Off-line optimization on NC machining based on virtual machining

With the help of the computer simulation technology, the virtual machining simulation system of NC machine tool was built based on the integrated simulation module of the UG NX software. Through setting the NC machine parameter of the virtual machining system, the machining process simulation of the typical parts in virtual environment was completed, which implements the collision and interference checking in the process of NC machining and verifies the correctness of NC programs. Finally, the simulation results prove that the assembly modeling method of virtual simulation system for the NC machining is feasible, and the simulation of machining process can effectively determine the mutual interference problem of the machining system, which lays the foundation for effectively preventing the collision accident.

With the continuous development of manufacturing industry, the application range of NC machining technology has been further expanded. The contour accuracy is strongly related to the NC machining quality as a key machine tool performance indicator. Its application efficiency is plainly low as the majority of offline compensation-based contour accuracy adjustments rely heavily on manual experience. Moreover, the isolated research on automatic error compensation and its combination with algorithms does not start with the characteristics of contour accuracy in data processing. Therefore, based on the advantages of strong the robustness of the fuzzy algorithm and the high effectiveness of parameter adjustment, an automatic compensation method for NC machining contour error based on fuzzy control is proposed. The contour error prediction model is designed according to the machining path, and then the automatic compensation strategy for contour error under fuzzy control is designed based on the feed speed. The results showed that under this method, the contour error can reach a maximum of 0.06 and a minimum of 0.025, which was 0.015 lower than the minimum contour error of genetic algorithm. This indicated that the method greatly reduced the CNC machining contour error and improved the contour accuracy, as well as reducing the time cost of contour error compensation, improving the efficiency of contour error compensation, and realizing the automation of error compensation control capability. This is helpful for advancing CNC machining automation technology and supporting the intelligent development of machinery manufacturing.

The geometric state of a workpiece is changing dynamically during the machining process. Such geometric changes lead to changes of the rigidity of workpiece features. Knowing the feature rigidity during the machining process is very crucial for the selection of right cutting parameters especially for aircraft structural parts with complex structures. This issue has rarely been addressed in the literature for cutting parameter optimization. In order to address this issue, an interim feature concept is introduced and the interim feature is modeled by using geometric information of final state and machining process information for aircraft structural parts. The interim feature rigidity is evaluated by simplifying the structures of interim feature. By considering the constraints of the interim feature rigidity, machine tool characteristics, and cutting tool performance, the cutting parameters are optimized using a genetic algorithm. A prototype system is developed based on the proposed approach, and a case study is performed. The experiment results show that the machining efficiency can be improved by 26 % after cutting parameter optimization, while the same machining quality is maintained.

Process parameters optimization is an important problem in numerical control machining, through the analysis of various factors affecting the cutting effect in cutting process, a mathematical model of cutting parameter optimization in NC machining is established and the constraint conditions are also determined in the paper. The article puts forward using genetic algorithm to realize the optimization of mathematical model, and the optimization analysis results are verified in practical processing. The experimental results show that the optimized cutting parameters can satisfy machining requests and improve the cutting efficiency.

Virtual machining allows simulation of the machining process by realistically representing kinematic, static and dynamic behaviour of the intended machine tools. Using this method, manufacturing-related issues can be brought to light and corrected before the product is physically manufactured. Machining systems utilised in the manufacturing processes are represented in the virtual machining environment, and there is a plethora of commercial virtual machining software used in the industry. Each software system has a different focus and approach towards virtual machining; more than one system may be needed to complete machining verification. Thus, the significant increase in the use of virtual machining systems in the industry has increased the need for information reusability. Substantial time and money has been put into the research of virtual machining systems. However, very little of this research has been deployed within industrial best practice, and its acceptance by the end user remains unclear. This article reviews current research trends in the domain of virtual machining and also discusses how much of this research has been taken on board by software vendors in order to facilitate machine tool information reusability. The authors present use cases which utilise the novel concept of machining capability profile and the emerging STEP-NC compliant process planning framework for resource allocation. The use cases clearly demonstrate the benefits of using a neutral file format for representing machining capability profiles, as opposed to remodelling and/or reconfiguring of this information multiple times for different scenarios. This article has shown through the use cases that machining capability profiles are critical for representing recourse information from a kinematic, static and dynamic perspective that commercial software vendors can subsequently use. The impact of this on mainstream manufacturing industry is potentially significant as it will enable a true realisation of interoperability.

