Machine Tool Research Articles

The influences of alloying elements and processing conditions on the machinability of wrought aluminium alloys: A literature review

Today, wrought aluminium alloys are ranked among the most important materials with low specific density, strength features, corrosion resistance, machinability and recyclability properties in numerous application fields. Machining process of these alloys involves a number of considerations to ensure the highest quality and productivity. Key machinability issues include the rate of tool wear, workpiece surface finish and integrity and machining process sustainability. Understanding and optimising these parameters can lead to improving the machinability, better surface finish, longer tool life, and overall, more efficient and cost-effective machining processes for wrought aluminium alloys. The machining of wrought aluminium alloys remains a challenging task due to their unique characteristics, such as high strength, thermal conductivity and refined grain structures with mechanical and/or heat treatment. Although considerable progress has been made in the development of cutting tool materials and machining techniques for wrought aluminium alloys, further research is required to improve the machinability of these materials and reduce the associated costs and time needed for production. Having access to relevant information about these characteristics can help industries and researchers make informed decisions and achieve better outcomes when machining of these important materials. Accordingly, in the current research work, a comprehensive study has been carried out on machining of wrought aluminium from different points of view.

Complementary Machining – Machining Strategy for Surface Modification: A Review

All fabrication techniques utilized to manufacture metallic parts modify the surface integrity of the part. Complementary machining is a relatively recent machining strategy characterized by combining metal cutting and mechanical surface treatment. Typically, it implies that after conventional machining, the cutting insert is used reversely to modify the surface by local plastic deformation. To improve product performance, mechanical surface treatment is an additional phase in the manufacturing process chain that usually results in longer production times and higher costs. As a result, a variety of hybrid techniques have been created, such as complementary machining, which has the benefit of using conventional machine tools and their associated cutting tools. The study on complementary machining is reviewed in the article. The main focus is to assess the viability of complementary machining to modify surface integrity for enhanced properties by specifically establishing its effect on tool wear, surface roughness, microhardness, fatigue, microstructure, and residual stress state.

Fully coupled thermal-structural-vibration characteristics of 3-axis machine tools with complex hybrid linear axis-spindle systems considering structural nonlinearities

For machine tools composed of functional components bonded by joints with structural nonlinearities, the load- and temperature-dependent nonlinear restoring force,stiffness, and frictional heat generationfrom the jointdetermine its complex dynamic fully coupled thermal-structural-vibration characteristics. In response, in this work, considering the vibration feedback dominated by the structural vibration and employing a partition-based closed-loop iterative algorithm, a fully coupled thermal-structural-vibration model that characterizes the nonlinear coupling between temperature and mechanical fields is developed with the joint as the link. The multi-body dynamics model of the whole machine tool, considering the flexibility of the screw shaft, spindle, and joint nonlinearity, characterizes the milling dynamics influenced by structural vibration and regeneration effects. The global thermal resistance network model with dynamic heat flux, viscosity-temperature effect, and time-varying thermal resistanceis establishedto characterize the thermal characteristics. The partition-based closed-loop iterative algorithm is employed to realize the multi-field coupling and consider the moving heat source and the load distribution change caused by the reciprocating moving joint. Considering the changes in contact angle and contact deformation caused by preload, dynamic load, and thermal effect, load- and temperature-dependent nonlinear joint restoring force and stiffnessare developed. Based on the viscosity-temperature effects and the change of contact friction caused by thermal expansion and dynamic load, the load- and temperature-dependent nonlinear joint frictional heat generation is calculated. The milling load from tool-workpiece interaction is calculated, and the influence of multi-field coupling on regenerative chatter is explored. The proposed model was experimentally verified, andthe effectof parameterswas numerically investigated. The results show that the dynamic milling load excites the whole machine tool vibration, and for every 10 μm increase in cutting depth, the radial amplitude of the tool nose increased by at least 1 μm. The nonlinear coupling between temperature and mechanical field significantly affects the system response, and the joint contact stiffness dominates the multi-field coupling behavior.

