Search

The effect of CO2 laser drilling on glass microballoon/epoxy syntactic foams are investigated in this study to optimize machining parameters to achieve a clean hole for various industrial applications. The epoxy matrix is reinforced with glass microballoons in concentrations of 0, 20 and 40 vol%. Cutting speed, laser power and additive percentage are input parameters for optimization. Kerf taper angle, surface roughness and ovality percentage are used as output responses to evaluate hole quality. For the optimization study, hybrid multi-criteria decision-making methods such as grey relational analysis and multi-objective optimization with ratio assessment methods are used, with equal weightage given to each output response. According to the study, low power and high speed produce better machining results such as a smaller kerf taper angle, lower surface roughness and a lower ovality percentage. Furthermore, a higher additive percentage is not appropriate for laser in epoxy/glass microballoon composite because it burns the area near the laser and increases surface roughness. © 2023 The Author(s)

The effect of CO2 laser drilling on glass microballoon/epoxy syntactic foams are investigated in this study to optimize machining parameters to achieve a clean hole for various industrial applications. The epoxy matrix is reinforced with glass microballoons in concentrations of 0, 20 and 40 vol%. Cutting speed, laser power and additive percentage are input parameters for optimization. Kerf taper angle, surface roughness and ovality percentage are used as output responses to evaluate hole quality. For the optimization study, hybrid multi-criteria decision-making methods such as grey relational analysis and multi-objective optimization with ratio assessment methods are used, with equal weightage given to each output response. According to the study, low power and high speed produce better machining results such as a smaller kerf taper angle, lower surface roughness and a lower ovality percentage. Furthermore, a higher additive percentage is not appropriate for laser in epoxy/glass microballoon composite because it burns the area near the laser and increases surface roughness. © 2023 The Author(s)

The effect of CO2 laser drilling on glass microballoon/epoxy syntactic foams are investigated in this study to optimize machining parameters to achieve a clean hole for various industrial applications. The epoxy matrix is reinforced with glass microballoons in concentrations of 0, 20 and 40 vol%. Cutting speed, laser power and additive percentage are input parameters for optimization. Kerf taper angle, surface roughness and ovality percentage are used as output responses to evaluate hole quality. For the optimization study, hybrid multi-criteria decision-making methods such as grey relational analysis and multi-objective optimization with ratio assessment methods are used, with equal weightage given to each output response. According to the study, low power and high speed produce better machining results such as a smaller kerf taper angle, lower surface roughness and a lower ovality percentage. Furthermore, a higher additive percentage is not appropriate for laser in epoxy/glass microballoon composite because it burns the area near the laser and increases surface roughness. © 2023 The Author(s)

Machining centers are complex equipments that demand high energy consumption affecting the production costs. Different studies have related some factors with the energy consumption, however it is not completely clear which variables or factors have a greater incidence in this energy consumption of these machining centers without decreasing the quality of the finishing surfaces of the manufactured parts. In the present study, an analysis of the influence of cutting depth, forward velocity and spindle speed on the energy consumption and roughness of the surface finish was carried out in the Haas UMC-750 and the Leadwell V-40iT® machining centers. An experiment was developed with an nK factorial design keeping some variables as constants such as the base material, the cutting tool and the machining path. With the results obtained, it was possible to identify that the forward velocity is the factor that depends the most on energy consumption. Besides, the interaction between the factors was evidenced, identifying that the cutting depth has a moderate influence on the roughness or surface finish of the manufactured parts. The energy consumption varies for each machining center and the experimental design developed helped to characterize the influence of some cutting variables on the energy consumption of each machining center., Los centros de mecanizado demandan un alto consumo de energía afectando los costos de producción en las empresas que los emplean. Diferentes estudios relacionan múltiples factores de los centros de mecanizado con el consumo de energía, pero hasta el momento no se identifica con claridad cuáles variables o factores tienen una mayor incidencia en el consumo de energía sin disminuir la calidad del acabado superficial de las piezas fabricadas. En el presente estudio se realizó un análisis descriptivo de la influencia que tiene la profundidad de corte, la velocidad de avance y la velocidad del husillo sobre el consumo de energía y la rugosidad en los centros de mecanizado Haas UMC-750 y el centro Leadwell V-40iT®. Se desarrolló una experimentación con un diseño factorial tomando como factores fijos el material base, la herramienta de corte y la ruta de mecanizado. A partir de un análisis descriptivo de los resultados obtenidos en este experimento se logró identificar que la velocidad de avance es el factor que más relación tiene con el consumo de energía. Además, se evidenció la interacción entre los factores identificando que la profundidad de corte posee una influencia moderada sobre la rugosidad o acabado superficial de las piezas. El consumo de energía varía para cada centro de mecanizado y el diseño experimental desarrollado ayudó a caracterizar la influencia de algunas variables de corte sobre el consumo de energía en cada centro de mecanizado.