Cutting parameters play an essential role in the economics of machining. In this paper, particle swarm optimization (PSO), a novel optimization algorithm for cutting parameters optimization (CPO), was discussed comprehensively. First, the fundamental principle of PSO was introduced; then, the algorithm for PSO application in cutting parameters optimization was developed; thirdly, cutting experiments without and with optimized cutting parameters were conducted to demonstrate the effectiveness of optimization, respectively. The results show that the machining process was improved obviously.

NC machine plays an irreplaceable role in the modern manufacturing because of its high machining processing accuracy, quality stable, flexibility. Through using the Renishaw ML10 laser interferometer detect the positioning accuracy and repositioning accuracy of X axis and Z axis of the HTC20 series of NC machine tools. According to the detection result compensate NC system to meet the machining accuracy requirement. The result shows that the error compensation of NC system is a effective method to improve the position accuracy of NC machine.

An increasing number of new multicore server systems use the Non-uniform Memory Access (NUMA) architecture due to its scalable memory performance. However, the multicore NUMA systems introduce complex interplay among data locality, contention on shared resources, and inter-socket data sharing overhead, which makes the achievement of an optimal and predictable system performance difficult. Virtualization technology further complicates the scheduling problem. In this paper, we first investigate the impact of virtual machine (VM) scheduling on multicore NUMA systems. Then, we analyze different VM mapping combinations and find that the best VM scheduling strategy does not only depend on the data sharing and memory characteristics of the VM itself, it is also impacted by the dynamic behavior of co-running VMs.

Simulation is a common approach for assisting system design and optimization. For system-wide optimization, energy and computational resources are often the two most critical limitations. Modeling energy-states of each hardware component and time spent in each state is needed for accurate energy and performance prediction. Tracking software execution in a realistic operating environment with properly modeled input/output is key to accurate prediction. However, the conventional approaches can have difficulties in practice. First, for a complex system such as an Android smartphone, building a cycle-accurate simulation environment is no easy task. Secondly, for I/O-intensive applications, a slow simulation would significantly alter the application behavior and change its performance profile. Thirdly, conventional software profiling tools generally do not work on simulators, which makes it difficult for performance analysis of complicated software, e.g., Java applications executed by the Dalvik virtual machine. Recently, virtual machine technologies are widely used to emulate a variety of computer systems. While virtual machines do not model the hardware components in the emulated system, we can ease the effort of building a simulation environment by leveraging the infrastructure of virtual machines and adding performance and power models. Moreover, multiple sets of the performance and energy models can be selectively used to verify if the speed of the simulated system impacts the software behavior. Finally, performance monitoring facilities can be integrated to work with profiling tools. We believe this approach should help overcome the aforementioned difficulties. We have prototyped a framework and our case studies showed that the information provided by our tools are useful for software optimization and system design for Android smartphones.

Geometric error is the major part of the inaccuracy of NC machine tools. This paper presents an integrated geometric error measuring, modeling and compensation method for NC machine tools. 21 geometric errors are anlysised. Taking three-axis machine tool as example, the positioning error is measured by a laser interferometer. A new method is proposed using MATLAB to fit the curve measured. The efficient methodology is developed and described for the establishment of the geometric error model. Finally, the compensation mechanism is executed based on the model. The experimental results show that the geometric accuracy of the machine tool is significantly improved by the error compensation.

A virtual reality machine shop environment was developed and integrated with a graphical model for the calculation of quantitative data affecting the roughness of a machined surface. During the machining process simulation in the virtual environment, material removal and milling machine axes kinematics are visualized in real time and qualitative parameters related to the process feasibility are evaluated. The model determines the machined surface topomorphy as a cloud of points retrieved from the visualization system Z buffer and surface roughness is calculated. The current study is focused on the methodology for the verification of the quantitative data acquired by the system. Results were verified with data determined in cutting experiments and by another numerical model that was integrated into the system. The results were found to be in agreement with both the numerical model and the experiments.

