Machine Tool Processing Research Articles

Research on Multi-source Energy Consumption Characteristics of CNC Machine Tool Processing System

CNC machine tools are a complex and variable system with a large number of energy-consuming components and the energy consumption type covers the field of electromechanical liquid. Therefore, it is of great significance to study the energy consumption of CNC machine tools in order to find energy conservation and emission reduction, improve resource utilization, reduce environmental pollution, and promote green manufacturing. This paper analyzes the energy consumption form and characteristics of a typical machine tool, combines the machining status of the machine tool, and studies the various energy-consuming components of the machine tool, establishes the power balance equations of each state, and the specific energy consumption and energy efficiency model of the machine tool. The model combines the energy consumption characteristics and machining status of the machine tool, and lays a theoretical foundation for the research of the energy of the CNC machine tool.

MTConnect-based Cyber-Physical Machine Tool: a case study

Recent advancements in Information and Communication Technology have shown immense potential of advancing current CNC machine tools to Cyber-Physical Machine Tools (CPMT). CPMT deeply integrates machine tool and machining processes with computation and networking, thus becoming more intelligent, interconnected and autonomous. However, challenges of data communication and management remain. This is due to the diversity of manufacturing devices, sensors used and hence the delivery of data types. This paper presents an MTConnect-based CPMT that allows diverse types of real-time manufacturing data to be effectively and efficiently collected and managed, to enable advanced human-machine interactions and cloud-based decision-making supports in CPMT.

Process-parallel virtual quality evaluation for metal cutting in series production

Within the metal cutting industry, methods of statistical process control are used to monitor the product quality of series processes. Following the specifications of an initial test planning, produced components are inspected in a sample-like manner. Due to the large number of machining centers, the resulting high output of manufactured parts and centrally installed quality assurance departments, there is still a considerable time delay when feeding back negative trends or tolerance violations. Within this period, scrap parts may be produced. The approach presented in this paper addresses this problem. Based on a combination of process-parallel recorded sensor signals and available manufacturing data along the CAD-CAM-NC process chain, an online material removal simulation is performed next to a real milling process. Within the simulation, the properties of cutting tools, machine tool and manufacturing process directly impacting workpiece quality are compiled in efficient models that run simultaneously and further approximate the machined workpiece shape. The resulting, digital workpiece is measured virtually based on given quality requirements defined in ISO 1101 to allow an early identification of negative trends and quality issues while reducing long quality feedback loops.

Regular Neural Network Based Machining Parameters Optimization Model Of NC Machine Tool

Energy consumption process is the basis for energy efficiency improvement of machine tools. Most of the existing researches focus on the static modelling of energy consumption of a machine tool; however, there are a few studies that paid attention to that how process parameters influence the energy consumption of machine tools during processing. It is noted that the process parameters can be selected to reduce energy consumption during machining processes without additional investment. In this paper, a characteristic energy consumption model for NC machine tool was proposed. Then, the mapping rule between process parameters and energy consumption of machine tool was studied, and the model was solved with the regular neural network (RNN). Finally, the result was verified with an experiment of milling the surface of aluminium block, which can effectively improve the energy efficiency of machine tool. The experiment results are shown that regular neural network is used to optimize the process parameters and process the same machining characteristics; we analyze the in machining process of machine tool based on the three cutting parameters, and then, a model of energy consumption. We employ to learn, and use this trained model to select optimal parameters.

Modelling and compensation of thermal deformation for machine tool based on the real-time data of the CNC system

As one of the main issue affecting the precision and stability of the machine tool, thermal deformation is determined by the environmental temperature as well as the heat generated in the working process of machine tool. Currently, research work on the modelling of thermal deformation mostly rely on the data retrieved from temperature sensors embedded or amount on the machine tool, where the thermal deformation is modelled as function of temperature field in the machine tool. Thermal deformation thus modelled is normally impractical in the thermal compensation process because of the high cost and low stability it may incur when a set of temperature sensors are utilized. In this paper, a novel approach for modelling and predicting the thermal deformation of ball screw is proposed. Instead of relying on the data from temperature sensors, the real-time data of the CNC system, e.g., current and velocity of driving motor, and displacement of axis, is utilized directly to derive the thermal deformation model. With such data, a model is firstly built with considering the heat generated from friction motion as well as the heat dissipation with the machining environment; the parameters of the proposed model were then identified by a multiple linear regression method. Based on the proposed model, the thermal deformation of ball screw can be precisely estimated in both the warming up and cooling down state of the machine tool, as validated by experiments on a ball screw of a small size machine tool.