The mathematical modelling of variation propagation in multistage machining processes helps to perform a quick analysis and diagnosis of the processes. The models for part quality prediction, such as Stream of Variation, include homogeneous transformations of the vectorial representations of parts and fixtures. However, these prediction models are complex when considering fixtures with locating surfaces and the associated matrix size is large. Towards mitigating the mathematical complexity, dual quaternions are proposed in representing and transforming a virtual part and fixture. To achieve this, the primary feature datum is assembled to the primary locating surface, followed by sliding the part to secondary and tertiary locating surfaces by reducing the distance between the vertices of the part and the locating surface. The prediction following the proposed approach gave a result within 0.36 % of the prediction made using CAD/CAM models and maintained the largest matrix size of 9 by 8 for a part with 9 features., QC 20220913

Aircraft structural parts are important and high-value parts used to constitute the frame of the aircraft, and are usually produced by NC machining, where the machining parameters are significant for the machining quality, efficiency, and cost. In the process planning, there are hundreds or even thousands of machining operations that require separate machining parameters, which is a huge task for the existing optimization-based methods that rely on iterative optimizations. Due to the complex structures and high requirements, the existing expert system-based methods require plenty of additional modifications. Recently, with the development of artificial intelligence, data-driven methods are used in machining parameter planning, which mines the knowledge and rules hidden in the historical data. However, the existing data-driven models require a large amount of training data and lack interpretability. To address this issue, this paper proposes an informed machine learning method for machining parameter planning, which introduces multiple prior constraints into the data-driven model. First, the part model is represented as an attribute graph, and the cutting area of each machining operation is correlated to a subgraph, which is used to obtain the vectorized representation of machining operation that covers cutting area and process information. Then, by fitting the mapping between the vectorized machining operation and the machining parameters, the knowledge and rules are learned. Next, to introduce prior constraints into the data-driven model, the constraint loss is designed and incorporated into the original loss function. The proposed method can generate machining parameters for all the machining operations in batch, thereby greatly reducing the human interactions. In the case study, the historical processing files of aircraft structural parts are used to train the proposed model for planning cutting width, cutting depth, spindle speed, and machining feedrate. The res, QC 20220825

Deep rolling is a machining process which is used to decrease roughness and to induce compressive residual stresses into component surfaces. A recent publication of this research group showed possibilities to predict the topography during deep rolling of bars in a lathe. Although deep rolling can be used in a milling machine to machine flat specimens, it is still unclear, whether the topography can be predicted to a similar extend using this application. To investigate the influence of the machining parameters on topography, three experimental stages are performed in this paper on cast AlSi10Mg. First, single-track deep rolling experiments are performed under variation of the deep rolling pressure pw to find the relationship between pw and the indentation geometry. Here, a logarithmic relationship between deep rolling pressure and the indentation characteristics could be found that achieved a relatively high agreement. In the second stage, surfaces are prepared using multi-track deep rolling. Here, the deep rolling pressure pw and the lateral displacement ab are varied. The multi-track rolled surfaces were compared to an analytical model for the calculation of the theoretical roughness that is based on the logarithmic relationship found in the first experimental stage. Here, the limits of the analytical prediction were shown because high similarities between predicted and measured surfaces only occurred for certain deep rolling pressures pw and lateral displacements ab. To further investigate the limitations of this procedure, a novel tool concept, which utilizes the rotation of the machine spindle, is used in the third stage. Here, the generated surface can also be interpreted as a periodic sequence of spheric indentations as shown in the second experimental stage, whereas the measured surfaces differed from the expected surfaces. As a result of this paper, the predictability of the surface topography after deep rolling of flat specimens is known (minimum pressure

