IP Library Granted Patent US 12,373,968
Granted Patent B2
US 12,373,968 · App. 17/952,939 · Granted Jul 29, 2025

Three-dimensional shape measurement system and machine tool system

Inventors: Shigemoto Hirota (Aichi, JP); Akihito Kataoka (Aichi, JP); Keigo Asano (Aichi, JP); Masahiro Maeda (Aichi, JP)
Assignee: OKUMA CORPORATION
G06T7/50G06T7/0004G06T7/60G06T17/00G06V10/141G06T2207/10028G06T2207/20081
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,373,968
App. No.
17/952,939
Granted
Jul 29, 2025
Kind
B2
Abstract

A three-dimensional shape measurement system includes an image pickup unit, a storage device that stores image pickup conditions required in imaging for measurement as condition information for each of a plurality of combinations of the material and surface property of an object, and a measurement controller that controls driving of the image pickup unit. The measurement controller identifies the material and surface property of the object, specifies image pickup conditions corresponding to the identified material and surface property of the object based on the condition information, causes the image pickup unit to perform the imaging for measurement under the specified image pickup conditions, and measures the shape of the object based on the obtained image for measurement.

Claims (23)

1. A three-dimensional shape measurement system, comprising:

an image pickup unit configured to have at least one camera that images an object;

a storage device that stores image pickup conditions required in imaging for measurement in which the object is imaged to measure the shape of the object, as condition information, for each of a plurality of combinations of the material and surface property of the object; and

a measurement controller that controls driving of the image pickup unit,

wherein the measurement controller identifies the material and surface property of the object, specifies image pickup conditions corresponding to the identified material and surface property of the object based on the condition information, causes the image pickup unit to perform the imaging for measurement under the specified image pickup conditions, and measures the shape of the object based on the obtained image for measurement.

2. The three-dimensional shape measurement system according to claim 1 , wherein the measurement controller causes the image pickup unit to image the object prior to the imaging for measurement and identifies at least one of the material and surface property of the object based on a preliminary image obtained thereby.

3. The three-dimensional shape measurement system according to claim 2 , wherein

the storage device further stores a learning model that receives, as an input, the preliminary image, and outputs at least one of the material and surface property of the object, and

the measurement controller identifies at least one of the material and surface property of the object based on the learning model.

4. The three-dimensional shape measurement system according to claim 1 , wherein

the image pickup unit is provided in a machine tool to image a workpiece to which machining is applied by the machine tool, as the object, and

the measurement controller identifies at least one of the material and surface property of the object based on at least either of a machining program of the workpiece and a machining drawing of the workpiece.

5. The three-dimensional shape measurement system according to claim 4 , wherein the measurement controller acquires information indicating at least either of the machining program and the machining drawing, from a numerical control device of the machine tool.

6. The three-dimensional shape measurement system according to claim 1 wherein at least one of the material and surface property of the object is designated by an operator.

7. The three-dimensional shape measurement system according to claim 1 , wherein the image pickup unit further includes one or more light sources each irradiating the object with image pickup light,

the measurement controller performs basic imaging for imaging the object in order to acquire a single image for measurement once or more, and

the image pickup conditions include at least one selected from the group consisting of the number of times the basic imaging is to be performed to acquire the single image for measurement, the shutter speed of the camera in each basic imaging to be performed once or more, the gain of the camera in each basic imaging to be performed once or more, and the luminance distribution of the image pickup light in each basic imaging to be performed once or more.

8. The three-dimensional shape measurement system according to claim 1 , wherein the measurement controller generates point cloud data of the object based on the image for measurement and generates three-dimensional data of the object from the point cloud data.

