Machining Power Research Articles

Comparison of machining forces, power consumption, and specific cutting energy in tools with grooved cutting edges for sustainable manufacturing

ABSTRACT The ease at which a material is cut or formed to the required shape is defined as machinability. Higher machinability, superior productivity, and sustainability are the three key parameters used to evaluate machining performance. The tools designed to support these performance parameters are intended to consume less power, generate lower forces, produce surfaces with lesser roughness, and provide a higher tool life. Hence, the purpose of the investigation is to compare the efficiency of milling inserts with grooves (serrations) on the cutting edges (also called porcupine edges) in machining AISI 304 austenitic stainless steel and AISI 4140 low-alloy, medium-carbon steel materials. The metal cutting test results show that the milling inserts with grooved cutting edges consumed 8% to 17% less power and specific cutting energy, and exhibited 7% to 16% lower resultant force in AISI 304 steel. Similarly, 9% to 15% less power consumption, specific cutting energy, and 10% to 14% lower resultant force than the tools with straight cutting edges were also observed in AISI 4140 steel. The reduction in cutting power, specific cutting energy, and forces is credited to the decreased contact length between the tool edge and workpiece, providing lesser resistance in the inserts with grooved cutting edges.

Energy-aware production lot-sizing and parallel machine scheduling with the product-specific machining tools and power requirements

This study addresses a multi-product lot-sizing and scheduling problem with sequence-dependent setup times, considering that the machining operations cause energy consumption. The production facility comprises identical parallel machines under which the production of each product requires a certain set of tools. The energy requirement of production depends on the product-specific machining tools. The problem deals with determining the minimum cost lot-sizing and scheduling plan considering the energy capacity of the production facility. We formulate the problem as a mixed integer linear programming model by introducing energy consumption-related costs and constraints. We perform a case study on CNC milling and turning workshops. Further, we propose an heuristic approach combining a decomposition-based Simulated Annealing heuristic and Fix&Optimise algorithms to handle larger-sized problem instances. The computational performance of the proposed heuristic approach is evaluated against the proposed mixed integer linear programming model on a numerical study. Our numerical experiments reveal that the proposed heuristic approach is capable of providing cost-efficient solutions without compromising time efficiency.

Surface roughness prediction through GAN-synthesized power signal as a process signature

Predicting machined surface roughness is critical for estimating a part’s performance characteristics such as susceptibility to fatigue and corrosion. Prior studies have indicated that power consumed at the tool-chip interface may represent an indicator for the surface integrity of the machining process. However, no quantitative association has been reported between the machining power and surface roughness due to a lack of data to develop predictive models. This paper presents a data synthesis method to address this gap. Specifically, a conditional generative adversarial network (CGAN) is developed to synthesize power signals associated with varying process parameter combinations. The quality of the synthesized signals is evaluated against experimentally measured power signals by examining the consistency in: 1) the spatial pattern of the signals induced by the cutting process as shown in the frequency domain, and 2) the temporal pattern as shown in the clustering of the synthesized and measured signals corresponding to the same parameter combination. The synthesized signals are then used to augment the measured signals and develop a convolutional neural network (CNN) for predicting the machined surface roughness. Experiments performed using H13 tool steel have shown that data augmentation by CGAN has effectively reduced the error of the surface roughness prediction from 58 %, when no synthetic data is used for CNN training, to 9.1 % when 250 synthetic samples are used. The results demonstrate the effectiveness of CGAN as a data augmentation method and CNN for mapping machining power to surface roughness.

