IP Library Granted Patent US 12,522,985
Granted Patent B2
US 12,522,985 · App. 18/414,301 · Granted Jan 13, 2026

Methods and apparatus for mobile additive manufacturing

Inventors: Robert A. Flitsch (North Providence, RI); Frederick A. Flitsch (Framingham, MA)
E01C23/07B33Y10/00B33Y30/00B33Y50/02E01C19/00E01C19/17G05D1/648G06V10/763
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Quick Facts
Patent No.
US 12,522,985
App. No.
18/414,301
Granted
Jan 13, 2026
Kind
B2
Abstract

The present disclosure provides various advancements for mobile and automated processing utilizing additive manufacturing. The present disclosure includes methods for the utilization of mobile and automated processing apparatus and may include examples of printing three dimensional items. In some examples, printing concrete structures may be performed. Artificial intelligence techniques may enhance operations and may be used to create model for the processing apparatus.

Claims (35)

1 . A method for constructing a three dimensional structure, the method comprising:

loading at least the first material into at least a first mobile additive manufacturing apparatus, wherein the first mobile additive manufacturing apparatus comprises:

a navigation system to determine location,

a mobile additive manufacturing apparatus controller capable of executing algorithms and providing control signals,

a vision system capable of scanning the surface and measuring a topography of the surface,

an additive manufacturing system to deposit at least the first material at a first prescribed location on a surface according to a digital model processed by the mobile additive manufacturing apparatus controller and to deposit at least the first material in a second prescribed location upon the deposit of the at least first material at the first prescribed location;

a power system capable of providing power to operate at least a drive system, navigation system, mobile additive manufacturing apparatus controller and additive manufacturing system, and

a communication system;

loading software algorithms into an artificial intelligence processing system with a global communication system, wherein the global communication system provides communication between at least the first mobile additive manufacturing apparatus and the artificial intelligence processing system;

communicating an objective for applying the first material to the surface to the artificial intelligence processing system, wherein the artificial intelligence processing system comprises a program which executes at least a first algorithm;

receiving information at the artificial intelligence processing system from the first mobile additive manufacturing apparatus comprising at least a location determined by the navigation system of the first mobile additive manufacturing apparatus;

processing the received information with the artificial intelligence processing system with at least a first artificial intelligence algorithm; and

communicating at least movement plans from the artificial intelligence processing system to at least each of the first mobile additive manufacturing apparatus.

2 . The method of claim 1 wherein the artificial intelligence processing system comprises an artificial intelligence processing chip.

3 . The method of claim 2 further comprising at least a second mobile additive manufacturing apparatus to form a team of mobile additive manufacturing apparatuses.

4 . The method of claim 3 further comprising:

receiving information at the artificial intelligence processing system from the first mobile additive manufacturing apparatus comprising at least an image determined by the vision system of the first mobile additive manufacturing apparatus;

processing the received information with the artificial intelligence processing system with at least a second artificial intelligence algorithm; and

communicating at least deposition plans of at least the first material to at least each of the first mobile additive manufacturing apparatus and the second mobile additive manufacturing apparatus of the team.

5 . The method of claim 4 wherein at least the first mobile additive manufacturing apparatus comprises an artificial intelligence processing chip.

6 . The method of claim 4 further comprising communicating at least a second deposition plan of at least a second material comprising a paint formulation.

7 . A method of depositing a series of layers of concrete, the method comprising:

loading at least a first concrete material into at least a first mobile additive manufacturing apparatus, wherein the first mobile manufacturing apparatus comprises:

a navigation system to determine location,

a controller capable of executing algorithms and providing control signals,

a vision system capable of scanning a first surface and measuring a topography of the first surface as the first mobile additive manufacturing apparatus moves over the first surface and capable of scanning a second surface of a deposit of concrete material,

a drive system to move the additive manufacturing apparatus,

an additive manufacturing system to deposit at least the first concrete material in a prescribed location on the first surface according to a digital model processed by the controller and to deposit the at least the first concrete material upon the second surface of the deposit of the at least the first concrete material at the prescribed location on the first surface,

a power system capable of providing power to operate at least the drive system, navigation system, controller and additive manufacturing system, and

a chassis, wherein the chassis supports all of the navigation system, the controller, the vision system, the drive system, the additive manufacturing system and the power system;

moving the first mobile additive manufacturing apparatus with the drive system, wherein an amount of movement is controlled by the digital model;

dispensing at least the first concrete material on one of the first surface and the second surface while the first mobile additive manufacturing apparatus is moving, wherein an amount of the at least the first concrete material dispensed is controlled by the digital model; and

wherein the controller comprises an artificial intelligence processing chip, and an artificial intelligence algorithm is used to create the digital model.

