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Virtual machining environment in Vericut was proposed with purpose of enhancing process planning practice training for undergraduates. Main issues include construction of virtual manufacturing resource library and virtual machining process execution environment for planned process. Virtual manufacturing resource library is composed of virtual and digital models for diverse kinds of machine tools. Type determination and classification principle of machine tools and virtual equipment modeling method in the proposed environment are studied. Virtual machining for particular process by multi setup and cut stock transfer method are defined. The proposed Virtual machining process execution environment provides effective ways for enrolled undergraduates applying machining knowledge in process planning, gaining experience with advanced machine tools and manufacturing technology in industrial world.

Smart manufacturing becomes a major trend of manufacturing industry in the context of Industry 4.0. The integration of physical manufacturing machines and digitized virtual counterparts is promoted by emerging concepts and technologies of Digital Twin. Aiming to seamlessly integrate the cyberspace and physical world by constructing a bi-directional mapping system, Digital Twin can highly improve the user experience and production efficiency in smart manufacturing. So far little attention has been paid to the mapping from the cyberspace to the physical world in Digital Twin. Without this mapping, operations on the digitized virtual machines are incapable of working on the physical ones, which actually limits the applicability of Digital Twin to more manufacturing processes. In addition, the traditional 2D interactive interface in the cyberspace is limited in visualizing the large amount of digital data and providing concise information to improve the operation efficiency. To optimize the conventional Digital Twin mapping system, this paper proposes a modular-based Digital Twin system for smart manufacturing, where the bi-directional real-time mapping of the cyber–physical space is established through socket communication. Moreover, the proposed Digital Twin system aggregates the functions of remote control and virtual machining using virtual reality and augmented reality. These two essential functions are designed to provide an immersive and friendly operating environment as well as a vivid preview of machining outcomes to improve production efficiency, minimize machining cost, and avoid potential risks. The feasibility and effectiveness of the proposed Digital Twin system are demonstrated by implementing the system on a CNC milling machine where the control latency and virtual machining accuracy are verified. The proposed Digital Twin system can be utilized as an essential part of smart manufacturing, having high potential to be applied to various industrial machines and smart systems.

With intelligent manufacturing development, applying adaptive control technology in the machining process is an effective way to increase productivity and quality. However, adaptive control alone cannot control cutting forces effectively when cutting conditions have excessive change. In this study, a digital twin of the milling process is introduced to cutting force adaptive control for system robustness and efficiency. The cutting force is indirectly measured based on the feed drive current using a Kalman filter, and unknown parameters in the estimation model are identified. A virtual machining system model is established based on online data communication and geometric operation. In addition, the machining state is predicted and introduced into the adaptive control algorithm based on the integrated digital twin for cutting force constraint control. Finally, rough milling of an S-shape specimen is carried out as the cutting experiment to verify the credibility and efficiency of the digital twin-driven cutting force adaptive control.

In the Industry 4.0 era, the modelling of machining operations happens to be a crucial aspect of production sector. With adequate models, predicting the appearance of chatter and selecting optimised operational parameters is possible. For the dynamics simulation of machine tools or robots performing 5-axis operations, modelling approaches are continuously in improvement. A robust method is proposed for the cutter-workpiece engagement (CWE) computation at each step of a dynamic simulation, by determining the machining forces as well as the resulting machined surface. The CWE is estimated based on the interference between the workpiece, modelled with tri-dexel approach, and the tool, considered as a triangle-mesh surface of the swept volume. The relative closest triangle algorithm is used for a robust intersection management, suited for 5-axis trajectories. A hybrid dexel-based-analytic method is presented for accurate estimation of the uncut chip thickness. Furthermore, an approach is proposed for a simulation-based evaluation of the part resulting from dynamic simulations by comparing dexel networks with each other. It allows to assess the impact of operational parameters on parts at the simulation level. The CWE determination method proposed is validated with experimental data from force measurements and benchmark tests of different scales from macro- to micro-milling.

One of the many definitions of \"cloud\" is that of an infrastructure-as-a-service (IaaS) system, in which IT infrastructure is deployed in a provider's data center as virtual machines. With IaaS clouds' growing popularity, tools and technologies are emerging that can transform an organization's existing infrastructure into a private or hybrid cloud. OpenNebula is an open source, virtual infrastructure manager that deploys virtualized services on both a local pool of resources and external IaaS clouds. Haizea, a resource lease manager, can act as a scheduling back end for OpenNebula, providing features not found in other cloud software or virtualization-based data center management software.