Dry and MQL Milling of AISI 1045 Steel with Vegetable and Mineral-Based Fluids

The use of mineral-based cutting fluids in machining has the drawback of affecting the environment and industries are under pressures to reduce its use in favor of cleaner productions. In this regard, the vegetal-based cutting fluids can be a superior alternative, provided they improve the technical outcomes. In the milling process, dry cutting is commonly performed, however, the application of cutting fluids using the minimum quantity of lubricant (MQL) method has proven advantageous when compared with dry machining. Furthermore, in the midst of the availability of several cutting fluids in the market, the testing of their individual performance can ascertain their potential and effectiveness for a particular application. This study examined the performances of two vegetable-based and one mineral-based oils applied by the MQL method, followed by their comparison with dry cutting amid end milling of AISI 1045 steel with TiAlN-coated cemented carbide inserts. The cutting temperature, machining forces, power consumption, workpiece surface roughness, tool life, and tool wear mechanisms were chosen as the output parameters. The experiments were conducted using two cutting speeds (150 and 200 m/min) and feed rates (0.07 and 0.14 mm/tooth), and constant axial (1 mm) and radial depths of the cut (25 mm). The temperature was measured using a K-type thermocouple soldered to the part and an infrared camera. The power was monitored with a Fluke 435 energy analyzer, and the machining force components with a Kistler dynamometer. The worn inserts were inspected under a scanning electron microscope (SEM) to analyze the tool wear mechanism. The MQL-assisted application of the cutting fluids notably lowered the cutting temperature and increased the tools’ lives. However, the cutting fluids did not reflect any significant effect on the machining force, power consumption, or surface roughness. Among all the analyzed cutting conditions, the abrasive wear mechanism dominated, damaging the cutting edges, flank, and rake surfaces of the cutting tools. In addition, adhesive and diffusion wear mechanisms were also observed.

A Multi-Objective Optimization Method for Flexible Job Shop Scheduling Considering Cutting-Tool Degradation with Energy-Saving Measures

Traditional energy-saving optimization of shop scheduling often separates the coupling relationship between a single machine and the shop system, which not only limits the potential of energy-saving but also leads to a large deviation between the optimized result and the actual application. In practice, cutting-tool degradation during operation is inevitable, which will not only lead to the increase in actual machining power but also the resulting tool change operation will disrupt the rhythm of production scheduling. Therefore, to make the energy consumption calculation in scheduling optimization more consistent with the actual machining conditions and reduce the impact of tool degradation on the manufacturing shop, this paper constructs an integrated optimization model including a flexible job shop scheduling problem (FJSP), machining power prediction, tool life prediction and energy-saving strategy. First, an exponential function is formulated using actual cutting experiment data under certain machining conditions to express cutting-tool degradation. Utilizing this function, a reasonable cutting-tool change schedule is obtained. A hybrid energy-saving strategy that combines a cutting-tool change with machine tool turn-on/off schedules to reduce the difference between the simulated and actual machining power while optimizing the energy savings is then proposed. Second, a multi-objective optimization model was established to reduce the makespan, total machine tool load, number of times machine tools are turned on/off and cutting tools are changed, and the total energy consumption of the workshop and the fast and elitist multi-objective genetic algorithm (NSGA-II) is used to solve the model. Finally, combined with the workshop production cost evaluation indicator, a practical FJSP example is presented to demonstrate the proposed optimization model. The prediction accuracy of the machining power is more than 93%. The hybrid energy-saving strategy can further reduce the energy consumption of the workshop by 4.44% and the production cost by 2.44% on the basis of saving 93.5% of non-processing energy consumption by the machine on/off energy-saving strategy.

Analysis of Available Data and Estimation of Energy Supply of Mechanical Processing

