Reduce Machining Time Research Articles

Effect of manufacturing new U-shaped electrode on die sinking EDM process performance

Traditional electrical discharge machining (EDM) electrodes have several limitations such as long machining time, low material removal rate MRR, hazard emissions and high energy consumption. To overcome these limitations, new electrodes (plane and frame U-shaped) were manufactured considering the electrode manufacturing classifications, such as electrode flushing mechanism, electrode cross-sectional area and electrode bottom machining area. Experiments were performed on a die-sinking EDM machine using copper electrodes and aluminum alloy workpieces. The results were compared with those obtained using the conventional electrode. The newly manufactured electrodes significantly improved the EDM process performance, reducing the machining time by more than 50%, which implies a higher MRR. The reduced machining time also led to lower costs, emissions and energy consumption for efficient EDM processes. Additionally, surface roughness measurements were performed to compare the surface quality of the machined areas using different pulse-code generator systems and electrodes. The results showed an improvement in the machined surface quality when using the new electrodes in comparison with the conventional electrode when using a roughing pulse code generator system.

Hermite Quartic Splines for Smoothing and Sampling a Roughing Curvilinear Spiral Toolpath

From an industrial point of view, the milling of 2.5D cavities is a frequent operation, consuming time and presenting optimization potential, especially through a judicious choice of the tool trajectory. Among the different types of trajectories, some have a general spiral-like aspect and can potentially offer a reduced machining time. They are called curvilinear trajectories and are obtained by interpolation between structure curves, which are the numerical solutions of a partial differential equation. In this case, the machine tool will connect points, and the trajectory will be made up of small segments. While these trajectories exhibit all the necessary qualities on a macroscopic level for rapid tool movement, the tangential discontinuities at a microscopic scale, inherent in the discretization, significantly increase the machining time. This article proposes a method to reparameterize the structure curves of the curvilinear spiral with a set of C2 connected Hermit quartic spline patches. This creates a smooth toolpath that can be machined at an average feedrate closer to the programmed one and will, de facto, reduce the machining time. This article shows that the proposed method increases on two representative geometries of cavities and toolpath quality indicators, and reduces the milling time from 10% to 18% as compared to the PDE curvilinear spiral generation method proposed by Bieterman and Sandström. In addition, the proposed method is suitable for any non-convex pocket, with or without island(s).

Influence of sequential machining process on the surface topography of dies

Near net-shape manufacturing of components is a trend that aims to significantly reduce machining times and material waste, trading the machining efficiency for smaller lead times. Cold forging of gear-shaped components is one such operation that generates a component with superior geometrical accuracy and precision, however, the machined cavity is often subject to high mechanical loads and fatigue failures. The usual manufacturing chain involves hard machining, die-sinking electro-discharge machining, and mechanical polishing with abrasive media. Without proper knowledge of the resulting surface roughness and morphology, the tribological aspects of the surface can be neglected. Therefore, this study aims to investigate the manufacturing chain of gear-shaped cavities in terms of surface roughness, surface morphology, and surface residual stresses generated after die-sinking electro-discharge machining (roughing and finishing regimes), as well as mechanical polishing. The surface of the gear tooth was evaluated in three distinct regions: flank, top, and bottom. The results showcased that, even though the surface roughness/texture of the different regions of interest illustrates similar magnitudes, the surface morphology presented completely adverse features, highlighting the importance of a thorough analysis of the machined surface. Residual stress analysis indicated that the mechanical polishing operation was not enough to remove the high-magnitude tensile residual stresses.