Modal Parameter Identification of Electric Spindles Based on Covariance-Driven Stochastic Subspace

Electric spindles are a critical component of numerically controlled machine tools that directly affect machining precision and efficiency. The accurate identification of the modal parameters of an electric spindle is essential for optimizing design, enhancing dynamic performance, and facilitating fault diagnosis. This study proposes a covariance-driven stochastic subspace identification (SSI-cov) method integrated with a simulated annealing (SA) strategy and fuzzy C-means (FCM) clustering algorithm to achieve the automated identification of modal parameters for electric spindles. Using both finite element simulations and experimental tests conducted at 22 °C, the first five natural frequencies of the electric spindle under free, constrained, and dynamic conditions were extracted. The experimental results demonstrated experiment errors of 0.17% to 0.33%, 1.05% to 3.27%, and 1.29% to 3.31% for the free, constrained, and dynamic states, respectively. Compared to the traditional SSI-cov method, the proposed SA-FCM method improved accuracy by 12.05% to 27.32% in the free state, 17.45% to 47.83% in the constrained state, and 25.45% to 49.12% in the dynamic state. The frequency identification errors were reduced to a range of 2.25 Hz to 20.81 Hz, significantly decreasing errors in higher-order modes and demonstrating the robustness of the algorithm. The proposed method required no manual intervention, and it could be utilized to accurately analyze the modal parameters of electric spindles under free, constrained, and dynamic conditions, providing a precise and reliable solution for the modal analysis of electric spindles in various dynamic states.

A novel FMECA method for CNC machine tools based on D-GRA and data envelopment analysis

Failure Modes, Effects, and Criticality Analysis (FMECA) is a commonly used method for analyzing system reliability. It is frequently applied in identifying weak points in the reliability of CNC machine tools. However, traditional FMECA has issues such as vague descriptions of risk factors, equal treatment of risk factors, and unclear directions for improving weak points. In response to the issue of vague descriptions of risk factors, this paper further expands severity (S) into machine hazard (M) and personal hazard (P), and subdivides detectability (D) into functional structural complexity (D1) and detection cost (D2). In addressing the issue of treating risk factors equally, this paper integrates Distance Analysis Method (DAM) and Grey Relational Analysis (GRA) to propose Distance-Grey Relational Analysis (D-GRA). Subsequently, based on the D-GRA method, the weights of each risk factor were determined by comprehensively considering expert system scores and actual economic loss indicators. In response to the issue of unclear improvement directions for weak points, this paper introduces the BCC model. It treats common failure modes of CNC machine tools as decision-making units within the BCC model, refines risk factors as input indicators, and evaluates the efficiency values of each decision-making unit based on various actual losses as output indicators. Through efficiency value analysis, it proposes improvement directions for weak points. Then, based on the weights of risk factors and the efficiency values of failure modes, a modified calculation method for the new Risk Priority Number (RPN) is proposed to amend the traditional RPN, This paper takes the electric spindle system of a certain machining center as an example, applies the proposed method to rank common failure modes with the new RPN, and compares it with other RPN calculation methods to verify the rationality of the proposed approach. Finally, it presents improvement directions for reliability enhancement.

Performance analysis of turning operation parameters empirically on Delrin

Delrin is the best additional material for metals because of its inherent qualities, such as great wear resistance and tensile strength. The main aim of this work is to investigate how independent variables like feed rate, spindle speed, and depth of cut are significant for dependent variables such as surface roughness, temperature, stresses, and material removal rate on novel material Delrin. The independent variable ranges are selected based on tool and workpiece material combinations and machine tool specification as spindle speed 230 – 844 rpm, feed rate 0.5 – 1.5 mm/rev, and depth of cut 1 – 3 mm. Based on the L27 orthogonal array experimental plan 27 numbers of experiments are conducted by the design of experiment concepts. The theoretical investigation through response surface methodology is also conducted to establish how independent variables affected dependent variables when Delrin was being machined. The independent variable's significance is determined by the ANOVA table for all the considered responses. In addition to the considered flow of work, the experiential model is developed by the utilization of regression analysis. The developed models are confirmed by experimental data and the models have the best validation results with the experimental results.

The Analysis and Compensation Experiment of Linear Feed Axis Positioning Error Based on Matrix Operation

The positioning accuracy of the linear feed axis is a key factor affecting the machining accuracy of CNC machine tools. The generation process of the positioning error of the linear feed axis is expounded. The Z axis of the vertical linear feed axis of the machining center is the research object, the positioning error analytical matrix is established, and the positioning error calculation of the Z axis of the vertical linear feed axis is completed based on the principle of solving the inhomogeneous linear equation. The positioning error detection of the vertical linear feed axis Z axis of the CNC machining center is carried out. Based on the characteristics of the error detection data, an error compensation model is constructed. , the variance of the positioning error detection point after compensation is reduced to 1.562, which meets the needs of precision machining.

