Abstract
This paper considers the problem of controlling bead geometry of a Gas-Metal Arc Weld (GMAW). Specifically, the desire is control bead width and depth in real-time, and travel speed of the torch and wire feedrate are chosen as the inputs. Previous work has shown that process dynamics are highly non-linear, and this is quantified for this problem through a series of experiments and off-line parameter identification experiments. Since the range of parameter variations can be as large as ± 40%, adaptive control is investigated to achieve high performance. A multi-variable deadbeat adaptive control algorithm is investigated and implemented to achieve the desired weld bead geometries. Control weighting factors are introduced to reduce the excessive control effort that is likely to exist in the deadbeat algorithm. Simulation results indicate that the weighted deadbeat adaptive controller works well for the welding process, and that the control weighting factors are essential to practical, non-saturating control performance.
| Original language | English |
|---|---|
| Title of host publication | American Society of Mechanical Engineers, Production Engineering Division (Publication) PED |
| Editors | Elijah Jr. Kannatey-Asibu, Hyung Suck Cho, Shuichi Fukuda |
| Publisher | Publ by ASME |
| Pages | 123-134 |
| Number of pages | 12 |
| Volume | 51 |
| ISBN (Print) | 0791808521 |
| Publication status | Published - 1991 |
| Externally published | Yes |
| Event | Winter Annual Meeting of the American Society of Mechanical Engineers - Atlanta, GA, USA Duration: 1991 Dec 1 → 1991 Dec 6 |
Other
| Other | Winter Annual Meeting of the American Society of Mechanical Engineers |
|---|---|
| City | Atlanta, GA, USA |
| Period | 91/12/1 → 91/12/6 |
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ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
- Mechanical Engineering
Cite this
Multivariable adaptive control of bead geometry in GMA welding. / Song, Jae-Bok; Hardt, David E.
American Society of Mechanical Engineers, Production Engineering Division (Publication) PED. ed. / Elijah Jr. Kannatey-Asibu; Hyung Suck Cho; Shuichi Fukuda. Vol. 51 Publ by ASME, 1991. p. 123-134.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Multivariable adaptive control of bead geometry in GMA welding
AU - Song, Jae-Bok
AU - Hardt, David E.
PY - 1991
Y1 - 1991
N2 - This paper considers the problem of controlling bead geometry of a Gas-Metal Arc Weld (GMAW). Specifically, the desire is control bead width and depth in real-time, and travel speed of the torch and wire feedrate are chosen as the inputs. Previous work has shown that process dynamics are highly non-linear, and this is quantified for this problem through a series of experiments and off-line parameter identification experiments. Since the range of parameter variations can be as large as ± 40%, adaptive control is investigated to achieve high performance. A multi-variable deadbeat adaptive control algorithm is investigated and implemented to achieve the desired weld bead geometries. Control weighting factors are introduced to reduce the excessive control effort that is likely to exist in the deadbeat algorithm. Simulation results indicate that the weighted deadbeat adaptive controller works well for the welding process, and that the control weighting factors are essential to practical, non-saturating control performance.
AB - This paper considers the problem of controlling bead geometry of a Gas-Metal Arc Weld (GMAW). Specifically, the desire is control bead width and depth in real-time, and travel speed of the torch and wire feedrate are chosen as the inputs. Previous work has shown that process dynamics are highly non-linear, and this is quantified for this problem through a series of experiments and off-line parameter identification experiments. Since the range of parameter variations can be as large as ± 40%, adaptive control is investigated to achieve high performance. A multi-variable deadbeat adaptive control algorithm is investigated and implemented to achieve the desired weld bead geometries. Control weighting factors are introduced to reduce the excessive control effort that is likely to exist in the deadbeat algorithm. Simulation results indicate that the weighted deadbeat adaptive controller works well for the welding process, and that the control weighting factors are essential to practical, non-saturating control performance.
UR - http://www.scopus.com/inward/record.url?scp=0026390677&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0026390677&partnerID=8YFLogxK
M3 - Conference contribution
SN - 0791808521
VL - 51
SP - 123
EP - 134
BT - American Society of Mechanical Engineers, Production Engineering Division (Publication) PED
A2 - Kannatey-Asibu, Elijah Jr.
A2 - Cho, Hyung Suck
A2 - Fukuda, Shuichi
PB - Publ by ASME
ER -