Integrated optimisation of multi-pass cutting parameters and tool path with hierarchical reinforcement learning towards green manufacturing

Five-axis machining, especially flank milling, is popular in machining thin-walled freeform surface parts with high energy consumption. Reducing the machining energy consumption is paramount for advancing green manufacturing. Therefore, this paper proposes an energy-efficient integration optimisation of cutting parameters and tool path with hierarchical reinforcement learning (HRL). Firstly, a novel multi-pass machining energy consumption model is developed with cutting and path parameters, based on which the integrated optimisation problem is modelled considering a dynamic workpiece deformation constraint. Secondly, HRL with a Soft Actor Critic agent (HSAC) decouples the model into two Markov Decision Processes at different timescales. The higher-layer plans cutting parameters for each pass on a macro timescale, while the micro-timescale lower-layer performs multiple tool path expansions with the planned cutting parameters, and provides feedback to the higher layer. By hierarchical optimisation and non-hierarchical interaction, the model is efficiently solved. Moreover, curriculum transfer learning is applied to expedite task completion of the lower layer, enhancing interaction efficiency between the two layers. Experiments show that, compared with two benchmarks, the proposed method improves machining energy consumption by 35.02 % and 30.92 %, and reduces machining time by 38.57 % and 27.17 %, providing a promising paradigm of green practices for thin-walled freeform parts and the broader manufacturing industry.

Regulating cutting fluid parameters for optimal energy and economic performance: Methods for efficient and Low-Energy electrical machining

Scholars today have been dedicated to researching economically viable and efficient methods to enhance the energy efficiency of manufacturing, aiming for sustainable production. Wire Electrical Discharge Machining (WEDM) holds a significant position in current mechanical processing due to its unique operational principle, which results in notably higher energy consumption compared to conventional processing methods. Various factors influencing machining performance are complex, among which the condition of the cutting fluid plays a crucial role. Effectively controlling the parameters of the cutting fluid to improve its discharge state during machining is vital for enhancing energy and economic efficiency. Specifically, this study analyzes the characteristics of the WEDM process, models its resource and energy consumption, and predicts its economy. A method for optimizing control of cutting fluid parameters is proposed. By correlating different cutting fluid parameters with machining performance using the selected machine tool, and recommending combinations of cutting fluid parameters that ensure quality while improving the material removal rate, the study aims to achieve optimal energy efficiency and minimal cost. The results show that the proposed models for energy efficiency and economic prediction have an average accuracy of 96.64 %. An optimized machining scheme selected reduces machining time and energy consumption by approximately 1255.6 s (about 19.14 %) and 2240112.92 J (about 17.48 %), respectively, and saves about ¥ 6.084 (approximately 14.90 %) in machining costs. Additionally, the energy consumption of the pulse system’s proportion of total energy use increased by 1.63 %. The research demonstrates a general method for specifying combinations of cutting fluid parameters aimed at optimal energy efficiency and economy during the process optimization design stage of machining, providing a reference for improving the energy efficiency of wire cutting operations.

Enhancing optimization and reducing machining time of freeform shapes through modeling, simulation, and Taguchi design of experiments with artificial neural networks

Enhancing optimization and reducing machining time of freeform shapes through modeling, simulation, and Taguchi design of experiments with artificial neural networks

Spiral complete coverage path planning based on conformal slit mapping in multi-connected domains

The generation of smoother and shorter spiral complete coverage paths in multi-connected domains is a crucial research topic in path planning for robotic cavity machining and other related fields. Traditional methods for spiral path planning in multi-connected domains typically incorporate a subregion division procedure that leads to excessive subregion bridging, requiring longer, more sharply turning, and unevenly spaced spirals to achieve complete coverage. To address this issue, this paper proposes a novel spiral complete coverage path planning method using conformal slit mapping. It takes advantage of the fact that conformal slit mapping can transform multi-connected domains into regular disks or annuluses without the need for subregion division. Firstly, a slit mapping calculation technique is proposed for segmented cubic spline boundaries with corners. Secondly, a spiral path spacing control method is developed based on the maximum inscribed circle radius between adjacent conformal slit mapping iso-parameters. Thirdly, the spiral coverage path is derived by offsetting iso-parameters. Numerical experiments indicate that our method shares a comparable order-of-magnitude in computation time with the traditional PDE-based spiral complete coverage path method, but it excels in optimizing total path length, smoothness, and spacing consistency. Finally, we performed experiments on cavity milling and dry runs to compare the new method with the traditional PDE-based method in terms of machining duration and steering impact, respectively. The comparison reveals that, with both algorithms achieving complete coverage, the new algorithm reduces machining time and steering impact by 12.34% and 22.78%, respectively, compared with the traditional PDE-based method.