Drilling reconfigurable machine tool selection and process parameters optimization as a function of product demand

Special purpose machines (SPMs) are customized machine tools that perform specific machining operations in a variety of production contexts, including drilling-related operations. This research investigates the effect of optimal process parameters and SPM configuration on the machine tool selection problem versus product demand changes. A review of previous studies suggests that the application of optimization in the feasibility analysis stage of machine tool selection has received less attention by researchers. In this study, a simulated model using genetic algorithm is proposed to find the optimal process parameters and machine tool configuration. During the decision-making phase of machine tool selection, unit profit is targeted as high as possible and is given by the value of the following variables: SPM configuration selection, machining unit assignment to each operation group, and feed and cutting speed of all operations. The newly developed model generates any random chromosome characterized by feasible values for process parameters. Having shown how the problem is formulated, the research presents a case study which exemplifies the operation of the proposed model. The results show that the optimization results can provide critical information for making logical, accurate, and reliable decisions when selecting SPMs.

Study of laser precision ablating texture patterns on large-scale freeform surface

Texture patterns are fabricated on the surface of freeform parts in order to improve the performance, and to meet special functional requirements. Due to its low processing efficiency and poor processing accuracy and quality, the conventional mechanical processing cannot fully meet the requirements of the texture pattern processing on parts surface. A novel laser cross-scale micro-3D processing technology, named laser ablation by projective galvanometer scanning (LPAGS), which can fabricate texture patterns on freeform surface with high efficiency and precision has been developed in this paper. The new method is based on the combination of multi-axis machine tool and 3D galvanometer scanning laser processing head. An equal arc length projection was adopted to establish reasonable and undistorted processing model on freeform surface. By reasonable division plan, freeform surface was divided into sub-areas, sub-layers and sub-blocks. Patterns on each sub-area were converted into 2D scanning processing path by reverse projection. By systematically studying the effect of the variation of surface curvature on the focused laser spot shape, energy distribution in laser spot, the criterions of surface partition which satisfy the processing dimensional accuracy and quality were given. The experimental results demonstrate the effectiveness of this method in fabricating texture patterns on large-scale freeform surface.

Augmented Reality-assisted Intelligent Window for Cyber-Physical Machine Tools

Aiming to advance current machine tools to a higher level of intelligence and autonomy, this paper presents a new generation of machine tools, i.e. Cyber-Physical Machine Tool (CPMT), inspired by the recent advances in Cyber-Physical Systems (CPS). CPMT refers to a CPS-enabled machine tool that integrates physical machine tool and machining processes with computation and networking capabilities. Augmented Reality (AR) is used to enable intuitive and efficient human-machine interactions between humans and CPMT. An AR-assisted Intelligent Window for CPMT is proposed. The Intelligent Window is essentially an advanced Human-Machine Interface (HMI) which provides users with intuitive interactions with CPMT. The proposed Intelligent Window consists of four main functional modules, Real-time Control, AR-enabled Process Monitoring, AR-enabled Machining Simulation, and Process Optimization. An AR-assisted Intelligent Window for an EMCO Concept 105 milling machine is developed making use of a touch-screen computer. The advantages and potentials of CPS and AR in manufacturing are discussed based on the experience gained from the experiments.

Large scale layering laser surface texturing system based on high speed optical scanners and gantry machine tool

Abstract A layering laser surface texturing system for large freeform surface workpiece was realized with a reconfigurable structure of usual mechanical gantry machine tool plus laser processing head, which is composed of dual high speed galvanometer optical scanning axes and a mechanical linear axis. The principle of layering processing ability of laser focus point and the method to divide freeform surface into partitions and layers was analyzed. The kinematic of five axes machine tool with three axes laser processing head was derived. A novel computer aided laser texturing software named WnloLaserRobots was designed on visual C++ platform, which provides full function of 3D model data analyzing and real time processing control. The laser head position, the orientation data, and the layering scanning paths for processing a freeform surface were generated through importing and analyzing the universal initial graphics exchange specification (IGES) model file. In the control module, the software sends command to five axes gantry machine tool to position laser head normal to the processing surface partition and control laser processing head to accomplish layering laser texturing. This proposed system is particular suitable for laser ablation of film layer from multilayer materials on large scale free form surface. It has been used in practical application and satisfactory results were obtained.

