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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 ma...
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Multi-image super-resolution (MISR) usually outperforms single-image super-resolution (SISR) under a proper inter-image alignment by explicitly exploiting the inter-image correlation. However, the large computational demand encumbers the deployment of MISR methods in practice. In this work, we propose a distributed optimization framework based on d...
Citations
... The xDT offers significant potential. Combining first principle predictions of process forces with a multi-body robot model and online calibration technologies allows the prediction of expected deflections of the robot and spindle [14] in real-time, i.e., faster than the typical control cycle time in the order of ms. Thus, they can be compensated in the control cycle [15]. ...
The Digital Twin, a virtual representation of a physical object that mimics its structure and behavior to inform decisions and optimize operational efficiency, is an established paradigm in industry. While Modelling, Simulation, and Optimization have been a standard practice in industry since long, the complementary role of models and data as well as a holistic and life cycle spanning approach distinguishes the Digital Twin paradigm. However, albeit nearly every industry is highlighting the potential and strategic importance of Digital Twins, they are by far not at a level of industrial practice as publicity suggests. A major hurdle is the effort associated with creating actionable and impactful Digital Twins. The reasons are multi-fold but novel algorithms and mathematical concepts will be key to overcome many of these. Within this article, we review the concept of the Digital Twin, major challenges of fostering its adoption, and highlight how research in Applied and Industrial Mathematics is key to address corresponding roadblocks.
... In [12], this is applied directly to an industrial robot to guide the optimal workpiece placement for dimensional compensation of a milling task. Similarly, [23] employs a novel voxel-based simulation approach for dimensional compensation. However, all of these methods still require CAD models of the desired workpiece. ...
... The workpiece was modelled as a heightfield data array interpolated across a bivariate cubic spline surface. Such an approach is chosen over methods with greater complexity and accuracy to minimise computational overhead, as such approaches are prevalent in literature [17], [23], and the focus of the current work is on learning a generalised control policy over a domain of materials, not on modelling the resulting workpiece geometry / tolerances. Tool paths were generated as NURBS (Non-Uniform Rational B-Spline) curves c(t). ...
This paper addresses the problem of robotic cutting during disassembly of products for materials separation and recycling. Waste handling applications differ from milling in manufacturing processes, as they engender considerable variety and uncertainty in the parameters (e.g. hardness) of materials which the robot must cut. To address this challenge, we propose a learning-based approach incorporating elements of interaction control, in which the robot can adapt key parameters, such as feed rate, depth of cut, and mechanical compliance during task execution. We show how a mathematical model of cutting mechanics, embedded in a simulation environment, can be used to rapidly train the system without needing large amounts of data from physical cutting trials. The simulation approach was validated on a real robot setup based on four case study materials with varying structural and mechanical properties. We demonstrate the proposed method minimises process force and path deviations to a level similar to offline optimal planning methods, while the average time to complete a cutting task is within 25% of the optimum, at the expense of reduced volume of material removed per pass. A key advantage of our approach over similar works is that no prior knowledge about the material is required.
... [11] Since overall optimization is largely based on problem formulation, emphasis has been placed on defining optimization criteria as crucial elements to represent performance. [12] In order to optimize manufacturing with energy efficiency is that it resorted to an immune system enabled by Big Data, programming is a critical stage to minimize the life cycle cost in manufacturing. [13] The virtual factory concept is presented as the vehicle for modeling and simulation of manufacturing where a path is proposed for its implementation that is based on technology developments and standards for the analysis of manufacturing data. ...
... [11] improved surface roughness through the application of the automated refrigerant supply system. [12] Energy savings of around 30% and improved productivity of more than 50% have been achieved by adopting I2 S in factories. ...
... On the other hand, the article [10] obtained surface roughness by applying the automatic coolant supply system, but a limitation found was that lubrication also helps to prevent chips from sticking to the tool, so, if this happened, the chips would hinder the subsequent cutting. For Articles [12] and [16] energy savings were achieved, but in [12] some 30% were achieved and productivity improved by more than 50%, but the mechanism implemented is rigid, which hinders the robustness and precision of the immune process and finally in the article [24] a greater lifetime was obtained to the used equipment which reduces the expenses in machineries. ...
... where F t,r,a are the instantaneous forces in the tangential, radial and axial direction respectively, h is the instantaneous uncut chip thickness and a is the axial depth of cut, K (t,r,a)c are the empirical parameters that represent the forces generated by the mechanical shearing of the metal and K (t,r,a)e are empirical parameters that represent the forces generated by all sources of friction independent of the cutting action such as ploughing and edge rubbing [16]. Linear mechanistic models have shown good results when applied to discretized workpieces, as shown by the work of Schnös et al. [17] and Meirs et al. [18]. In both papers a linear mechanistic model is applied to a voxelized approximation of a known CAD-CAM tool path to accurately estimate the machining forces generated. ...
This paper presents a workpiece discretization method to apply existing cutting force models to predict the forces generated during low material removal rate robotic machining operations of features with arbitrary geometry. Two machining operations along a straight edge which are modelled using this feature discretization method are shown, a chamfer pass on a sharp corner and the removal of a trapezoidal cross section. The workpiece features are measured using a high-resolution laser profile scanner to obtain the volume of the features to be removed. The identified features are discretized into rectangular sections such that the cutting force models can be applied to predict the cutting forces. A linear and an exponential mechanistic model which relate tool immersion and feed rate to the cutting force are applied to the scanned workpiece features. The linear and nonlinear models show good agreement with the measured data, with the exception that the linear model occasionally over predicts the forces depending on the radial depth of cut.