Special Issue on Abrasive Technology for High-Precision and High-Efficiency Machining of High-Performance Materials

The demand for high-precision and high-efficient machining of high-performance materials and components has increased in various industries such as optical, automotive, communication, life sciences, and medical sciences. Certain difficult-to-machine materials can be reliably machined using deterministic precision cutting processes. However, hard and brittle materials such as ceramics, carbides, hardened steel molds, glassy materials, or semiconductor materials, require precision abrasive technologies with super abrasives like diamond, cBN, or new tool materials for machining. However, machining high-precision components and their molds/dies using abrasive processes is considerably more difficult due to their complex and non-deterministic nature and textured surfaces. Furthermore, high-energy processes such as laser technology can assist abrasive technologies in ensuring higher precision and efficiency. Precision grinding and polishing processes are primarily used to generate high-quality and functional components, typically made of difficult-to-machine materials. Thus, the surface quality achievable by precision grinding and polishing processes becomes more important for reducing machining time and costs. This special issue features 10 papers on the most recent advances in precision abrasive technologies. These papers cover the following topics: - Ultrasonic grinding of micro holes using cemented WC tools - Drilling holes in CFRP aircraft using cBN electroplated ball end mill - In situ evaluation of drill wear using tool images - Grinding belt based on modified information entropy - Radial directional vibration-assisted grinding of Al2O3 ceramics - Chatter vibration suppression using fixed superabrasive polishing stone - Free abrasive finishing of internal channels with different cross-sectional geometries - High-quality machining of cemented carbide using PCD ball end mills - Fixed-abrasive machining with magnetic brush for Ti-6Al-4V ELI alloy - High-efficient polishing of polymer surfaces using catalyst-referred etching This issue will provide an understanding of the recent developments in abrasive technologies, leading to further research. We deeply appreciate the careful work of all the authors, and we thank the reviewers for their incisive efforts.

On-Machine Measurement as a Factor Affecting the Sustainability of the Machining Process

On-Machine Measurement as a Factor Affecting the Sustainability of the Machining Process

Повышение адекватности моделей электромеханотронных систем

Development of electromechanotronic systems is connected with modeling and calculation of processes of various kinds. Electromagnetic, mechanical and thermal processes are analyzed, control, regulation, protection, monitoring and diagnostics problems are solved. Systems may contain a large number of power semiconductor elements. Short-term and multi-hour processes are calculated. Taking into account mutual connections of processes of various kinds in calculations of complex systems requires inadmissibly large expenditures of machine time. The problems are solved under more or less significant assumptions, which leads to a decrease in the adequacy of the results. The methods of systems calculation are proposed, which allow to reduce machine time consumption and to use more detailed description of installations and more accurate computer models. Possibilities of calculation of processes by «smooth components» of variables, and also application of dual models, in which speed and accuracy are combined, are considered. As an example, a system with an active cascade frequency converter containing 780 transistors and diodes, parasitic inductances and snubber circuits, and a vector-controlled induction motor with ladder circuits accounting for current displacement is analyzed. The model takes into account energy losses in the converter and in the motor, as well as the distribution of losses by components. It also takes into account emergency operation modes associated with disconnection of a part of low-voltage units of the cascade converter and maintenance of power quality at its input and output by symmetrization of the main current components and suppression of parasitic components.

Analysis of surface roughness, temperature, short aging, and residual notched and bearing strengths in supported drilling of thin GFRP composites

This study aimed to analysis open‐hole tensile strength, pin-bearing strength, cutting temperature, and surface roughness of drilled thin glass fiber reinforced polymer composites. The specimens were clamped from both sides during drilling operations. The cutting temperature was measured using high resolution infrared camera. Surface roughness was evaluated by seven parameters to identify the most suitable ones. Multiple regression models were developed to predict critical thrust force (CTF), critical feed, and cutting temperature in drilling operations. The measurements indicate that the cutting temperature is higher than the matrix's glass transition temperature and hence, the surface roughness paramaters Ra, Rq and Ry are increased at the maximum cutting conditions by 57%, 51%, and 36% respectively. In contrast, the bearing strength improved by 13%. This peculiar phenomenon was deeply analyzed with the aid of short ageing effects on the post curing, which increase the crosslinking density within the composites and improve the matrix's capacity to efficiently transmit the loads to the reinforcing fibers. For the first time, the optimum cutting conditions were determined based on the optimum short ageing parameters (temperature and time). Supported drilling increases CTF by 23%, which can reduce machining time and manufacturing costs with fewer delamination concerns.

