Part precision improvement in incremental sheet forming of not axisymmetric parts using an artificial cognitive system

Fiorentino, Antonio; Feriti, Giancarlo C.; Giardini, Claudio; Ceretti, Elisabetta

doi:10.1016/j.jmsy.2015.02.003

Cited by 30 publications

(10 citation statements)

References 30 publications

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“…Besides, Micari et al [83] have tried to model the errors at the bottom corner and under forming of the bottom using geometrical parameters such as sheet thickness, part geometry and process variables such as tool diameter and step down. In other attempts, Fiorentino et al [130] have used an artificial cognitive system based on iterative learning control to improve part precision. In addition to these isolated efforts, one of the significant achievements in the last 10 years has been systematic compensation using error correction functions that use accuracy response surfaces and graph topological tool path planning discussed next.…”

Section: Accuracy Improvement Strategiesmentioning

confidence: 99%

Single point incremental forming: An assessment of the progress and technology trends from 2005 to 2015

Behera

Sousa

Ingarao

et al. 2017

Journal of Manufacturing Processes

225

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The last decade has seen considerable interest in flexible forming processes. Among the upcoming flexible forming techniques, one that has captured a lot of interest is Single Point Incremental Forming (SPIF), where a flat sheet is incrementally deformed into a desired shape by the action of a tool that follows a defined toolpath conforming to the final part geometry. Research on SPIF in the last ten years has focused on defining the limits of this process, understanding the deformation mechanics and material behavior and extending the process limits using various strategies. This paper captures the developments that have taken place over the last decade in academia and industry to highlight the current state of the art in this field. The use of different hardware platforms, forming mechanics, failure mechanism, estimation of forces, use of toolpath and tooling strategies, development of process planning tools, simulation of the process, aspects of sustainable manufacture and current and future applications are individually tracked to outline the current state of this process and provide a roadmap for future work on this process.

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Section: Accuracy Improvement Strategiesmentioning

confidence: 99%

Single point incremental forming: An assessment of the progress and technology trends from 2005 to 2015

Behera

Sousa

Ingarao

et al. 2017

Journal of Manufacturing Processes

225

View full text Add to dashboard Cite

show abstract

“…This springback is caused by the release of residual stress that is unevenly distributed after deformation. In addition, the "pillow effect" typically occurred at the bottom circle of the truncated cone in SPIF [10]. In order to improve the poor geometric accuracy, other attempts have been presented.…”

Section: Introductionmentioning

confidence: 99%

Research on the Radial Accuracy of Ultrasonic Vibration-Assisted Single Point Incremental Forming Parts

Yang

Bai

Lin

et al. 2019

International Journal of Aerospace Engineering

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With the more and more complexity demands of the market, the geometric accuracy of the part has become the main factor restricting the development of single point incremental forming technology (SPIF). For this reason, with the truncated cone as the target part, the radial accuracy error generation mechanism was analyzed from the aspects of sheet springback and residual stress distribution. Four factors and three levels of surface response experiments were designed using the Box-Behnken Design (BBD) for tool head diameter, layer spacing, sheet thickness, and wall angle. The single and interactive influence law of the process parameters on the radial accuracy was obtained. In response to the above research results, the ultrasonic vibration was introduced into the process of SPIF to reduce springback by reducing residual stress. The influence of vibration parameters on the accuracy was obtained through experiments. The results showed that ultrasonic vibration could effectively improve and control the accuracy of the part.

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“…Importantly for the production of individual goods, the embodied cognition of human operatives continues to provide unsurpassed flexibility for diverse sensorimotor/psychomotor tasks in assembly work [ 3 , 4 ]. Reliance on human workers for sensorimotor/psychomotor tasks in the production of individual goods continues in spite of many efforts to improve the embodied cognition of robots [ 5 , 6 ]. Nonetheless, human operatives make many errors during production work [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Cognitive Factories: Modeling Situated Entropy in Physical Work Carried Out by Humans and Robots

Fox

Kotelba

Niskanen

2018

Entropy

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Entropy in factories is situated. For example, there can be numerous different ways of picking, orientating, and placing physical components during assembly work. Physical components can be redesigned to increase the Information Gain they provide and so reduce situated entropy in assembly work. Also, situated entropy is affected by the extent of knowledge of those doing the work. For example, work can be done by knowledgeable experts or by beginners who lack knowledge about physical components, etc. The number of different ways that work can be done and the knowledge of the worker combine to affect cognitive load. Thus, situated entropy in factories relates to situated cognition within which knowledge is bound to physical contexts and knowing is inseparable from doing. In this paper, six contributions are provided for modelling situated entropy in factories. First, theoretical frameworks are brought together to provide a conceptual framework for modelling. Second, the conceptual framework is related to physical production using practical examples. Third, Information Theory mathematics is applied to the examples and a preliminary methodology in presented for modelling in practice. Fourth, physical artefacts in factory production are reframed as carriers of Information Gain and situated entropy, which may or may not combine as Net Information Gain. Fifth, situated entropy is related to different types of cognitive factories that involve different levels of uncertainty in production operations. Sixth, the need to measure Net Information Gain in the introduction of new technologies for embodied and extended cognition is discussed in relation to a taxonomy for distributed cognition situated in factory production. Overall, modelling of situated entropy is introduced as an opportunity for improving the planning and control of factories that deploy human cognition and cognitive technologies including assembly robotics.

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Part precision improvement in incremental sheet forming of not axisymmetric parts using an artificial cognitive system

Cited by 30 publications

References 30 publications

Single point incremental forming: An assessment of the progress and technology trends from 2005 to 2015

Single point incremental forming: An assessment of the progress and technology trends from 2005 to 2015

Research on the Radial Accuracy of Ultrasonic Vibration-Assisted Single Point Incremental Forming Parts

Cognitive Factories: Modeling Situated Entropy in Physical Work Carried Out by Humans and Robots

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