8 . The method of claim 7 wherein a gantry supports at least a first printing head to control the deposit of the at least the first concrete material.

9 . The method of claim 7 wherein the first surface is one of a concrete slab or a graded ground.

Continuity (3)
Continuation 16878136 · May 19, 2020
Continuation 16324058
Related Publication 20250059715A1 · Feb 20, 2025
References Cited (94)
US 2082259A · Peden · 1937 [cited by applicant]
US 2126869A · William et al. · 1938 [cited by applicant]
US 4842441A · Watkins · 1989 [cited by applicant]
US 4881361A · Dalton · 1989 [cited by applicant]
US 5217653A · Mashinsky et al. · 1993 [cited by applicant]
US 5294210A · Lemelson · 1994 [cited by applicant]
US 5333969A · Blaha et al. · 1994 [cited by applicant]
US 5439313A · Blaha et al. · 1995 [cited by applicant]
US 5614670A · Nazarian et al. · 1997 [cited by applicant]
US 6186700B1 · Omann · 2001 [cited by applicant]
US 6206607B1 · Medico, Jr. et al. · 2001 [cited by applicant]
US 6299934B1 · Manning · 2001 [cited by applicant]
US 6391251B1 · Keicher et al. · 2002 [cited by applicant]
US 7445441B2 · West et al. · 2008 [cited by applicant]
US 8414280B2 · Pettis · 2013 [cited by applicant]
US 9231498B2 · Hashimoto et al. · 2016 [cited by applicant]
US 9255364B2 · Ichikawa · 2016 [cited by applicant]
US 9303368B2 · Reda · 2016 [cited by applicant]
US 9637870B1 · Coe · 2017 [cited by applicant]
US 9903078B2 · Ali · 2018 [cited by applicant]
US 9948898B2 · Barfield et al. · 2018 [cited by applicant]
US 9970758B2 · Shah et al. · 2018 [cited by applicant]
US 10066348B2 · Yun et al. · 2018 [cited by applicant]
US 10087589B2 · Yun et al. · 2018 [cited by applicant]
US 10190269B2 · Shah et al. · 2019 [cited by applicant]
US 20010020058A1 · Kamaishi et al. · 2001 [cited by applicant]
US 20030069668A1 · Zurn · 2003 [cited by applicant]
US 20050065400A1 · Banik et al. · 2005 [cited by applicant]
US 20060240183A1 · Pollard et al. · 2006 [cited by applicant]
US 20060258912A1 · Belson et al. · 2006 [cited by applicant]
US 20070164641A1 · Pelrine et al. · 2007 [cited by applicant]
US 20080253834A1 · Colvard · 2008 [cited by applicant]
US 20080276834A1 · Jorda · 2008 [cited by applicant]
US 20090000323A1 · Walker · 2009 [cited by applicant]
US 20100041795A1 · Wilson, Jr. · 2010 [cited by applicant]
US 20100121476A1 · Kritchman · 2010 [cited by applicant]
US 20100275817A1 · Williams et al. · 2010 [cited by applicant]
US 20110089610A1 · El-Siblani et al. · 2011 [cited by applicant]
US 20110146111A1 · Pender · 2011 [cited by applicant]
US 20110219899A1 · Dize et al. · 2011 [cited by applicant]
US 20110233105A1 · Bailey · 2011 [cited by applicant]
US 20120031724A1 · Noll et al. · 2012 [cited by applicant]
US 20130051913A1 · Eul · 2013 [cited by applicant]
US 20130295338A1 · Keating et al. · 2013 [cited by applicant]
US 20140084517A1 · Sperry et al. · 2014 [cited by applicant]
US 20140203479A1 · Teken et al. · 2014 [cited by applicant]
US 20140232035A1 · Bheda · 2014 [cited by applicant]
US 20140268604A1 · Wicker et al. · 2014 [cited by applicant]