Increasing tool life and machining performance by dynamic spindle speed control along toolpaths for milling complex shape parts

This paper focuses on dynamic spindle speed and feed rate control to increase tool life and reduce machining time during milling of complex shaped parts. Machining of complex shaped surfaces is one of the most demanding machining applications, especially if the workpiece is made of difficult-to-cut material. Tool life needs to be considered during selection of cutting conditions. Both feed rate and cutting speed also have a direct effect on the resulting roughness and accuracy. When machining with tools with a circular cutting edge, such as ball or toroidal mills, the real cutting diameter of the tool changes continuously and as a result the cutting speed along the toolpath also changes continuously. Therefore a new method to compute the required spindle speed and feed rate was proposed based on the implemented kinematic parameters of a real spindle controller. In addition, a method to calculate the effective cutting radius of the tool based on the actual height of the remaining material was proposed to achieve a constant cutting speed through dynamic spindle speed control. The benefits of using the new optimization method were verified when machining a duplex stainless steel (1.4462) part. This test confirmed that the new optimization method results in a significant increase in tool life as well as a significant saving of machining time, while achieving the desired surface quality.

New Calculation Software for Analytic Support Structure Optimization in Metal Additive Manufacturing

Powder based additive manufacturing systems often require support structures for overhanging geometries and thermal dissipation. On the one hand, the support material should be reduced to a minimum. On the other hand, the stiffness of the structures can be used as a fixture for post-processing. The contribution presents a unique analytic model to determine the stresses occurring in the support structures during post-processing. FEM simulations with different support types are carried out to validate the new calculation model. The results of this analysis subsequently serve as basis for dimensioning the support elements of complex and large parts. By specifying a machining process, it is possible to determine the required dimensions of the support structure (e.g. block, rod, or cross). The aim of this optimization process is to reduce machining time, material consumption and post-processing costs. The results of this contribution and the new software help to implement direct machining into industrial 3D printing processes.

Review on temperature monitoring system for welding application – A case study on thermocouple array

Process Monitoring System is implemented in machining process to get the quality products, increase the precision, reduce defects, reduce machining time. The process monitoring system, involves in monitoring many parameters such as temperature, force, pressure, voltage, vibration etc., these parameters are implemented in machining process like lathe operation, milling operation, drilling operation, boring, welding etc. On a traditional way of process monitoring system, there are two techniques namely sensor-based technique and image processing technique. In sensor-based techniques different types of sensors has been used for monitoring such as thermocouple, thermistor, RTD, pressure sensor, accelerometer, vibration sensor, load cell, strain gauge etc., these sensors have been calibrated with a microcontroller for data analysis. Image Processing technique involves in capturing the radiation of the machined/machining product by using Infrared camera, ultraviolet camera, photoelectric thermoscope, optical thermoscope, thermal image camera, continuous circuit television etc., the data captured by using the image sensing devices has been processed and analysed by using machine learning algorithm. In this work monitoring system was developed for monitoring the temperature of the weld region that were welded using MIG/TIG, GTAW, GMAW, Laser, Gas Welding. It can be achieved by using Sensor-based technique. A K-Type thermocouple is fixed with a titanium alloy. This kit is placed under the welded metal and the heat distribution of the metal is studied and analysed.