The issue of energy efficiency of machining processes has been the focus of attention for the last 50 years. This is due to the fact that in comparison with other industries, metallurgy and mechanical engineering are characterized by a high level of energy intensity of products. Analysis of available in the literature indicators of energy costs of processing processes and determination of consistent data from these indicators. Establishing an analytical relationship between the grinding energy and the energy required for melting the finishing material. It is determined that the cutting energy of the material during chip for­mation is close to the energy required for melting the metal, and the excess amount of spent grinding energy is spent on friction between the chips and the grinding wheel. It is shown that in the literature there are data on the energy consumption of different treatments: turning - 2 kJ/cm3, milling - 9 kJ/cm3, grinding - 60 kJ/cm3, electrospark treatment - 3000 kJ/cm3. At the same time, the specific energy consumption of steel grinding is 60 kJ/cm3. And the specific heat of fusion of steel is 0.64 kJ/cm3. As a result, 100 times more heat is pumped into the steel during grinding than is needed to melt it. That is, there is a contradiction. To find ways to resolve this contradiction, it is more accurate to estimate the specific energy consumption of diamond-abrasive machining of superhard materials through additional consideration, in addition to productivity and effective machining power, wear of the working layer of the wheel.

Development of a Laser Scanning Machining System Supporting On-the-Fly Machining and Laser Power Follow-Up Adjustment.

In this study, a laser scanning machining system supporting on-the-fly machining and laser power follow-up adjustment was developed to address the increasing demands for high-speed, wide-area, and high-quality laser scanning machining. The developed laser scanning machining system is based on the two-master and multi-slave architecture with synchronization mechanism, and realizes the integrated and synchronous collaborative control of the motion stage or robot, the galvanometer scanner, and the laser over standard industrial ethernet networks. The galvanometer scanner can be connected to the industrial ethernet topology as a node, via the self-developed galvanometer scanner control gateway module, and a “one-transmission and multiple-conversion” approach is proposed to ensure real-time ability and synchronization. The proposal of a laser power follow-up adjustment approach could realize real-time synchronous modulation of the laser power, along with the motion of the galvanometer scanner, which is conducive to ensuring the machining quality. In addition, machining software was developed to realize timesaving and high-quality laser scanning machining. The feasibility and practicability of this laser scanning machining system were verified using specific cases. Results showed that the proposed system overcame the limitation of working field size and isolation between the galvanometer scanner controller with the stage motion controller, and achieved high-speed and efficient laser scanning machining for both large-area consecutively and discontinuously arrayed patterns. Moreover, the integration of laser power follow-up adjustment into the system was conducive to ensuring welding quality and inhibiting welding defects. The proposed system paves the way for high-speed, wide-area, and high-quality laser scanning machining and provides technical convenience and cost advantages for customized laser-processing applications, exhibiting great research value and application potential in the field of material processing engineering.

Analysis of Surface Grinding of Thermoplastics Specimens with Inline Measurements

This paper analyzes the surface grinding of unfilled and glass-filled polyamides. The process is performed by varying the workpiece velocities to evaluate applied practical applications in the industry while being energy efficient. During the machining, the temperatures, normal forces, tangential forces, and spindle power were collected, and the surface quality was evaluated by a scanning electron microscope (SEM), helping to determine material removal mechanisms and study their behavior under grinding. One of the primary outcomes of the present research was that, different from most metallic and ceramic materials, polyamides benefited from the material removal rate increase. We had higher quality material removed efficiently. Also, the specific energy of both materials converged to previously demonstrated values, showing once again that it is highly dependent on the matrix. Moreover, the time-dependent mechanical properties of the material during processing were identified. The fast application of the force at high speed gave less time to respond to the mechanical strain, determining an improvement in the surface quality of the samples. Consequently, the surface quality of the final product improved with a speed increase, leading to low roughness values.

Investigation on sustainable machining characteristics of tools with serrated cutting edges in face milling of AISI 304 Stainless Steel

