Control of free length when coiling a helical spring

Readman, Mark C.; Muldoon, Mark; Reynolds, L.; Morris, Robert L.; Bayliss, Martin T.; Wood, David А.; Stewart, Ian

doi:10.1049/ip-cta:20010426

Cited by 3 publications

(2 citation statements)

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“…Finally, the application of fuzzy control was investigated (see, e.g. reference [7]), and eventually a Fuzzy PIþD controller inspired by the work of Malki et al [8] was implemented on a typical wire coiling (Waffios) machine with significant success (for more detail, see references [9,10]).…”

Section: Control Of a Spring-forming Machinementioning

confidence: 99%

Making better springs using aspects of chaos theory

Wood

2006

Proceedings of the Institution of Mechanical Engineers, Part C:

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This paper outlines two projects concerned with the quality testing of wire and strip metal used for the formation of springs and attempts of in-line control of a (helical) spring coiling machine for wire. The Fracmat project was a highly successful attempt to test wire for its coilability, which is known to be due to sequential variability. Methods from chaos theory, notably the Packard -Takens plot, were used to extract information that had otherwise been ignored to produce a test that could successfully distinguish between good wire and poor wire which otherwise satisfied all other available quality tolerances.

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Section: Control Of a Spring-forming Machinementioning

confidence: 99%

Making better springs using aspects of chaos theory

Wood

2006

Proceedings of the Institution of Mechanical Engineers, Part C:

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show abstract

“…General formulas for calculating stiffness and natural frequency of a commonly used helical spring were proposed in his study. Researchers found these traditional formulas effective in static stress analysis of automobile springs [7], process control of coiling helical springs [8] and optimal design of helical springs made of either a metal material [9] or a composite material [10,11]. Besides, these formulas also contributed to the development of dynamical spring models, for instance, the multi-mass models and the distributed parameters models.…”

Section: Introductionmentioning

confidence: 99%