US 20140374933A1 · Flitsch et al. · 2014 [cited by applicant]
US 20140374935A1 · Flitsch · 2014 [cited by examiner]
US 20150079214A1 · Shi et al. · 2015 [cited by applicant]
US 20150132425A1 · Lacaze et al. · 2015 [cited by applicant]
US 20150140150A1 · Schmehl et al. · 2015 [cited by applicant]
US 20150171305A1 · Hashimoto et al. · 2015 [cited by applicant]
US 20160032536A1 · Reda · 2016 [cited by applicant]
US 20160032540A1 · Reda · 2016 [cited by applicant]
US 20160093212A1 · Barfield et al. · 2016 [cited by applicant]
US 20160136730A1 · McMurtry et al. · 2016 [cited by applicant]
US 20160209511A1 · Dolinar et al. · 2016 [cited by applicant]
US 20160229124A1 · Yoshikawa · 2016 [cited by applicant]
US 20160236411A1 · Ohnishi · 2016 [cited by applicant]
US 20160325498A1 · Gelbart · 2016 [cited by applicant]
US 20170001379A1 · Long · 2017 [cited by applicant]
US 20170129180A1 · Coates et al. · 2017 [cited by applicant]
US 20170136697A1 · Kia et al. · 2017 [cited by applicant]
US 20170145640A1 · Coe · 2017 [cited by applicant]
US 20170204569A1 · Shah et al. · 2017 [cited by applicant]
US 20170226709A1 · Ali · 2017 [cited by applicant]
US 20170238595A1 · Atureliya · 2017 [cited by applicant]
US 20170246684A1 · Hellestam · 2017 [cited by applicant]
US 20170298580A1 · Flitsch et al. · 2017 [cited by applicant]
US 20170305137A1 · Flitsch et al. · 2017 [cited by applicant]
US 20170314918A1 · Shah et al. · 2017 [cited by applicant]
US 20170342669A1 · Yun et al. · 2017 [cited by applicant]
US 20170350698A1 · Shah et al. · 2017 [cited by applicant]
US 20170370053A1 · Yun et al. · 2017 [cited by applicant]
US 20170372480A1 · Anand et al. · 2017 [cited by applicant]
US 20180297115A1 · Diwinsky et al. · 2018 [cited by applicant]
US 20180370148A1 · Sekine et al. · 2018 [cited by applicant]
US 20190055699A1 · Flitsch et al. · 2019 [cited by applicant]
US 20190111619A1 · Schalk et al. · 2019 [cited by applicant]
US 20190147331A1 · Arditi · 2019 [cited by applicant]
US 20190154442A1 · Annovi et al. · 2019 [cited by applicant]
US 20190160743A1 · Matsubara et al. · 2019 [cited by applicant]
US 20190188501A1 · Ryu · 2019 [cited by applicant]
US 20190240912A1 · Alves · 2019 [cited by applicant]
CN 202202237U · 2012 [cited by applicant]
CN 102953312A · 2013 [cited by applicant]
EP 2772626A1 · 2014 [cited by applicant]
WO 2009048828A1 · 2009 [cited by applicant]
WO 2012087150A1 · 2012 [cited by applicant]
A. Cubero-Fernandez, J. Rodriguez-Lozano, R. Villatoro, J. Olivares and J. Palomares, “Efficientpavement crack detection and classification,” EURASIP Journal on Image and Video Processing, 2017 (Year: 2017). [cited by applicant]
G. Younes, C. Attia and Z. Djelloul, “Supervised learning and automatic recognition of asphalt pavement deteriorations,” In Proc. 2009 IEEE/ACS International Conference on ComputerSystems and Applications, pp. 205-210, … [cited by applicant]
L. Marques, R. Williams, W. Zhou, “A Mobile 3D Printer for Cooperative 3D Printing,” In Proc. 28th Annual International Solid Freeform Fabrication Symposium, pp. 1645-1660, 2017 (Year: 2017). [cited by applicant]