Deep rolling is a machining process which is used to decrease roughness and to induce compressive residual stresses into component surfaces. A recent publication of this research group showed possibilities to predict the topography during deep rolling of bars in a lathe. Although deep rolling can be used in a milling machine to machine flat specimens, it is still unclear, whether the topography can be predicted to a similar extend using this application. To investigate the influence of the machining parameters on topography, three experimental stages are performed in this paper on cast AlSi10Mg. First, single-track deep rolling experiments are performed under variation of the deep rolling pressure pw to find the relationship between pw and the indentation geometry. Here, a logarithmic relationship between deep rolling pressure and the indentation characteristics could be found that achieved a relatively high agreement. In the second stage, surfaces are prepared using multi-track deep rolling. Here, the deep rolling pressure pw and the lateral displacement ab are varied. The multi-track rolled surfaces were compared to an analytical model for the calculation of the theoretical roughness that is based on the logarithmic relationship found in the first experimental stage. Here, the limits of the analytical prediction were shown because high similarities between predicted and measured surfaces only occurred for certain deep rolling pressures pw and lateral displacements ab. To further investigate the limitations of this procedure, a novel tool concept, which utilizes the rotation of the machine spindle, is used in the third stage. Here, the generated surface can also be interpreted as a periodic sequence of spheric indentations as shown in the second experimental stage, whereas the measured surfaces differed from the expected surfaces. As a result of this paper, the predictability of the surface topography after deep rolling of flat specimens is known (minimum pressure

Increasing precision requirements motivate research on the system perspective of machining systems. Virtual environments for the prediction of the Process Machine Interaction (PMI) are one key to obtain a priori understanding of machining accuracy, save expensive testing and planning time of industrial users, and increase the overall resource utilization. This paper introduces the modular node-based software platform SharpCut (#cut). The platform follows a sequential modeling approach of manufacturing processes and machine tools by using nodes. The novelty of the method lies in its open architecture with a strong focus on extendibility, adaptability, and research applicability. Besides the node-based modeling approach, four implemented software modules are described. The four modules are the workpiece discretization, the tool and cutting inserts, the material removal, and the system's kinematics. The machine tool's motion and kinematic errors are modeled with Homogeneous Transformation Matrices (HTMs). The workpiece discretization is performed with a memory-efficient implementation of Depth Elements (Dexels). The material removal follows from the modelling of the Tool-Workpiece Engagement (TWE) with a generic model of the tool and the computationally efficient MÖLLER-TRUMBORE ray-triangle intersection algorithm. With the presented modules, it is possible to predict the results of machining processes and perform sensitivity or root cause analyses. The paper concludes with an example model and simulation of a face-milling process., Part of proceedings ISBN: 978-0-9957751-9-0QC 20220330

This work presents the development of the Particle Finite Element Method (PFEM) for the modelling of 3D solid mechanics problems under cutting conditions. The study and analysis of numerical models reproducing the cut of a material is a matter of interest in several areas; namely, the improvement of the material properties, the optimization of the process and tool geometries and the prediction of unexpected failures. The analysis of bi-dimensional (2D) models is the most common approach for different reasons. Just focusing on the simulation point of view, it is the simplest procedure, the cheapest in terms of computational cost and sometimes the only feasible numerical solution. However, many industrial machining processes, such as cutting, blanking, milling and drilling have not a possible simplification to 2D models. Actually even a simple turning processes for non-orthogonal cuts can not be simplified to 2D. This work present an upgrade of the PFEM techniques in order to deal with the 3D machining problems. We present recent improvements in the finite element formulation, the meshing re-connections and the contact detection. By applying these developments the PFEM has the capability for modelling a wide range of practical machining processes. In this paper the capacity of the formulation and the accuracy of the results are analyzed and validated with some representative examples. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