Stainless steel is a ferrous-based difficult-to-cut material that is widely used in aerospace, automobile, food processing, and chemical industries due to its superior strength, high fracture toughness, and exceptional corrosion resistance. Machining of these materials requires a rigid machine setup with high power capabilities and tools with outstanding properties. The high strength of the material also consumes high machining power and specific cutting energy which is non-eco-friendly. Besides, the low thermal conductivity and work hardening behavior compounds the complexity which prevents the tools from operating at higher parameters. However, tools with serrated cutting edges can help in overcoming the machining-related challenges besides providing a sustainable solution by reducing energy consumption. Hence, the objective of the study is to evaluate the machinability of AISI 304 stainless steel material (SS304) using face milling inserts with linear (conventional) and serrated cutting edges with focus on key sustainable machining characteristics like machining power, specific cutting energy, and forces. Tools with serrated cutting edges were manufactured for the study by the wire-cut Electric Discharge Machining (wire-EDM) technique. The study reveals that an 8 to 17% reduction in machining power, specific cutting energy, and forces can be obtained by using tools with serrated cutting edge.

Study of machining strategies for CNC milling of cavities on Ultra High Molecular Weight Polyethylene

Cavity milling is one of the most used machining operations by the industry in different types of materials; among them, thermoplastics. Several cutting strategies are available that influence different parameters of the manufacturing process and the surface quality of the final part. In this research, the spiral, zigzag, and one-way performance cutting strategies for CNC cavity milling on Ultra High Molecular Weight Polyethylene (UHMW) were compared using a methodology and measuring machining time, power, energy, and surface roughness. The results allowed a performance strategy ranking according to the measured parameters. The developed methodology can be applied to other materials, geometries, and cutting strategies.

Predictive Modeling for Machining Power Based on Multi-source Transfer Learning in Metal Cutting

Energy efficiency has become crucial in the metal cutting industry. Machining power has therefore become an important metric because it directly affects the energy consumed during the operation of a machine tool. Attempts to predict machining power using machine learning have relied on the training datasets processed from actual machining data to derive the numerical relationship between process parameters and machining power. However, real fields hardly provide training datasets because of the difficulties in data collection; consequently, traditional learning approaches are ineffective in such data-scarce or -absent environment. This paper proposes a transfer learning approach for the predictive modeling of machining power. The proposed approach creates machining power prediction models by transferring the knowledge acquired from prior machining to the target machining context where machining power data are absent. The proposed approach performs domain adaptation by adding workpiece material properties to the original feature space for accommodating different machining power patterns dependent on the types of workpiece materials. A case study demonstrates that the training datasets obtained from the fabrication of steel and aluminum materials can be successfully used to create the power-predictive models that anticipate machining power for titanium material.

Effect of mass transfer channels on flexural strength of C/SiC composites fabricated by femtosecond laser assisted CVI method with optimized laser power

In this study, femtosecond laser assisted-chemical vapor infiltration (LA-CVI) was employed to produce C/SiC composites with 1, 3, and 5 rows of mass transfer channels. The effect of laser machining power on the quality of produced holes was investigated. The results showed that the increase in power yielded complete hole structures. The as-obtained C/SiC composites with different mass transfer channels displayed higher densification degrees with flexural strengths reaching 546 ± 15 MPa for row mass transfer channel of 3. The strengthening mechanism of the composites was linked to the increase in densification and formation of “dense band” during LA-CVI process. Multiphysics finite element simulations of the dense band and density gradient of LA-CVI C/SiC composites revealed C/SiC composites with improved densification and lower porosity due to the formation of “dense band” during LA-CVI process. In sum, LA-CVI method is promising for future preparation of ceramic matrix composites with high densities.

Database for Optimal Selection of Cutting Conditions, Forces and Power Consumption in Machining Processes (Dept.M)