A novel generating machining process for screw-rotors using an internal-cylindrical skiving cutter on a new CNC skiving machine

To reduce machining time and improve accuracy for producing screw rotors, this study proposes a generating machining process using an internal-cylindrical skiving cutter on a new CNC skiving machine. An internal-barrel rotor is first generated based on a conjugation relationship with a male/female rotor. Then, the cutting edges of the internal-cylindrical skiving cutter are defined by the intersection curves between the rake plane and the barrel rotor surface. A geometrical analysis of the clearance angle allows adjustment of the relative position between the cylindrical cutter and the skived rotor on the cutter's axis. Furthermore, a general model for the skiving process is presented. The result shows that the normal deviations on the skived screw rotors are very close to zero. In addition, a new CNC skiving machine is proposed to perform the cutting process when considering the influence of feed rate. Accordingly, the surface morphology of the skived screw rotor is in the form of micro-size waves arranged in a diagonal structure. Finally, the skived screws surface with a predefined grinding stock is achieved by modifying the cutter geometric.

EFFECT OF LONGITUDINAL FEEDS ON FLAT GRINDING PROCESSES ON VIBRATION AND SURFACE ROUGHNESS IN OCR12VM MATERIAL

The grinding process is machining to achieve a high-quality surface finish and high dimensional accuracy. There are two primary processes, the roughing and finishing process. The roughing process generates higher dynamic cutting forces than the finishing process. It leads to a higher vibration that reduces the ground surface quality, tends to lower dimensional accuracy, and shortens the grinding wheel life. On the other hand, the roughing process is applied to reduce machining time, increasing productivity. We conducted this research to study the effect of longitudinal feed on vibration and the surface roughness of hardened tool steel OCR12VM (equivalent to JIS SKD 11), machined by the surface grinding process. The longitudinal feed was varied from 100 mm/s up to 250 mm/s with a 50 mm/s increment. Each variation was machined with a depth of cut of 0.01 mm, 0.02 mm and 0.03 mm, respectively. The constant machining parameter was set: n= 3000 rpm, transversal feeding= 0.04 mm/stroke. The dimension of the grit 80 Aluminium oxide grinding wheel= Ø 200 mm x 25 mm. The result of the research showed that the surface roughness of the ground surface was linearly proportional to the RMS vibration level. The longitudinal feed effects that increased feed resulted from higher surface roughness. The roughness data trend line on the surface has a square function to the longitudinal feed. The similarity, as previously mentioned, is that the vibrations level also increases quadratically with increasing the longitudinal feed. Thus, it is evident that the surface roughness of the ground surface is closely related to the vibration level during the grinding process. Based on this result, it can predict the roughness of the ground surface by measuring the vibration level during the grinding process without measuring the surface roughness.

Machining Characteristics Investigations of DSS‐2205 Using RSM–ANN and Gray Relational Analysis

DSS has low machinability characteristics due to its high strength, machining is complicated, and careful attention is required when selecting machining parameters. The main criteria discussed in this paper concern the turning optimization parameters and machining time reduction of DSS 2205 as the work material. The input parameters are cutting velocity, feeds, cutting depth, and tooltip nose radius of the cutting tool. The design of experiments methodology is employed to design the experiments using Design‐Expert V12 software. The second‐order mathematical model was developed, and analysis of variance was performed to analyze the performance characteristics to recognize the critical variables influencing the output parameter. An artificial neural network (ANN) backpropagation algorithm using MATLAB software was used to develop the mathematical model and optimize the output. The model was developed, and the results were optimized using MATLAB software’s ANN back propagation method to find the best possible solutions. The generated models were significant based on the analysis of variance and the R‐squared value, and these results indicate that the cutting velocity is the most critical factor. For a low machining time, the cutting velocity should be between 100 and 140 m/min, and the tooltip nose radius should be 2.8 mm. The optimal parameter settings are validated by performing a lower is better confirmation test using gray relational analysis (GRA). The GRA exposed the lower machining time at a cutting velocity of 140 m/min, rate of feed of 0.5 mm/rev, cutting depth of 0.5 mm, and tooltip nose radius of 2.4 mm. The predicted values were close to the experimental values, and the result indicates the optimal level of the highest GRA grade of the machining variable.

