or damage in its contour can quickly cause breakage of the part, which can have dangerous consequences. This paper focuses on finding the best combinations of cutting parameters to perform repair and maintenance operations of holes in stacked hybrid magnesium–titanium–magnesium components in an efficient, timely, and sustainable (without lubricants or coolants) manner, under dry drilling conditions. For the machining trials, experiments were designed and completed. A product of a full factorial 23 and a block of two factors (3 × 2) was used with surface roughness as the response variable measured as the mean roughness average. Analysis of variance (ANOVA) was used to examine the results. A set of optimized tool and cutting conditions is presented for performing dry drilling repair operations.

An Integrated Smarter Cutting Parameter Selection System with a Case Study for Pocket Milling

This paper develops an integrated smart cutting parameter selection system for pocket milling. Currently, general CAD/CAM systems generate pocket milling tool paths according to user-designated cutting parameters typically including cutting depth, feed rate, and spindle speed. Pocket milling is often used for rough machining, and shorter cutting times are used to increase productivity. Although shorter machining times can be accomplished with deeper cutting, it may result in unstable cutting (chatter). Stability lobe diagrams (SLD) generated from cutting and tapping tests on selected machine tools describe the relationship between cut depth and spindle speed. However, despite offering useful information for selecting cutting parameters for chatter-free cutting, SLD has not been integrated with existed CAD/CAM systems. Therefore, this study integrates SLD in a CAD/CAM system to effectively determine the optimum cut depth and spindle speed for a theoretically chatter free pocket milling tool path. A case study is used to verify the proposed system integration.

Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation

The CNC machine is controlled by manipulating cutting parameters that could directly influence the process performance. Many optimization methods has been applied to obtain the optimal cutting parameters for the desired performance function. Nonetheless, the industry still uses the traditional technique to obtain those values. Lack of knowledge on optimization techniques is the main reason for this issue to be prolonged. Therefore, the simple yet easy to implement, Optimal Cutting Parameters Selection System is introduced to help the manufacturer to easily understand and determine the best optimal parameters for their turning operation. This new system consists of two stages which are modelling and optimization. In modelling of input-output and in-process parameters, the hybrid of Extreme Learning Machine and Particle Swarm Optimization is applied. This modelling technique tend to converge faster than other artificial intelligent technique and give accurate result. For the optimization stage, again the Particle Swarm Optimization is used to get the optimal cutting parameters based on the performance function preferred by the manufacturer. Overall, the system can reduce the gap between academic world and the industry by introducing a simple yet easy to implement optimization technique. This novel optimization technique can give accurate result besides being the fastest technique.

A graphical method for performance mapping of machines and milling tools

Optimal design of the machining setup in terms of installed machines, cutting tools and process parameters is of paramount importance for every manufacturing company. In most of the metal cutting companies, all choices related to machine eligibility and cutting parameters selection typically come from heuristic approaches and follow supplier indications or base on the skill of experienced machine operators. More advanced solutions, such as model-based and virtual approaches, are adopted less frequently mainly due to the lack of these techniques in grasping the underlying knowledge successfully. Aim of this work is to introduce a synthetic graphical representation of machining centers and cutting tools capabilities, to provide an accessible way to evaluate the feasibility and close-to-limit conditions of the cutting process. Taking inspiration from previous scientific works from the measurement engineering field, a set of 2D and 3D graphs are presented to map machine, tools and process capabilities, as well as their obtainable manufacturing performances and expectable tool life. This approach synthesizes the nominal data coming from different sources (catalogues, database, tool model geometries etc.) and the real cutting tools parameters used during the production phase. Some examples are provided to show the potential of this graphical evaluation in supporting process planning and decision-making and in formalizing the machining setup knowledge. Further developments are devoted to extend the method to other manufacturing processes, including hybrid processes. At the same time, an in-process data gathering software will be integrated for building a solid database that can be used by an autonomous multi-technological process selector, as well as by a pre-process condition advisor in an Industry 4.0 oriented way.

Swept surface-based approach to simulating surface topography in ball-end CNC milling

The parts, in automotive, aerospace, die/mold industry, which have extremely high demands on the quality and integrity of the surface, are usually milled by CNC machine tools. In order to obtain the desirable surface quality, it is an effective way to choose the appropriate cutting parameters before machining by simulating the surface topography formed in the milling process. To do so, this paper develops a model based on the swept surface of the cutting edge and N-buffer model for predicting the surface topography and studies the effect of various cutting parameters. In this developed model, the mathematical equation of the cutting edge is first given, and then based on the relative motion between the cutter and the workpiece, the swept surface of the cutting edge along the tool path is accurately analyzed and modeled from the perspective of kinematics, which is used to describe realistically the cutting interaction between the cutter and the workpiece. Subsequently, the milling process is simulated by an improved N-buffer model by means of the proposed accurate interpolation method for calculating the cusp height. This procedure presents the advantage of not requiring any numerical iteration or approximation to gain the cusp height of any point on workpiece. On basis of the model, the effect of the cutting parameters such as spindle speed, feedrate, inclination angle, path interval, and cutter runout is investigated. Finally, the real machining experiments are performed and compared with the predicted results. The simulated surface topography shows a good agreement with the experimental one. This demonstrates that the developed model can predict accurately the surface topography and also provide the great potential for the surface quality control and the cutting parameter selection in actual production.