Selecting the cutting data (cutting speed, feed, depth of cut) and the proper cutting tools play a significant role in achieving minimum production time, consistent quality and in controlling the overall cost of manufacturing. However, searching for a proper tool/insert-job combination calls for a huge amount of data and an extensive knowledge base and may take a long time and effort if it is done manually. In this paper, a software has been designed and established using Microsoft Visual Basic.Net and SQL server to store all different types of tool grades with their available cutting data, inserts, and tool holders for all materials and some machining operations (turning, drilling and milling) in a database. The software is capable of selecting the suitable cutting data, tool grade, insert and tool holders for each operation in two cases. The first case uses the maximum cutting speed of the tool grade (minimum tool life) while the second case uses the minimum cutting speed (maximum tool life). In the two cases, the software considers the constraints of power, efficiency, and maximum spindle revolution of the machine as well as the required surface roughness of the workpiece. The software is capable of calculating the machining time, intermittent time, force, torque, power, spindle revolution and total production time and cost for each operation. In addition, the workpiece material, required operations, cutting data, force, torque, power, the suitable tool grade and its manufacturer, insert and tool holder are displayed in a single report as a process sheet. Another report can be used to show the total production time and the total cost. A case study has been done to test the proposed software and it was found that about 34% of the total production time could be saved by using the maximum cutting speed, compared with the minimum cutting speed. On the other hand, about 28% of the total production cost could be saved by using the minimum cutting speed, compared with the minimum cutting speed.

High speed and low roughness micromachining of silicon carbide by plasma etching aided femtosecond laser processing

Silicon carbide has a high hardness and corrosion resistance. Accordingly, high rate machining of this material is a big challenge to obtain good surface quality. In this work, plasma etching aided femtosecond laser micromachining was proposed to address this issue. A series of square and circular blind holes for forming SiC pressure sensor diaphragms were obtained by using a 50fs, 800 nm, 1000Hz Ti:Sapphire laser. Using the control variable method, the effects of laser machining power, scan line spacing and scanning rate on surface topography of the structure were investigated. As demonstrated by experimental results, surface roughness of the structure increases with the laser machining power within a certain range. While the scanning rate has negligible effects on surface quality of the structure. The maximum surface roughness after femtosecond laser micromachining is 2.867 μm measured by laser scanning confocal microscope, and this value is reduced to 2.143 μm after inductively coupled plasma etching. In addition, a Raman spectroscopy shows that this composite micromachining method can eliminate accumulation of amorphous carbon on SiC surface caused by the femtosecond laser processing. The reduction of surface roughness and removal of amorphous carbon can effectively decrease probability of crack failure of the sensor diaphragm and increase its deformation capability, thereby ensuring sensitivity of the sensor. The composite micromachining method fully combines the advantages of high efficiency of femtosecond laser machining and high surface quality of plasma etching, making it has high potential to be applied in precision machining of silicon carbide and fabrication of SiC sensors.

Automated Unsupervised 3D Tool-Path Generation Using Stacked 2D Image Processing Technique

Tool-path, feed-rate, and depth-of-cut of a tool determine the machining time, tool wear, power consumption, and realization costs. Before the commissioning and production, a preliminary phase of failure-mode identification and effect analysis allows for selecting the optimal machining parameters for cutting, which, in turn, reduces machinery faults, production errors and, ultimately, decreases costs. For this, scalable high-precision path generation algorithms requiring a low amount of computation might be advisable. The present work provides such a simplified scalable computationally low-intensive technique for tool-path generation. From a three dimensional (3D) digital model, the presented algorithm extracts multiple two dimensional (2D) layers. Depending on the required resolution, each layer is converted to a spatial image, and an algebraic analytic closed-form solution provides a geometrical tool path in Cartesian coordinates. The produced tool paths are stacked after processing all object layers. Finally, the generated tool path is translated into a machine code using a G-code generator algorithm. The introduced technique was implemented and simulated using MATLAB® pseudocode with a G-code interpreter and a simulator. The results showed that the proposed technique produced an automated unsupervised reliable tool-path-generator algorithm and reduced tool wear and costs, by allowing the selection of the tool depth-of-cut as an input.