Modeling of relationships between producer and consumer: The impact of advanced production equipment

Subject. The article discusses the influence of advanced production equipment on modeling the relationship between manufacturer and consumer. Objectives. The aim is to assess the impact of the organization's existing production facilities, created on the basis of modern machine tools, on the possibility of building mutually beneficial relations between the manufacturer and the buyer of products. Methods. The research rests on methods of economic and mathematical modeling, in particular, the game theory tools. Results. We formulated a meaningful model of idealized interaction between the parties in the process of concluding a contract for production and purchase of goods, offered a model of positional game of the parties with complete information, in which the participants consistently make decisions with understanding the opponent's previous actions. The analysis of the constructed model enabled to determine the situation of balance between the parties and demonstrate a decrease in consumer's influence on the results of the interaction, due to flexibility and adaptability of production capabilities of the manufacturing organization. Conclusions. The findings demonstrate the influence of aspect associated with the involvement of modern production equipment on the consumer's willingness to establish mutually beneficial relationships with the manufacturing organization. This provides a basis for subsequent implementation of partnership within the framework of relationship marketing.

An interval prediction approach for quantifying the thermal error uncertainty of ball screw system

The ball screw system is one of the most core components of CNC machine tool, and its thermal positioning error directly affects the machining accuracy of parts. Establishing a data-based thermal error model is currently a popular method to predict this thermal error. Uncertain disturbances are unavoidable in reality. However, most of the existing thermal error models are deterministic, which cannot consider the uncertainty and generally can only provide the predictive value. To quantify the uncertainty of thermal error from the ball screw system, this paper proposes an interval prediction method, in which gated recurrent unit (GRU) neural network is embedded into the model and prediction interval (PI) evaluation indexes are used to determine the model parameters. Both the temporal and spatial characteristics of thermal error in the ball screw system are taken into account by the proposed method. The experimental data has verified that the proposed method can effectively generate PIs for the thermal error of ball screw system.

Toward a digital twin of a robot workcell: standards and methods

Digital twins are poised to improve the engineering, operation, and management of manufacturing facilities. However, implementing digital twins has been a challenge primarily due to a lack of resources and the absence of standardized digital twin frameworks and methods. This paper describes creating a digital twin of a robot workcell using available standards, tools, and methods. The workcell comprises collaborative robot arms, a computer numeric control (CNC) machine tool, and a coordinate measuring machine (CMM). The ISO 23247 standard provides a guideline for building the digital twin. Data are collected from physical devices and the MTConnect standard provides a format for communicating the data between the physical equipment and their digital counterparts. The machine components in the workcell are represented as models in the virtual world. This research shows that the above standards and methods support building a digital twin of a robot workcell. The data collected and communicated can be used to improve workcell functions such as quality management and predictive maintenance.

Hyper CLS-Data-Based Robotic Interface and Its Application to Intelligent Peg-in-Hole Task Robot Incorporating a CNN Model for Defect Detection

Various types of numerical control (NC) machine tools can be standardly operated and controlled based on NC data that can be easily generated using widespread CAD/CAM systems. On the other hand, the operation environments of industrial robots still depend on conventional teaching and playback systems provided by the makers, so it seems that they have not been standardized and unified like NC machine tools yet. Additionally, robotic functional extensions, e.g., the easy implementation of a machine learning model, such as a convolutional neural network (CNN), a visual feedback controller, cooperative control for multiple robots, and so on, has not been sufficiently realized yet. In this paper, a hyper cutter location source (HCLS)-data-based robotic interface is proposed to cope with the issues. Due to the HCLS-data-based robot interface, the robotic control sequence can be visually and unifiedly described as NC codes. In addition, a VGG19-based CNN model for defect detection, whose classification accuracy is over 99% and average time for forward calculation is 70 ms, can be systematically incorporated into a robotic control application that handles multiple robots. The effectiveness and validity of the proposed system are demonstrated through a cooperative pick and place task using three small-sized industrial robot MG400s and a peg-in-hole task while checking undesirable defects in workpieces with a CNN model without using any programmable logic controller (PLC). The specifications of the PC used for the experiments are CPU: Intel(R) Core(TM) i9-10850K CPU 3.60 GHz, GPU: NVIDIA GeForce RTX 3090, Main memory: 64 GB.

Electro-discharge machining using copper-coated additively-manufactured AlSi10Mg electrodes

Customized fabrication of electrodes for electro-discharge machining (EDM) is considered as a useful emerging application of metal additive manufacturing technologies for building complex shaped tools in a short period of time. This approach of producing electrodes not only effectively addresses the design flexibility and complexity issues of the tools but also enables the use of various materials so as to ingrain the desired properties in them. The current study explores the production of laser powder bed fusion (LPBF) AlSi10Mg tools for machining of titanium alloy. Moreover, it explicitly assesses the machining performance of additively manufactured electrodes when the tools are subjected to a thin layer of copper coating. Three types of acidic baths are used for electroless coating of tools. The machining performance of the tools in terms of material removal rate, tool wear rate, surface crack density, surface roughness, white layer thickness, and microhardness of the white layer is compared for different electroless copper coating processes. It is observed that the coated tools significantly enhance material removal rate; however, uncoated LPBF tools exhibit superior surface morphology and surface roughness.