9. A machine tool system comprising:

the three-dimensional shape measurement system according to claim 8 ; and

a machine tool that performs machining on an object,

wherein

the machine tool performs, based on the three-dimensional data generated by the measurement controller, at least one selected from the group consisting of checking whether the tool interferes with the object, generating a path of the tool, determining machining accuracy, determining whether the shape of the object coincides with a reference shape, and determining whether the object is in a predetermined position.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 27, 2022
From: HIROTA, SHIGEMOTO; KATAOKA, AKIHITO; ASANO, KEIGO; MAEDA, MASAHIRO
To: OKUMA CORPORATION
Reel/Frame 061222/0448 →
Priority Claims (2)
JP 2021-156958 · Sep 27, 2021 · national
JP 2022-005238 · Jan 17, 2022 · national
Continuity (1)
Related Publication 20230101718A1 · Mar 30, 2023
References Cited (36)
US 8983797B2 · Ingram, Jr. · 2015 [cited by examiner]
US 9082071B2 · Skaff · 2015 [cited by examiner]
US 9367909B2 · Tin · 2016 [cited by examiner]
US 9562857B2 · Debevec · 2017 [cited by examiner]
US 10055882B2 · Marin · 2018 [cited by examiner]
US 10235797B1 · Sheffield · 2019 [cited by examiner]
US 11468552B1 · Valikhani · 2022 [cited by examiner]
US 20130093883A1 · Wang · 2013 [cited by examiner]
US 20150016711A1 · Tin · 2015 [cited by examiner]
US 20180047208A1 · Marin · 2018 [cited by examiner]
US 20190188841A1 · Kato · 2019 [cited by examiner]
US 20190294126A1 · Watanabe · 2019 [cited by applicant]
US 20200174240A1 · Kang et al. · 2020 [cited by applicant]
US 20200238460A1 · Suzuki et al. · 2020 [cited by applicant]
US 20210027491A1 · Satou · 2021 [cited by applicant]
US 20210035354A1 · Williams · 2021 [cited by examiner]
US 20210279492A1 · Vemury · 2021 [cited by examiner]
US 20220084181A1 · Isken · 2022 [cited by examiner]
US 20220168898A1 · Satat · 2022 [cited by examiner]
US 20230082268A1 · Delaney · 2023 [cited by examiner]
EP 3879486A1 · 2021 [cited by examiner]
JP 2006349416A · 2006 [cited by applicant]
JP 2009139239A · 2009 [cited by examiner]
JP 2019069486A · 2019 [cited by applicant]
JP 2019166603A · 2019 [cited by applicant]
JP 2020086293A · 2020 [cited by applicant]
JP 2021018662A · 2021 [cited by applicant]
JP 2021089215A · 2021 [cited by examiner]
JP 7152223B2 · 2022 [cited by examiner]
WO WO2023005827A1 · 2023 [cited by examiner]
Y. D. Chethan, et al “Machine vision for correlating Tool status and machined Surface in Turning Nickel-base super alloy,” 2015 International Conference on Emerging Research in Electronics, Computer Science and Technolo… [cited by examiner]
Rajneesh Kumar, P. Kulashekar, B. Shanasekar, B. Ramamoorthy “Application of digital image magnification for surface roughness evaluation using machine vision” International Journal of Machine Tools & Manufacture vol. 4… [cited by examiner]
G. D. Babu, K. S. Babu and B. U. M. Gowd, “Evaluation of surface roughness using machine vision,” INTERACT-2010, Chennai, India, 2010, pp. 220-223, doi: 10.1109/INTERACT.2010.5706143. (Year: 2010). [cited by examiner]
K. Tang, F. Chen and F. Chang, “Roughness Classification of End Milling Based on Machine Vision,” 2020 3rd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM), Shanghai, China, 2020, pp. 29… [cited by examiner]
Özcan, B. Schwermann R, Blankenbach J. A Novel Camera-Based Measurement System for Roughness Determination of Concrete Surfaces. Materials (Basel). Dec. 31, 2020;14(1):158. doi: 10.3390/ma14010158. (Year: 2020). [cited by examiner]
Hagihara et al., “Study on Grasping Measurement Error and Establishment of High Precision Measurement Method by Non-contact 3D Scanner (2nd Report)—Verification about High Precision Measurement Method”; Mar. 26, 2021, (… [cited by applicant]