Optimization Parameters of Milling Process of Mould Material for Decreasing Machining Power and Surface Roughness Criteria

Optimization Parameters of Milling Process of Mould Material for Decreasing Machining Power and Surface Roughness Criteria

Influence of milling direction in the machinability of Inconel 718 with submicron grain cemented carbide tools

The nickel-based alloys have a growing demand in many fields due to their outstanding properties at high temperatures. These properties lead to relatively low machinability, one of the main obstacles to its more extensive use. Improvements in cutting tool quality are one of the key points to overcome the challenges. The decrease to a submicron scale of the grains of cemented carbide tools is one of the alternatives to improve the machinability of the nickel-based superalloys. In this paper, two different grades of submicron grains of uncoated cemented carbide tools, TMG30 (10% Co, S30-40) and CTS18D (9% Co, S20-40), were evaluated in the end milling process of Inconel 718, through a 24 factorial design of experiments having as parameters the cutting speed, feed rate, machining direction (up and down milling), and tool grade. The tool life, machining power, and surface roughness were used as machinability evaluators. It was found that the machining direction, cutting speed, and feed rate had a significant influence on the machinability output variables, with the machining direction being the most significant one. The differences in the two tool grades were too small to be statistically significant. Simulations using the finite element method of the effective plastic strain, validated by the measurement of experimental machining power, showed that the up milling presented around 14% more plastic deformation than the down milling, which combined with the work-hardenability of the Inconel 718 explains the shorter tool life of this condition.

Use of a virtual milling system to generate power-aware tool paths for 2.5-dimensional pocket machining

Traditional (direction-parallel and contour-parallel) and non-traditional (trochoidal) tool paths are generated by specialized geometric algorithms based on the pocket shape and various parameters. However, the tool paths generated with those methods do not usually consider the required machining power. In this work, a method for generating power-aware tool paths is presented, which uses the power consumption estimation for the calculation of the tool path. A virtual milling system was developed to integrate with the tool path generation algorithm in order to obtain tool paths with precise power requirement control. The virtual milling system and the tests used to calibrate it are described within this article, as well as the proposed tool path generation algorithm. Results from machining a test pocket are presented, including the real and the estimated power requirements. Those results were compared with a contour-parallel tool path strategy, which has a shorter machining time but has higher in-process power consumption.

An experimental study on cutting forces when machining a CoCrMo alloy

An important property of the metallic biomaterials used in medical applications is their machinability. A good knowledge of this property is a major advantage in terms of the changes that have been made lately in the medical practice due to the shift to value-based healthcare solutions that have led to increased cost-cutting requirements while keeping the same qualitative requirements for this type of products. Having a good biocompatibility and mechanical properties, the Co-Cr alloys are very often used in medical applications such as dentistry and orthopaedics. When talking about machinability, one of the most important technological parameters in machining process is represented by the cutting forces which are used for determining the necessary machining power and dimensioning the cutting tools. The aim of this research paper is to present the experiments conducted in order to determine a cutting force prediction mathematical model when turning a biocompatible CoCrMo alloy by using TiAlN PVD coated inserts.

Research on the EDM Technology for Micro-holes at Complex Spatial Locations

For the demands on machining micro-holes at complex spatial location, several key technical problems are conquered such as micro-Electron Discharge Machining (micro-EDM) power supply system’s development, the host structure’s design and machining process technical. Through developing low-voltage power supply circuit, high-voltage circuit, micro and precision machining circuit and clearance detection system, the narrow pulse and high frequency six-axis EDM machining power supply system is developed to meet the demands on micro-hole discharging machining. With the method of combining the CAD structure design, CAE simulation analysis, modal test, ODS (Operational Deflection Shapes) test and theoretical analysis, the host construction and key axes of the machine tool are optimized to meet the position demands of the micro-holes. Through developing the special deionized water filtration system to make sure that the machining process is stable enough. To verify the machining equipment and processing technical developed in this paper through developing the micro-hole’s processing flow and test on the real machine tool. As shown in the final test results: the efficient micro-EDM machining pulse power supply system, machine tool host system, deionized filtration system and processing method developed in this paper meet the demands on machining micro-holes at complex spatial locations.