Making sense of the interaction between geopolitics and middle-technology trap: evidence from China’s catching-up CNC machine tool industry

This study proposes a framework to analyze the interaction between geopolitics and the Middle-Technology Trap, drawing on evidence from China's CNC machine-tool industry. Qualitative methods such as interviews have been utilized to identify geopolitical variables that have driven the development of the Chinese CNC machine tools industry. To explore the combined effects of the Middle-Technology Trap and the intense competition between China and the West, this article focuses on an industry that has been significantly influenced by the economic decoupling between China and the US, a phenomenon deeply rooted in recent great power competition. While several industries may illustrate the new status quo of the Middle-Technology Trap, the CNC machine-tool industry is particularly exemplary due to its close interrelation with geopolitics. Accordingly, this study presents a catch-up case that incorporates technology-oriented aspects and factors from strategic competition, underlining three scenarios that geopolitics could influence the result of addresing the Middle-Technology Trap, respectively technological decoupling, the relocation of supply chain, and industrial policy.

Design and damping characterization of sandwich composite made of particle-filled hollow spheres and steel sheets

Sandwich composites made of particle-filled hollow sphere structures (PHSS) and steel sheets offer excellent lightweight and damping properties, ideal for high-speed machine tool components under dynamic loads. Previous research has focused on single particle-filled hollow sphere (PHS) and small PHSS, leaving a gap understanding of PHSS/steel sandwich composites. In this study a test rig was developed, and Design of Experiments and Response Surface Analysis were used to investigate the effects of sheet and core thickness (design parameters), filling ratio, and particle size (particle parameters) on the damping performance of PHSS/steel sandwiches. The results indicate that the design parameters have a significant influence on damping performance. The interaction between design and particle parameters also substantially affects damping. Minimizing particle size, increasing filling ratio, thinning the face layer, and thickening the core layer significantly improve structural damping. To address manufacturing tolerances, a finite element (FE) model-based optimization was developed to accurately determine PHSS material parameters. These parameters were used in an FE model of the PHSS-steel composite, with identified contact parameters minimizing measurement and simulation differences. The homogenized material model and the linear model using global damping parameters accurately reproduce the dynamic properties of the PHSS-steel sandwich composite in low vibration modes.

Wear Behavior of TiAlN/DLC Coating on Tools in Milling Copper–Beryllium Alloy AMPCOLOY® 83

In recent years, the exponential growth of the machining industry and its needs has driven the development of new manufacturing technologies, more advanced cutting tool types, and new types of coatings to extend tool lifespan. New coating solutions have been studied and implemented for machining tools, which provide a low friction coefficient and lubrication, thus increasing tool lifespan. Following this line of reasoning, it is relevant to develop scientific work aimed at studying the behavior of cutting tools coated with thin films that promote low friction and high lubrication, as is the case with DLC (diamond-like carbon) coatings. These coatings promote good resistance to oxidation and allow high machining speeds, properties also exhibited by TiAlN (titanium aluminum nitride) coatings. In fact, there is a gap in the literature studying the performance of cemented carbide tools provided with multilayered coatings in milling operations of Cu–Be alloys, commonly used in inserts of plastic injection molds. This study’s objective was to investigate the effect of a multilayer coating (TiAlN/DLC) on end-milling tools to analyze their cutting performance when milling a Cu–Be alloy known commercially as AMPCOLOY®83. The quality of the machined surface was evaluated, and the wear of the cutting tool was studied. A comparative analysis of milling parameters with respect to their effect on the condition of the surface after machining and the resulting wear on the tools, using coated and uncoated tools and different machining parameters, allowed us to verify a better quality of the machined surface and wear quantified in approximately half when used coated tools.

Positioning errors and accuracy of CNC machine tools

Abstract Computerized numerically controlled (CNC) machine tools are a solid foundation for modern industrial manufacturing. These machines provide linear and angular displacements in three or five axes. The accuracy of moving and positioning the tooltip relative to the work part's surface determines the final product's accuracy. Errors due to machine positioning can be linear or angular or both. This work studies the positioning errors of a vertical turning center CNC machine type. This machine has three axes, two linear X (horizontal) and Z (vertical), and one rotation axis “C”. The travel of the spindle carrying the cutting tool along the X-axis is 1400mm, and the travel of the carriage holding the spindle along the Z-axis is 1300mm divided into six latches. The machine rotary table which carries the work part can rotate 360°. A highly precise laser interferometer system determines the linear positioning errors in both the X and Z axes. The angular positioning errors for the “C” rotation axis are determined accurately by an autocollimator system. The positioning error measurements are carried out based on ISO 230-2 as a measurement standard of positioning deviation, accuracy, and repeatability. The collected error measurements in linear displacements and angular rotation are analyzed, interpreted, and compared with manufacturer specifications and ISO 13041-4 allowable tolerances.

