Spiral toolpath generation method for pocket machining

Huang, Nuodi; Jin, Yongqiao; Lu, Yaoan; Yi, Bowen; Li, Xiaoyong; Wu, Shijing

doi:10.1016/j.cie.2019.106142

Cited by 12 publications

(5 citation statements)

References 25 publications

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“…Choosing tools, coatings, and even more efficient machining strategies, induces an increase in productivity reducing the overall part production cost by, primarily, reducing the machining time. This was reported by Huang et al [13], where the authors devise a new machining strategy, where the cutting length and machining time are promoted for pocket milling operations. A model was successfully developed, able to generate a spiral toolpath that can be applied for a multitude of pocket milling operations, in which the material removal rate, cycle time, and tool-path length were optimized.…”

Section: Introductionmentioning

confidence: 93%

Build-Up an Economical Tool for Machining Operations Cost Estimation

Silva

Sousa

Pinto

et al. 2022

Metals

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Currently, there is a lack of affordable and simple tools for the estimation of these costs, especially for machining operations. This is particularly true for manufacturing SMEs, in which the cost estimation of machined parts is usually performed based only on required material for part production, or involves a time-consuming, non-standardized technical analysis. Therefore, a cost estimation tool was developed, based on the calculated machining times and amount of required material, based on the final drawing of the requested workpiece. The tool was developed primarily for milling machines, considering milling, drilling, and boring/threading operations. Regarding the considered materials, these were primarily aluminum alloys. However, some polymer materials were also considered. The tool first estimates the required time for total part production and then calculates the total cost. The total production time is estimated based on the required machining operations, as well as drawing, programming, and machine setup time. A part complexity level was also introduced, based on the number of details and operations required for each workpiece, which will inflate the estimated times. The estimation tool was tested in a company setting, comparing the estimated operation time values with the real ones, for a wide variety of parts of differing complexity. An average error of 14% for machining operation times was registered, which is quite satisfactory, as this time is the most impactful in terms of machining cost. However, there are still some problems regarding the accuracy in estimating finishing operation times.

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Section: Introductionmentioning

confidence: 93%

Build-Up an Economical Tool for Machining Operations Cost Estimation

Silva

Sousa

Pinto

et al. 2022

Metals

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“…As depicted in Figure 3, the complete point cloud data set is divided into multiple sections through multi-layer slicing operations. By intersecting the point cloud data of each section, an overview of the entire point cloud model can be obtained, and the computational expression of this process is shown in Formula (Huang et al, 2020).…”

Section: Rough Toolpath Generation Based On Point Cloud Modelsmentioning

confidence: 99%

“…In Formula 3 (Huang et al, 2020), Δdist represents the distance between adjacent layers of slices, and E 1 , E 2 , E 3 , /E i , E m represents the number of layers of slices. As scattered point clouds are discretely distributed, to further optimize the contour of the point cloud model, the study introduces the concept of slicing thickness.…”

Section: Rough Toolpath Generation Based On Point Cloud Modelsmentioning

confidence: 99%

“…Techniques such as realtime toolpath generation, toolpath generation for multi-axis and five-axis machine tools, and data-driven trajectory generation have been proposed . While these techniques have met the requirements of CNC equipment in the past, they are increasingly unable to satisfy current mass demands due to technological innovations and rising structural requirements for processed parts (Huang et al, 2020). With the rise of threedimensional (3D) data models, point cloud models, as a special type of 3D model, are widely used in various fields due to their high resolution, precise 3D representation, and real-time data characteristics, addressing complex issues (Praveen et al, 2020).…”

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confidence: 99%

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Data model-based toolpath generation techniques for CNC milling machines

Liao,

Huang

2024

Front. Mech. Eng.

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Introduction: With the development of computer technology and data modeling, the use of point cloud models to generate tool paths is particularly important for improving productivity and accuracy.Methods: This study proposes a new method that first preprocesses the point cloud data using four-point denoising and octree methods to improve processing efficiency. Subsequently, roughing tool paths were analyzed using the layer slicing method and finishing paths using the residual height method.Results and Discussion: The experimental results show that the layer slicing method has a minimum error close to 10% on the roughing path generation and the computation time is reduced to 35 s, while the residual height method has an error rate of 10.17% on the finishing path and the computation time is only 11.82 s, which reflects a high trajectory smoothness and accuracy. The above results show that the study not only optimizes the tool path generation process and improves the machining efficiency and accuracy, but also demonstrates the potential application of point cloud models in the machining of complex parts.Conclusion: The novel tool roughing and finishing methods provide more reliable path planning for actual machining operations, and future research will be devoted to further improving the performance of the data processing algorithms and exploring more efficient path planning strategies to facilitate automated production.

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“…The selection of the right machining strategy and cutting tool also plays a vital role in reducing cutting times, as well as the final cost and quality of the process [17]. Some authors seek to improve the calculation of some common cutting strategies, achieving a reduction in cutting forces and longer tool life [18] or reducing machining time by more than 55% [19]. Other approaches have shown that the sequence of cutting tools used can influence the amount of energy consumed and the cost of the process [20], and that the correct planning of operations allows for the reduction of errors, setup times, and non-productivity times [21].…”

Section: Introductionmentioning

confidence: 99%

Using an Artificial Neural Network Approach to Predict Machining Time

Rodrigues

Silva

Sousa

et al. 2022

Metals

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One of the most critical factors in producing plastic injection molds is the cost estimation of machining services, which significantly affects the final mold price. These services’ costs are determined according to the machining time, which is usually a long and expensive operation. If it is considered that the injection mold parts are all different, it can be understood that the correct and quick estimation of machining times is of great importance for a company’s success. This article presents a proposal to apply artificial neural networks in machining time estimation for standard injection mold parts. For this purpose, a large set of parts was considered to shape the artificial intelligence model, and machining times were calculated to collect enough data for training the neural networks. The influences of the network architecture, input data, and the variables used in the network’s training were studied to find the neural network with greatest prediction accuracy. The application of neural networks in this work proved to be a quick and efficient way to predict cutting times with a percent error of 2.52% in the best case. The present work can strongly contribute to the research in this and similar sectors, as recent research does not usually focus on the direct prediction of machining times relating to overall production cost. This tool can be used in a quick and efficient manner to obtain information on the total machining cost of mold parts, with the possibility of being applied to other industry sectors.

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Spiral toolpath generation method for pocket machining

Cited by 12 publications

References 25 publications

Build-Up an Economical Tool for Machining Operations Cost Estimation

Build-Up an Economical Tool for Machining Operations Cost Estimation

Data model-based toolpath generation techniques for CNC milling machines

Using an Artificial Neural Network Approach to Predict Machining Time

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