The relative position and orientation deviation between grinding wheel and workpiece caused by geometric errors affect the machining accuracy of five-axis CNC gear profile grinding machine tools directly. Therefore, geometric error modeling and compensation are presented according to homogeneous transformation and differential motion matrix based on the multi-body system theory for the accuracy enhancement of the machine tools. Firstly, the open kinematic chain and the ideal homogeneous transformation matrix from workpiece to grinding wheel are established according to the topological structure of the CFXZAY-type, five-axis gear profile grinding machine tools and the basic homogeneous transformation matrix between coordinate frames. Secondly, the homogenous transformation matrices of linear pairs and rotary pairs with geometric errors are calculated, and the relationships of the error propagation from workpiece to grinding wheel are acquired as well on the basis of 37 geometric error components analysis. The geometric error models including position and orientation errors of grinding wheel in workpiece coordinate system are obtained with matrix multiplication by using small-angle approximation and ignoring the second-order and high-order error terms. Then, the Jacobian is obtained by using transforming differential motion matrix of each motion axis to compensate the integrated error components of the grinding wheel, which can make the compensation effective and convenient with using the corresponding homogeneous transformation matrix. Finally, error measurement, error compensation, and machining experiments are carried out on a five-axis CNC gear profile grinding machine tool SKMC-1200W/10 to verify the applicability and effectiveness of the proposed error modeling, error compensation, and research approach.

In the field of parallel processing, there is a great diversity of languages and architectures which become obsolete at a rapid pace. In this environment, portability is an important issue. Unfortunately, most parallel languages are not portable. This portability problem can be solved using a virtual machine approach. In this approach, front-end translators translate various parallel source languages into code for a virtual machine. Back-end translators translate the virtual machine code into executable codes for a variety of parallel architectures. The Virtual Machine for Parallel Processing (VMPP) is designed to provide portability for a variety of high-level parallel programming languages without significantly sacrificing performance. >

Machining time is a major performance criterion when it comes to high-speed machining. CAM software can help in estimating that time for a given strategy. But in practice, CAM-programmed feed rates are rarely achieved, especially where complex surface finishing is concerned. This means that machining time forecasts are often more than one step removed from reality. The reason behind this is that CAM routines do not take either the dynamic performances of the machines or their specific machining tolerances into account. The present article seeks to improve simulation of high-speed NC machine dynamic behaviour and machining time prediction, offering two models. The first contributes through enhanced simulation of three-axis paths in linear and circular interpolation, taking high-speed machine accelerations and jerks into account. The second model allows transition passages between blocks to be integrated in the simulation by adding in a polynomial transition path that caters for the true machining environment tolerances. Models are based on respect for path monitoring. Experimental validation shows the contribution of polynomial modelling of the transition passage due to the absence of a leap in acceleration. Simulation error on the machining time prediction remains below 1%.

Downsizing energy consumption during the machining of metals is vital for sustainable manufacturing. As a prerequisite, energy consumption should be determined, through direct or indirect measurement. The manufacturing process of interest is the finish turning which has been explored to generate (near) net shapes, particularly for hardened steels. In this paper, we propose using measured cutting forces to calculate the electrical energy consumption during the finish turning process of metals where typically the depth of cut is lower than the cutting tool nose radius. In this approach, the resultant cutting force should be used for calculating the energy consumption, instead of only the main (tangential) cutting force as used in the conventional approach. A case study was carried out where a hardened stainless steel (AISI 420, hardness of 47–48 HRC) was turned using a coated carbide tool, with a nose radius of 0.8 mm, without cutting fluid, and at 0.4 mm depth of cut. The experimental design varied the cutting speed (100, 130, and 170 m/min) and feed (0.10, 0.125, and 0.16 mm) while other parameters were kept constant. The results indicate that the electrical energy consumption during the particular dry turning of hardened steel can be calculated using cutting force data as proposed. This generally means machining studies that measure cutting forces can also present energy consumption during the finish or hard turning of metals, without specifically measuring the power consumption of the machining process. For this particular dry turning of hardened stainless steel, cutting parameters optimization in terms of machining responses (i.e., low surface roughness, long tool life, low cutting force, and low energy consumption) was also determined to provide an insight on how energy consumption can be integrated with other machining responses towards sustainable machining process of metals.