Influence of Tool Wear and Workpiece Diameter on Surface Quality and Prediction of Surface Roughness in Turning

In turning, tool wear and cutting vibration are inevitable, which have great influence on surface quality. Analyzing the influence mechanism of tool wear and cutting vibration on surface quality is important to achieve the accurate prediction of surface roughness before machining and improve machining quality. In this paper, a turning vibration experiment is conducted to reveal that the diameter of shafts is an important factor affecting the vibration amplitude and frequency. In addition, based on machining parameters, tool wear and workpiece diameter, this empirical model, the response surface method and a support vector machine are used to model and predict surface roughness. The fitting accuracy, prediction accuracy and generalization performance of the proposed methods are compared in detail. The results show that the response surface modeling method has the highest fitting accuracy, but the exponential empirical modeling method has the highest prediction accuracy and best generalization performance. Additionally, the prediction results indicate that the surface roughness increases with the increase in tool wear and decreases with the increase in workpiece diameter.

Influence of different wire materials on the machining of Albromet W130 by WEDM

Wire Electrical Discharge Machining (WEDM) is an unconventional machining technology, widely spread in different sectors of many industries. The machining uses periodically repeating electrical impulses, which allows all materials with at least a minimum electrical conductivity to be machined. The machining tool is a thin wire that can be made of many different materials. Wire electrode material requirements vary from industry to industry. This study aimed to increase the efficiency of WEDM through an analysis of the influence of the type of wire material on the machining of Albromet W130 copper alloy. The analysis comprised a molybdenum, brass and a copper wire of a 0.25 mm diameter and a Box-Behnken design of experiment totalling 42 rounds. The design of experiment results analysis covered cutting speed, machined surfaces’ topography and morphology, and the subsurface layer condition. Furthermore, an analysis of the wire electrodes used in the experiment was carried out. The highest cutting speed was achieved using a copper wire and the lowest R a values were achieved in samples machined with a molybdenum wire.

Uncertainty propagation of initial conditions in thermal models

The operation of machine tools often demands a highly accurate knowledge of the tool center point’s (TCP) position. The displacement of the TCP over time can be inferred from coupled thermal models, which comprise a set of geometrically coupled heat equations. Each of these equations represents the temperature in part of the machine, and they are often formulated on complicated geometries. The accuracy of the TCP prediction depends highly on the accuracy of the model parameters, such as heat exchange parameters, and the initial temperature. Thus it is of utmost interest to determine the influence of these parameters on the TCP displacement prediction. In turn, the accuracy of the parameter estimate is essentially determined by the measurement accuracy and the sensor placement. Determining the accuracy of a given sensor configuration is a key prerequisite of optimal sensor placement. In this paper, we develop a thermal model for the discretization of realistic machine tool. On top of this model we propose two numerical algorithms to evaluate any given thermal sensor configuration with respect to its accuracy. We compute the posterior variances from the posterior covariance matrix with respect to an uncertain initial temperature field. The full matrix is dense and potentially very large, depending on the size of the discretized model. We first present a way to compute this approximation, which relies on the computation of the model sensitivities with respect to the initial values. Additionally, we present a low-rank tensor method which exploits the underlying system structure. We compare the efficiency of both algorithms with respect to runtime and memory requirements and discuss their respective advantages with regard to optimal sensor placement problems.

Digital dynamic modeling and topology optimized design of shell face mills

This paper presents digital modeling of shell face mills in milling cylinder heads. The cutter's structural dynamics and its mode shapes are predicted using a Finite Element system. The geometries of the cutter body and inserts are imported from their Computer Aided Design (CAD) models. The insert edge is discretized into small segments to model its varying normal rake and inclination angles, which affect the cutting mechanics. The cutter is dynamically assembled with the target machine tool spindle using the receptance coupling method. A general dynamic cutting force model, which considers the varying edge geometry and inserts’ run-outs, is developed and used to predict cutting forces and chatter stability diagrams. The proposed model is experimentally verified to demonstrate the feasibility of the systematic application of physics-based digital design and analysis of tools for the mass machining of specific parts. The cutter body shape is optimized to increase the stiffness of the bending mode shape that caused chatter via topology optimization, which led to five-fold increase in the absolute stable depth of cut.