The growing global energy demand from industry results in significant ecological and economical costs. Aiming to decrease the impact of machining operations, an increasing number of research activities and publications regarding energy efficient machine tools and machining processes can be found in the literature. This keynote paper provides an overview of current machine- and process-related measures to improve the energy efficiency of metal cutting machine tools. Based on an analysis of the energy requirements of machine tool components, design measures to reduce the energy demand of main and support units are introduced. Next, methods for an energy efficient operation of machine tools are reviewed. Furthermore, latest developments and already available energy efficiency options in the machine tool industry are discussed. The paper concludes with recommendations and future research questions for more energy efficient machine tools.

Machining vibrations is a critical phenomenon in the industry as they negatively affect the quality and tool-life. One common avoidance strategy for machining vibrations is the fine-tuning of process parameters, thus leading to longer production time. Our research addresses this challenge and uses different streams of data to classify problematic processes. Data streams of machining parameters, tool position, loads, vibration sensors, together with process plan data and cutting tool usage information, are visualized. Experiments are performed to derive classification criteria. These results are then used to observe vibrations in a five-axis machining center for further process adjustment., QC 20210505

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. © 2020, The Author(s)., QC 20201201

Boring bars with tuned mass dampers have a passive damper tuned with respect to the frequency of the first bending mode of the tool. When the tool is clamped into the machine tool there is a stiffness loss that lowers the natural frequency of the bar compared to ideal clamping conditions. For large tools the difference can be more than 35%, depending on clamping structure, tool size and overhang. In this paper we investigate a simple two-degree-of-freedom model for the tool-machine interaction consisting of a bending mode coupled with a rotational stiff mode. The model gives good insight into the system behavior and fits well with measurements. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of The 17th CIRP Conference on Modelling of Machining Operations, open access

Geometric modelling is an established approach for gathering detailed knowledge about the chronological sequence of process conditions and for determining technological values of machining processes such as milling, turning, grinding or additive manufacturing. Performance and accuracy essentially depend on the chosen workpiece model and its parametrization. Furthermore, several influences on the investigated machine tool system lead to errors, which must be modeled separately. This paper shows approaches to increase performance and accuracy of the simulation by choosing an appropriate combination of different geometric representations of the workpiece and by considering possible errors within the kinematic model. Examples for different applications in metal cutting are given.

Multi-dexel based material removal simulations provide a fast and flexible way to compute process forces and tool deflections for milling and turning operations. This allows an advanced process planning including detection of collisions for complex toolpaths. However, using dexel simulations for designing cutting tools has rarely been investigated. Especially the position of individual cutting edges is not considered, because current approaches only subtract the sweep volume of the tool envelop instead of the rake face. This paper presents a new method to design cutting tools using material removal simulations and a detailed tool geometry representation. The discretization of the tool allows an efficient calculation of the engagement conditions of individual cutting edges. The method is used to optimize novel porcupine milling cutters with round indexeble inserts, which produces a geometry analogous to serrated end mills. Based on the calculated forces, the positions of individual indexable inserts are adjusted to minimize the maximum radial force. An optimum has been found that reduces radial force by 12% compared to conventional porcupine milling cutters with squared inserts. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of The 17th CIRP Conference on Modelling of Machining Operations