The Rack-Gear Tool Generation Modelling. Non-Analytical Method Developed in CATIA, Using the Relative Generating Trajectories Method

The modelling of a curl of surfaces associated with a pair of rolling centrodes, when it is known the profile of the rack-gear's teeth profile, by direct measuring, as a coordinate matrix, has as goal the determining of the generating quality for an imposed kinematics of the relative motion of tool regarding the blank. In this way, it is possible to determine the generating geometrical error, as a base of the total error. The generation modelling allows highlighting the potential errors of the generating tool, in order to correct its profile, previously to use the tool in machining process. A method developed in CATIA is proposed, based on a new method, namely the method of “relative generating trajectories”. They are presented the analytical foundation, as so as some application for knows models of rack-gear type tools used on Maag teething machines.

Twin-Control: A New Concept Towards Machine Tool Health Management

Twin-Control (http://twincontrol.eu/) is a new concept for machine tool and machining process performance optimization. It is based on a new simulation model that integrates the different aspects that affect machine tool and machining performance. This holistic approach will allow a better estimation of machining performance than single featured simulation packages, including lifecycle concepts like energy consumption and end-life of components.This theoretical representation of the machine is complemented with real process data by the monitoring of the most important variables of the machining process and machine condition. This monitored information, combined with the developed models, is used at machine level to perform model-based control actions and/or warn about damaged components of the machine tool. In addition, a fleet-level data management system is used for a proper health management and optimize the maintenance actions on the machine tools.

Human-Machine Interface for Artificial Neural Network Based Machine Tool Process Monitoring

Through the use of Artificial Neural Network (ANN) for sensor signal analysis, the traditional trial-and-error approach to process monitoring can be omitted. There is, however, still the need for a review of the Human-machine interface (HMI). Correct decisions must be made regarding ANN machine learning. There will always be a requirement to be able to modify and improve the monitoring system based on experience. Moreover, the question remains- can monitoring data be used for increased process knowledge and for both short term and long-term analysis and decision making? The work presented in this paper is a part of the EU FP7 REALISM Project.

Wear mechanism of coated and uncoated carbide cutting tool in machining process

Abstract

Assembly errors analysis of linear axis of CNC machine tool considering component deformation

During the assembly process of CNC machine tool, the assembly dimensions are varied due to the effect of the component deformation caused by gravity. Hence, a mathematical model predicting assembly errors needs to be constructed considering component deformation before tolerance design process. This paper presents an assembly error model of linear axis of CNC machine tool to analyze the effects of component manufacturing error and deformation on axis based on the 3-2-1 localization theory. Thereafter, axis position and angular deviations are calculated under four different conditions, ideal condition, condition considering manufacturing error only, condition considering the effect of structure, and actual condition. At last, the effect of worktable gravity on Y-axis deformation is taken as an example to verify the model. This model can be used to verify the tolerance at tolerance design or to calculate modification amount in assembly process.

Online Prediction of Tool Wear in the Milling of the AISI P20 Steel Through Electric Power of the Main Motor

Molds and dies have specific geometries that are linked to the profile of injected products. The great challenge for modern industry is to manufacture these products with high quality and low time. Ball nose end cutters provide flexible manufacturing due to their geometry that allows a single point of contact on free-form surfaces. However, the exact definition of the tool’s wear during the milling of molds and dies is complex. This work shows the monitoring of tool wear based on information obtained from the electric power consumed by the main motor. Experiments were carried out on work pieces of AISI P20 steel with dimensions of 20 × 20 × 10 mm. The tools used in the test were solid carbide ball nose mills with a diameter of 6 mm. Analysis of variance was applied to define the most influential input parameter in the milling process, considering electric power and torque as a response. The results showed that the online monitoring of the tool based on electric power information can be a good technique to define the tool’s wear during the milling process. Moreover, the electric system of monitoring was easier to assemble than traditional devices, such as piezoelectric dynamometers or load cells, because it provided an assembly without great interference in the machine tool or milling process. The energy analyzer was capable of detecting a variation in the electric power. The electric power and the torque decreased when the tool’s wear increased from new to worn tool.

