Single Point Incremental Forming (SPIF) is a versatile process for producing small batches or custom components in precision-demanding industries. This dieless metal forming technique utilizes a hemispherical-tipped tool that follows a controlled trajectory. While SPIF offers flexibility and high formability, challenges related to geometric accuracy and springback persist. This study investigates the impact of machine compliance on geometric accuracy and forming forces during stainless steel SPIF using both a CNC machine and a robot, combining experimental tests and FEM analysis. The results reveal that the CNC machine is approximately 2.5<InlineEquation ID="IEq1"> <EquationSource Format="TEX">$\times $</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation>, 4<InlineEquation ID="IEq2"> <EquationSource Format="TEX">$\times $</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation>, and 11<InlineEquation ID="IEq3"> <EquationSource Format="TEX">$\times $</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation> stiffer than the robot in the X, Y, and Z directions, respectively. CNC-formed parts demonstrated lower wall angle deviations (e.g., 0.02–0.05° vs. 0.14–0.18° for the robot) and smaller springback distortions in truncated cones. Conversely, the robot achieved 45.6% lower surface roughness (e.g., 0.72–1.14 <InlineEquation ID="IEq4"> <EquationSource Format="TEX">$\upmu $</EquationSource> <EquationSource Format="MATHML"><math> <mi mathvariant="normal">μ</mi> </math></EquationSource> </InlineEquation>m vs. 1.41–1.86 <InlineEquation ID="IEq5"> <EquationSource Format="TEX">$\upmu $</EquationSource> <EquationSource Format="MATHML"><math> <mi mathvariant="normal">μ</mi> </math></EquationSource> </InlineEquation>m for CNC) across all geometries. Regarding forming forces, CNC exhibited 15–24% higher in-plane forces but 2–20% lower Z-forces compared to the robot, with total forces remaining similar (difference below 3%). Finite element simulations corroborated these trends but underestimated lateral forces due to shell-element limitations. These findings highlight the trade-offs between stiffness, accuracy, and surface quality, providing actionable insights for selecting SPIF systems based on application priorities.

Effects of machine compliance on forming accuracy and forces in SPIF of AISI 430

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2025年6月7日

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Alejandro Simoncelli,
Luciano Buglioni,
Daniel Martínez Krahmer,
Antonio Sánchez Egea

摘要

Single Point Incremental Forming (SPIF) is a versatile process for producing small batches or custom components in precision-demanding industries. This dieless metal forming technique utilizes a hemispherical-tipped tool that follows a controlled trajectory. While SPIF offers flexibility and high formability, challenges related to geometric accuracy and springback persist. This study investigates the impact of machine compliance on geometric accuracy and forming forces during stainless steel SPIF using both a CNC machine and a robot, combining experimental tests and FEM analysis. The results reveal that the CNC machine is approximately 2.5 $\times $ $\times$ , 4 $\times $ $\times$ , and 11 $\times $ $\times$ stiffer than the robot in the X, Y, and Z directions, respectively. CNC-formed parts demonstrated lower wall angle deviations (e.g., 0.02–0.05° vs. 0.14–0.18° for the robot) and smaller springback distortions in truncated cones. Conversely, the robot achieved 45.6% lower surface roughness (e.g., 0.72–1.14 $\upmu $ $μ$ m vs. 1.41–1.86 $\upmu $ $μ$ m for CNC) across all geometries. Regarding forming forces, CNC exhibited 15–24% higher in-plane forces but 2–20% lower Z-forces compared to the robot, with total forces remaining similar (difference below 3%). Finite element simulations corroborated these trends but underestimated lateral forces due to shell-element limitations. These findings highlight the trade-offs between stiffness, accuracy, and surface quality, providing actionable insights for selecting SPIF systems based on application priorities.