In recent years, matrix‐free conjugate gradient (CG) solvers with graphics processing unit (GPU) acceleration have been effectively used to reduce the execution timings of finite element method (FEM)‐based engineering simulations. However, there is not much in the literature that discusses the application of matrix‐free CG solvers for elastoplasticity. The primary challenge is the presence of both elastic and plastic states, which introduce branching issues in the parallel computation of sparse matrix‐vector multiplication (SpMV). The current work proposes an efficient split kernel strategy for elastoplasticity that segregates elements in elastic and plastic zones and allows the application of standard GPU‐based matrix‐free SpMV strategies in the literature to elastoplastic simulation. The proposed strategy avoids branching issues, facilitates coalesced memory access, allows efficient usage of on‐chip memory, and uses minimum storage to realize large‐scale simulation on a GPU with limited memory. We also propose matrix‐free SpMV strategies for GPU implementation based on an element‐by‐element technique that makes efficient use of memory hierarchy available in a GPU. The computational efficiency of the proposed strategies is demonstrated over three large‐scale benchmark examples from elastoplasticity, and the performance results are compared with GPU optimized node‐based and degrees of freedom (DOF)‐based matrix‐free strategies. The results show that the splitting of computation is most suitable for low plasticity problems, where most of the matrix‐free strategies show significant speedups with respect to single kernel strategy for elastoplasticity. The proposed element‐by‐element strategy using node‐wise thread allocation is found to have the best performance, achieving up to 7.16 and 3.35 speedup over node‐based and DOF‐based strategy, respectively. For problems with higher amounts of plasticity, the proposed strategies perform slightly better and achieve modest speedups due to advantages in the initial stages of Newton iterations. In terms of wall‐clock timings, the proposed matrix‐free solver is found to be up to 2 faster than GPU optimized assembly‐based elastoplasticity solver.
This paper addresses the problem of robotic cutting during disassembly of products for materials separation and recycling. Waste handling applications differ from milling in manufacturing processes, as they engender considerable variety and uncertainty in the parameters (e.g. hardness) of materials which the robot must cut. To address this challenge, we propose a learning-based approach incorporating elements of interaction control, in which the robot can adapt key parameters, such as feed rate, depth of cut, and mechanical compliance during task execution. We show how a mathematical model of cutting mechanics, embedded in a simulation environment, can be used to rapidly train the system without needing large amounts of data from physical cutting trials. The simulation approach was validated on a real robot setup based on four case study materials with varying structural and mechanical properties. We demonstrate the proposed method minimises process force and path deviations to a level similar to offline optimal planning methods, while the average time to complete a cutting task is within 25% of the optimum, at the expense of reduced volume of material removed per pass. A key advantage of our approach over similar works is that no prior knowledge about the material is required.
Note to Practitioners
—This work is motivated by challenges in emerging fields such as recycling of electric vehicles, where products such as batteries adopt a range of designs with varying physical geometry and materials. More generally, this applies when considering robotic disassembly of any unknown component where semi-destructive operations such as cutting are required. Product-to-product variation introduces challenges when planning cutting processes required to disassemble a component, as contemporary planning approaches typically require advance knowledge of the material properties, shape and desired path to select tool speed, feed and depth of cut. In this paper, we show a mathematical model of milling force embedded in a simulation environment can be used as a relatively inexpensive approach to simulate a broad spectrum of cutting processes the robot may encounter. This allows the robot to learn from experience a strategy that can select these key parameters of a milling task online without user assistance. We develop a framework for controlling a robot using this strategy that allows the stiffness of the robot arm to be modulated over time to best satisfy metrics of productivity (e.g. required cutting time), while maintaining safe interaction of the robot with its environment (e.g. by avoiding force limits), similarly to how a human operator can vary muscular tension to accomplish different tasks. We posit that the proposed method can substitute a trial-and-error strategy of selecting process parameters for disassembly of novel products, or integrated with existing planning approaches to adjust the parameters of milling tasks online.
The assembly interference matrix is a foundational information model for assembly process planning such as assembly sequence and assembly path planning, and supports digital assembly simulation, intelligent assembly, digital twin-based assembly, and so on. The assembly interference matrix represents two parts which are collision or not when they move along specific directions. Traditional assembly interference matrix construction adopts geometric collision detection on the approximate swept volume of parts which are composed by moving step by step. This not only leads to a heavy computational load because of the small moving steps but it is also easy to misjudge the contact as collision as well as missing collision due to discrete spatial movement of the part. To overcome these drawbacks, this work equivalent the collision detection of parts during the establishment of the assembly interference matrix as visibility judgment, and develops an image-assisted calculation method to process the collision between the part’s swept volume as the occlusion of images via computer graphics rendering. This method is implemented on OpenGL to ensure its generalizability, and the implicit calculation is completed in a computer graphics rendering process by setting proper rendering conditions by adopting depth testing and stencil testing. Furthermore, the erosion operation of the image process is employed to distinguish whether contact or collision. Lastly, an example for a gear box assembly confirms the effectiveness of this method.
