Automatic Motion Generation for Robotic Milling Optimizing Stiffness with Sample-Based Planning

Posada, Julian; Schneider, Ulrich; Sridhar, Arjun; Verl, Alexander

doi:10.3390/machines5010003

Cited by 18 publications

(6 citation statements)

References 31 publications

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“…Dimensional errors as low as 0.05 mm were obtained in pocket milling tests on aluminum. In more recent work, Diaz Posada et al [66] presented their offline error compensation strategy ( Fig. 15) with experimental verification results obtained on a KUKA Quantec KR270 2700 industrial robot.…”

Section: Offline Methodsmentioning

confidence: 95%

Robots in machining

et al. 2019

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Robotic machining centers offer diverse advantages: large operation reach with large reorientation capability, and a low cost, to name a few. Many challenges have slowed down the adoption or sometimes inhibited the use of robots for machining tasks. This paper deals with the current usage and status of robots in machining, as well as the necessary modelling and identification for enabling optimization, process planning and process control. Recent research addressing deburring, milling, incremental forming, polishing or thin wall machining is presented. We discuss various processes in which robots need to deal with significant process forces while fulfilling their machining task.

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Section: Offline Methodsmentioning

confidence: 95%

Robots in machining

et al. 2019

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“…The disadvantage of using robots for machining is their low stiffness and accuracy [21], whereas the cutting force of the ultrasonic cutting process for Nomex honeycomb is small enough [22] to meet the accuracy of robot. At present, the studies of robotic machining path planning focus more on robotic kinematic optimization on machining efficiency [23] and accuracy [24], [25], and the tool path is usually generated by general CAM system [24], [26], [27]. However, V-shaped cutting requires special path planning method as mentioned above, and there are few or no reports on the V-shaped path planning for robots at present.…”

Section: Figure 1 Comparison Of Chip Formation Between Milling [7] Amentioning

confidence: 99%

A Path Planning Method for V-Shaped Robotic Cutting of Nomex Honeycomb by Straight Blade Tool

et al. 2020

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V-shaped cutting by straight blade tool has been proposed as a typical process to quickly remove large volumes of Nomex honeycomb. To meet the process requirements of V-shaped cutting by robots, a major problem is that the six degrees of freedom of the straight blade tool must be precisely programmed. In this paper, a path planning method for the V-shaped robotic cutting process is presented. The determining method of path parameters for cylindrical parts is firstly analyzed, with an optimization model proposed. The position of path points is pre-generated by robotic machining module of CAM software, and machining files in the robot language are output. Considering the requirement of V-shaped cutting path, a special post-processing program is developed to translate the CAM software generated machining files into final files for V-shaped cutting, which converts the reciprocating milling path into the V-shaped processing path and adds the orientation data of the path points. Furthermore, the robot machining files generated by the post-processing program are verified in MATLAB. V-shaped cutting experiments on Nomex honeycomb were carried out using 6-axis robot and ultrasonic cutting system to verify the presented method. Results show that the cylindrical surface of the machined workpiece is consistent with the expectation. INDEX TERMS path planning, V-shaped cutting, Nomex honeycomb, robot processing, straight knife.

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“…Recently, realistic modeling, together with the optimization of the production process, were paired together in order to analyze the process parameters for a minimum cycle time [5,10]. This enables numerically controlled devices-such as CNC machine tools, 3D printers, robots, 3D scanners, and coordinate sampling machines-to successfully perform their desired task [11][12][13][14]. In [15], contour parallel tool-path optimization was presented for the milling operation of 2D pocket regions.…”

Section: State Of the Artmentioning

confidence: 99%

Automated Unsupervised 3D Tool-Path Generation Using Stacked 2D Image Processing Technique

Tuli

Cesarini

2019

JMMP

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Tool-path, feed-rate, and depth-of-cut of a tool determine the machining time, tool wear, power consumption, and realization costs. Before the commissioning and production, a preliminary phase of failure-mode identification and effect analysis allows for selecting the optimal machining parameters for cutting, which, in turn, reduces machinery faults, production errors and, ultimately, decreases costs. For this, scalable high-precision path generation algorithms requiring a low amount of computation might be advisable. The present work provides such a simplified scalable computationally low-intensive technique for tool-path generation. From a three dimensional (3D) digital model, the presented algorithm extracts multiple two dimensional (2D) layers. Depending on the required resolution, each layer is converted to a spatial image, and an algebraic analytic closed-form solution provides a geometrical tool path in Cartesian coordinates. The produced tool paths are stacked after processing all object layers. Finally, the generated tool path is translated into a machine code using a G-code generator algorithm. The introduced technique was implemented and simulated using MATLAB® pseudocode with a G-code interpreter and a simulator. The results showed that the proposed technique produced an automated unsupervised reliable tool-path-generator algorithm and reduced tool wear and costs, by allowing the selection of the tool depth-of-cut as an input.

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Automatic Motion Generation for Robotic Milling Optimizing Stiffness with Sample-Based Planning

Cited by 18 publications

References 31 publications

Robots in machining

Robots in machining

A Path Planning Method for V-Shaped Robotic Cutting of Nomex Honeycomb by Straight Blade Tool

Automated Unsupervised 3D Tool-Path Generation Using Stacked 2D Image Processing Technique

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