Boring bars with tuned mass dampers have a passive damper tuned with respect to the frequency of the first bending mode of the tool. When the tool is clamped into the machine tool there is a stiffness loss that lowers the natural frequency of the bar compared to ideal clamping conditions. For large tools the difference can be more than 35%, depending on clamping structure, tool size and overhang. In this paper we investigate a simple two-degree-of-freedom model for the tool-machine interaction consisting of a bending mode coupled with a rotational stiff mode. The model gives good insight into the system behavior and fits well with measurements. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of The 17th CIRP Conference on Modelling of Machining Operations, open access

Boring bars with tuned mass dampers have a passive damper tuned with respect to the frequency of the first bending mode of the tool. When the tool is clamped into the machine tool there is a stiffness loss that lowers the natural frequency of the bar compared to ideal clamping conditions. For large tools the difference can be more than 35%, depending on clamping structure, tool size and overhang. In this paper we investigate a simple two-degree-of-freedom model for the tool-machine interaction consisting of a bending mode coupled with a rotational stiff mode. The model gives good insight into the system behavior and fits well with measurements. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of The 17th CIRP Conference on Modelling of Machining Operations

Boring bars with tuned mass dampers have a passive damper tuned with respect to the frequency of the first bending mode of the tool. When the tool is clamped into the machine tool there is a stiffness loss that lowers the natural frequency of the bar compared to ideal clamping conditions. For large tools the difference can be more than 35%, depending on clamping structure, tool size and overhang. In this paper we investigate a simple two-degree-of-freedom model for the tool-machine interaction consisting of a bending mode coupled with a rotational stiff mode. The model gives good insight into the system behavior and fits well with measurements. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of The 17th CIRP Conference on Modelling of Machining Operations, open access

Boring bars with tuned mass dampers have a passive damper tuned with respect to the frequency of the first bending mode of the tool. When the tool is clamped into the machine tool there is a stiffness loss that lowers the natural frequency of the bar compared to ideal clamping conditions. For large tools the difference can be more than 35%, depending on clamping structure, tool size and overhang. In this paper we investigate a simple two-degree-of-freedom model for the tool-machine interaction consisting of a bending mode coupled with a rotational stiff mode. The model gives good insight into the system behavior and fits well with measurements. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of The 17th CIRP Conference on Modelling of Machining Operations, open access

Boring bars with tuned mass dampers have a passive damper tuned with respect to the frequency of the first bending mode of the tool. When the tool is clamped into the machine tool there is a stiffness loss that lowers the natural frequency of the bar compared to ideal clamping conditions. For large tools the difference can be more than 35%, depending on clamping structure, tool size and overhang. In this paper we investigate a simple two-degree-of-freedom model for the tool-machine interaction consisting of a bending mode coupled with a rotational stiff mode. The model gives good insight into the system behavior and fits well with measurements. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of The 17th CIRP Conference on Modelling of Machining Operations, open access

Boring bars with tuned mass dampers have a passive damper tuned with respect to the frequency of the first bending mode of the tool. When the tool is clamped into the machine tool there is a stiffness loss that lowers the natural frequency of the bar compared to ideal clamping conditions. For large tools the difference can be more than 35%, depending on clamping structure, tool size and overhang. In this paper we investigate a simple two-degree-of-freedom model for the tool-machine interaction consisting of a bending mode coupled with a rotational stiff mode. The model gives good insight into the system behavior and fits well with measurements. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of The 17th CIRP Conference on Modelling of Machining Operations, open access

This paper proposes a guideline for interpreting frequency content and time history of sound measurements in metal drilling processes. Different dynamic phenomena are reflected in generated sound in cutting processes. The footprint of such phenomena including torsional, lateral regenerative chatter and whirling in sound measurement results are discussed. Different indexable insert drills, at several cutting conditions, are covered. The proposed analysis could be used for studying, online monitoring and controlling of drilling processes. © 2017 The Authors., CC BY-NC-ND 4.0Available online 31 May 2017This paper is the result of a joint project between Sandvik Coromant and University West, Sweden. Resources provided by SiCoMaP and the Knowledge Foundation (KK-stiftelsen) are greatly appreciated.