A STEP-compliant Intelligent Process Planning System for Milling

The new standard ISO 14649 provides CAD/CAPP/CAM/CNC with an interface and a comprehensive high level and detailed manufacturing information. CNC technology based on STEP-NC will be the future trend, and the intel- ligent process planning is the core of STEP-NC oriented CNC technology. The objective of this research is to present the architecture, key technologies and implementation of STEP-NC oriented intelligent computer-aided process planning sys- tem. Some key technologies, such as feature recognition, setup planning, operations selection, tool selection, machine tool selection and process planning optimization are described in detail. A hybrid approach based on artificial intelligent tech- niques (neural networks, fuzzy logic and genetic algorithm) is adopted as the inference engine in the proposed system. Based on Solidworks CAD platform, the corresponding prototype system was developed using VB.NET programming language. The implementation of the proposed ST-ICAPP system is demonstrated by an example case study. The result shows that the proposed system is valid and feasible.

Position-dependent stability analysis of turning with tool and workpiece compliance

A universal frequency domain approach for studying the dynamics of metal cutting with a flexible workpiece and a compliant tool is derived. The method is used for the identification of the position-dependent stability lobes in turning. It enables a fast, accurate, and systematic stability analysis of turning processes in the parameter space, which is not restricted to simple dynamic models with only a specific number of modes in one spatial direction. In particular, a combination of experimental data for the tool tip dynamics with analytical or numerical data for the workpiece dynamics is possible, which is demonstrated by a concrete example. The effect of the mode interaction between tool and workpiece modes via the cutting process is illustrated. Counterintuitively, the possibility of a process destabilization for an increased workpiece stiffness was observed, which can be explained by the mode interaction. The presented methods and results can be efficiently used for optimizing machine tool development and process planning.

Method for Modeling the Dynamic Energy Characteristics of Multi-energy Sources in CNC Machine Tool

Estimation and analysis of energy consumption for machine tool is the basis of energy efficiency improvement. The existing researches of energy model focus on the static modelling of energy consumption, and a few study of dynamic energy consumption model focus on the dynamic characteristics of operating states of machine tool, the study of dynamic characteristics of energy sources, especially multi energy sources of a CNC machine tool is scarce. Based on the characteristics of CNC machine tool(e.g. multi-energy sources, dynamically changed machining tasks and process parameters), a method for modeling the dynamic energy characteristics of multi-energy sources in CNC machine tool is proposed. The dynamic characteristics of energy consumption process of machine tool are analyzed. On this basis, the colored timed object-oriented Petri net(CTOPN) integrated with virtual component method is applied to model the dynamic energy consumption of machine tool. The CTOPN model is used to describe the dynamic characteristics of operating states of machine tool and multi-energy sources. The virtual component method is used to describe the dynamic characteristics of multi energy sources which is influenced by process parameters. The virtual component model is driven by the information of activity transitions in CTOPN model. The case study shows that the proposed energy model is effective and can provide fundamental support for dynamic energy consumption forecasting, comprehensive depiction of the energy profile and quantitative analysis of influencing factors and so on during the operation of a CNC machine tool, which has significance in practice.

Manufacturing Accuracy Control of Precision Machine Tool Based on Taguchi Method

In the manufacturing process of precision machine tool, the precision of the machine tool can not simply rely on parts design tolerance to achieve, and also need choose parts assemble for different tolerance values to ensure the accuracy of machine tools. Machine tool assembling deviation accumulation model is established by using the homogeneous coordinate transformation matrix, the quality loss function of taguchi methods apply to mechanical products matching activities. Considering the processing deviation of parts, the quality loss function of assembly cumulative deviation as an effective indicator for measure machine tool accuracy, the assembly precision of machine tools are ensured by optimize matching different geometric deviation parts. An example is given to testify the validity of this method.

Research Precision CNC Machine Error Analysis Hot Weights

The level of industrial strength is a measure of an important indicator of a country's pillar industry level is machinery manufacturing, in modern times, the rapid development of manufacturing technology, precision and ultra-precision manufacturing technology has become an important part of modern machinery processing and manufacturing,which CNC machine tools due to its own characteristics and soon became an important part of industrial manufacture. CNC machine tool manufacturing and processing machinery as an important tool, it is an important factor affecting the accuracy of indicators work-piece machining accuracy. Therefore, improving the accuracy of CNC machine tool research has been of great concern. This paper studies CNC machine thermal geometrical analysis accuracy error rights.