Chatter suppression with productivity improvement by scheduling a [formula omitted] continuous feedrate to match spindle speed variation

Available spindle speed variation (SSV) methods, which are used to suppress regenerative chatter through disrupting the phase relationship between the inner and outer modulation on the machined surface, are usually performed in conjunction with the periodically varying feeds per tooth from the originally given constant feedrate. This article presents a new feedrate scheduling algorithm to match the SSV so that feed per tooth can be maximized during the whole machining. Concretely, a sinusoidal profile of feedrate is scheduled to match the designed SSV, meanwhile, the C3 continuous condition is guaranteed within all kinematic constraints. The feedrate is scheduled as two parts, i.e. a jerk continuous acceleration profile with the constant feedrate and a continuous sinusoidal profile above the constant feedrate. The continuity of the junction between these two parts is achieved through scheduling a gradually varying sinusoidal profile. To guarantee the kinematic constraints, the derivatives of the sinusoidal amplitude variation are obtained by the properties of trigonometric functions. As a result, a constant feed per tooth, which actually corresponds to the maximum of the periodically varying feeds per tooth associated with all existing SSV methods, is obtained, while the speed still periodically varies. It proves that the proposed scheduling algorithm can improve the machining efficiency since the feed per tooth is kept at the maximum level in most machining periods, and this is a great advancement over all existing SSV methods. Experiments confirm that chatter can be well suppressed with the significantly reduced machining time by using the proposed method.

A Supervised Machine Learning Model for Tool Condition Monitoring in Smart Manufacturing

In the current industry 4.0 scenario, good quality cutting tools result in a good surface finish, minimum vibrations, low power consumption, and reduction of machining time. Monitoring tool wear plays a crucial role in manufacturing quality components. In addition to tool monitoring, wear prediction assists the manufacturing systems in making tool-changing decisions. This paper introduces an industrial use case supervised machine learning model to predict the turning tool wear. Cutting forces, the surface roughness of a specimen, and flank wear of tool insert are measured for corresponding spindle speed, feed rate, and depth of cut. Those turning test datasets are applied in machine learning for tool wear predictions. The test was conducted using SNMG TiN Coated Silicon Carbide tool insert in turning of EN8 steel specimen. The dataset of cutting forces, surface finish, and flank wear is extracted from 200 turning tests with varied spindle speed, feed rate, and depth of cut. Random forest regression, Support vector regression, K Nearest Neighbour regression machine learning algorithms are used to predict the tool wear. R squared, the technique shows the random forest machine learning model predicts the tool wear of 91.82% of accuracy validated with the experimental trials. The experimental results exhibit flank wear is mainly influenced by the feed rate followed by the spindle speed and depth of cut. The reduction of flank wear with a lower feed rate can be achieved with a good surface finish of the workpiece. The proposed model may be helpful in tool wear prediction and making tool-changing decisions, which leads to achieving good quality machined components. Moreover, the machine learning model is adaptable for industry 4.0 and cloud environments for intelligent manufacturing systems.

A Method of Increasing the Accuracy of Low-Stiffness Shafts: Single-Pass Traverse Grinding Without Steady Rests

Abstract The article presents a method of increasing the shape and dimensional accuracy of low-stiffness shafts manufactured in a single pass of a grinding wheel in traverse grinding. One-pass manufacturing is one of the ways for reducing machining time and increasing efficiency, thus lowering production costs. However, maintaining the necessary accuracy proves to be a challenge because the whole machining allowance has to be removed at once, leaving no room for errors that could be fixed in additional passes of the tool. It is especially true in finishing operations, such as traverse grinding. In addition, grinding the workpiece in a single pass of a grinding wheel leads to high forces, which cause elastic deformation of the part. The lower the stiffness of the part, the more difficult it is to achieve the required accuracy. As a result, there are many methods of improving the accuracy of grinding such parts, but they tend to be either expensive or reduce the machining efficiency. Thus, it is important to seek new methods that would allow improving the accuracy of the machining without reducing its efficiency. The proposed method does not require using steady rests and is based on the measurement of the normal grinding force component. Knowing the value of the grinding force when grinding with a set grinding depth, the elastic deformation of the machine tool–tool–workpiece system is calculated in each position of the grinding wheel. Based on the calculated deformation, the additional infeed of the grinding wheel is implemented in order to stabilise real grinding depth and to increase the accuracy of the produced part. The experimental tests were conducted to prove the effectiveness of the proposed method.