The success of machining process automation hinges primarily on the effectiveness of the monitoring and adaptive control systems. A new digital twin–based process monitoring method and system in small batch machining is presented, and the cutting model is integrated into the monitoring method to improve the diagnosis accuracy. Model-based and signal-based monitoring indicators are developed, and indicators of the residual force component for the tool wear monitoring and energy ratio for the chatter detection are introduced to the digital twin–based monitoring. The critical monitoring algorithm is verified in two cases: tool wear monitoring and chatter detection. The results show that the method proposed can accurately evaluate the percentages of the residual useful life of the tool and the chatter. Moreover, a new process monitoring system in small batch machining is developed by integrating the advanced algorithm. This study can provide a critical reference for intelligent machining monitoring and control in industry.

Program comprehension is an important activity in software maintenance, as software must be sufficiently understood before it can be properly modified. The study of a program's execution, known as dynamic analysis, has become a common technique in this respect and has received substantial attention from the research community, particularly over the last decade. These efforts have resulted in a large research body of which currently there exists no comprehensive overview. This paper reports on a systematic literature survey aimed at the identification and structuring of research on program comprehension through dynamic analysis. From a research body consisting of 4,795 articles published in 14 relevant venues between July 1999 and June 2008 and the references therein, we have systematically selected 176 articles and characterized them in terms of four main facets: activity, target, method, and evaluation. The resulting overview offers insight in what constitutes the main contributions of the field, supports the task of identifying gaps and opportunities, and has motivated our discussion of several important research directions that merit additional consideration in the near future.

Variation propagation modelling in multistage machining processes through use of analytical approaches has been widely investigated for the purposes of dimension prediction and variation source identification. Yet the variation prediction of complex features is non-trivial task to model mathematically. Moreover, the application of the variation propagation approaches and associated variation source identification techniques using Skin Model Shapes is unclear. This paper proposes a multilayer shallow neural network regression approach to predict geometrical deviations of parts given manufacturing errors. The neural network is trained on a simulated data, generated from machining simulation of a point cloud of a part. Further, given a point cloud data of a machined feature, the source of variation can be identified by optimally matching the deviation patterns of the actual surface with that of shallow neural network generated surface. To demonstrate the method, a two-stage machining process and a virtual part that has planar, cylindrical and torus features was considered. The geometric characteristics of machined features and the sources variation could be predicted at an error of 1% and 4.25%, respectively. This work extends the application of Skin Model Shapes in variation propagation analysis in multistage manufacturing.

Virtual simulation of high-resolution multi-axis machining processes nowadays plays an important role in the production of complex parts in various industries. In order to improve the surface quality and productivity, process parameters, such as spindle speed, feedrate, and depth of cut, need to be optimized by using an accurate process model of milling, which requires both the fast virtual prototyping of machined part geometry for tool path verification and accurate determination of cutter–workpiece engagement for cutting force predictions. Under these circumstances, this paper presents an effective volumetric method that can accurately provide the required geometric information with high and stable computational efficiency under the condition of high grid resolution. The proposed method is built on a tri-level grid, which applies two levels of adaptive refinement in space decomposition to abolish the adverse effect of a large fine-level branching factor on its efficiency. Since hierarchical space decomposition is used, this multi-level representation enables the batch processing of affected voxels and minimal intersection calculations, achieving fast and accurate modeling results. To calculate the instantaneous engagement region, the immersion angles are obtained by fusing the intersection points between the bottom-level voxel edges and the cutter surface, which are then trimmed by feasible contact arcs determined using envelope theory. In a series of test cases, the proposed method shows higher efficiency than the tri-dexel model and stronger applicability in high-precision machining than the two-level grid.

The simulation of subtractive manufacturing processes has a long history in engineering. Corresponding predictions are utilized for planning, validation and optimization, e.g., of CNC-machining processes. With the up-rise of flexible robotic machining and the advancements of computational and algorithmic capability, the simulation of the coupled machine-process behaviour for complex machining processes and large workpieces is within reach. These simulations require fast material removal predictions and analysis with high spatial resolution for multi-axis operations. Within this contribution, we propose to leverage voxel-based concepts introduced in the computer graphics industry to accelerate material removal simulations. Corresponding schemes are well suited for massive parallelization. By leveraging the computational power offered by modern graphics hardware, the computational performance of high spatial accuracy volumetric voxel-based algorithms is further improved. They now allow for very fast and accurate volume removal simulation and analysis of machining processes. Within this paper, a detailed description of the data structures and algorithms is provided along a detailed benchmark for common machining operations.