Robot-Based Incremental Sheet Metal Forming – Increasing the Geometrical Accuracy of Complex Parts

Kreimeier, Dieter; Buff, B.; Magnus, Christian S.; Smukala, V.; Zhu, Jinying

doi:10.4028/www.scientific.net/kem.473.853

Cited by 18 publications

(8 citation statements)

References 9 publications

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“…There has been a significant research done and being carried out in stretching methods including single-point incremental forming (SPIF) [12] and double-point incremental forming (DPIF) [13]. Though they were studied, they are beyond the scope of this research.…”

Section: Automated Dieless Sheet Metal Formingmentioning

confidence: 99%

An automated solution for fixtureless sheet metal forming

Ilangovan

Monfared

Jackson

2015

Int J Adv Manuf Technol

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Manual forming of sheet metal parts through traditional panel beating is a highly skilled profession used in many industries, particularly for sample manufacturing or repair and maintenance. However, this skill is becoming gradually isolated mainly due to the high cost and lack of expertise. Nonetheless, a cost-effective and flexible approach to forming sheet metal parts could significantly assist various industries by providing a method for fast prototyping sheet metal parts. The development of a new fixtureless sheet metal forming approach is discussed in this article. The proposed approach, named Mechatroforming ® , consists of integrated mechanisms to manipulate sheet metal parts by a robotic arm under a controlled hammering tool. The method includes mechatronicsbased monitoring and control systems for (near) real-time prediction and control of incremental deformations of parts. This article includes description of the proposed approach, the theoretical and modelling backgrounds used to predict the forming, skills learned from manual operations, and proposed automation system being built.

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Section: Automated Dieless Sheet Metal Formingmentioning

confidence: 99%

An automated solution for fixtureless sheet metal forming

Ilangovan

Monfared

Jackson

2015

Int J Adv Manuf Technol

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“…Many of these methods embrace alterations of the geometrical input for the process, just as our method does. For instance, they advocate the creation of addendum surfaces [44] and extracurricular support structures [45], input geometry splitting and varying toolpath designs to enable multi-pass forming [46], as well as input geometry alteration [47,48]. However, what makes all those methods fundamentally different from our approach is their goal.…”

Section: Research Results In a Broader Contextmentioning

confidence: 99%

From Undesired Flaws to Esthetic Assets: A Digital Framework Enabling Artistic Explorations of Erroneous Geometric Features of Robotically Formed Molds

Zboinska

2019

Technologies

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Until recently, digital fabrication research in architecture has aimed to eliminate manufacturing errors. However, a novel notion has just been established—intentional computational infidelity. Inspired by this notion, we set out to develop means than can transform the errors in fabrication from an undesired complication to a creative opportunity. We carried out design experiment-based investigations, which culminated in the construction of a framework enabling fundamental artistic explorations of erroneous geometric features of robotically formed molds. The framework consists of digital processes, assisting in the explorations of mold errors, and physical processes, enabling the inclusion of physical feedback in digital explorations. Other complementary elements embrace an implementation workflow, an enabling digital toolset and a visual script demonstrating how imprecise artistic explorations can be included within the computational environment. Our framework application suggests that the exploration of geometrical errors aids the emergence of unprecedented design features that would not have arisen if error elimination were the ultimate design goal. Our conclusion is that welcoming error into the design process can reinstate the role of art, craft, and material agency therein. This can guide the practice and research of architectural computing onto a new territory of esthetic and material innovation.

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“…duplex incremental forming (DPIF), also referred to as double sided incremental forming (DSIF) [24]. In DSIF, the sheet is actively formed by a tool that is usually hemispherical, with assistance from a second tool on the sheet's reverse.…”

Section: Forming Setupmentioning

confidence: 99%

Prediction of forming accuracy in incremental sheet forming using artificial neural networks on local surface representations

Möllensiep,

Detering,

Kulessa

et al. 2024

Preprint

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While incremental sheet metal forming offers the potential for producing sheet metal parts in small lot sizes, the relatively low forming accuracy prevents a widespread industrial use. For improving the forming accuracy, research institutes are using machine learning techniques to predict the geometric accuracy and modify the toolpath based on the prediction. A critical challenge is it to ensure the generalisability of the prediction model as only a small amount of process data is available to train the model due to the lack of industrial collaborations. This publication presents a highly transferable feature engineering approach where surface representations of the part's geometry around each toolpath point are transferred into a standardized coordinate system. Several artificial neural networks were trained and used for predicting the forming accuracy and modifying the toolpath. During the validation experiments, the forming errors of parts which were independent of the training process were reduced by up to 68.5 %. The framework for computing the surface representations alongside with several pre-trained artificial neural networks is publicity available for download.

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Robot-Based Incremental Sheet Metal Forming – Increasing the Geometrical Accuracy of Complex Parts

Cited by 18 publications

References 9 publications

An automated solution for fixtureless sheet metal forming

An automated solution for fixtureless sheet metal forming

From Undesired Flaws to Esthetic Assets: A Digital Framework Enabling Artistic Explorations of Erroneous Geometric Features of Robotically Formed Molds

Prediction of forming accuracy in incremental sheet forming using artificial neural networks on local surface representations

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