US 10,054,929 B1Granted

Intelligent machine tools

AssigneeAccenture Global Solutions Limited

InventorMichael L. George

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Abstract

Methods and systems for generating sensor data, wherein the sensor data includes measured transit time information for items flowing through a work process, accessing a set of control data for one or more machine tool workstations and accounting data for the work process, calculating a standard deviation of the measured transit time information, calculating an achievable minimum WIP for at least one of the workstations using current values of workstation performance parameters, receiving input including: information identifying process improvement projects and corresponding predictive performance parameter values, and information identifying resources available for process improvement, for the at least one of the workstations: determining an achievable minimum WIP using the corresponding predictive performance parameter values, determining a reduction in minimum WIP based on the difference between the achievable minimum WIP for the current performance parameter values and the predictive performance parameter values, and determining one or more process efficiency improvements.

Claims19

Dependent claims

A system comprising:

a collection of machine tool workstations used in a work process, wherein each workstation is associated with a set of performance parameters and one or more of the machine tool workstations comprise (i) a cutting tool, (ii) a drilling machine, (iii) a lathe, (iv) a boring machine or (v) a grinding machine;
one or more data sensors, the one or more data sensors:
in data communication with each other, and
performing operations comprising:
generating a set of sensor data, wherein the sensor data includes measured transit time information for items flowing through the work process,
accessing a set of production control data for each workstation and a set of accounting data for the work process,
calculating a standard deviation of the measured transit time information, and
calculating an achievable minimum work in progress (WIP) for each workstation using current values of workstation performance parameters, comprising calculating an achievable minimum WIP for each workstation using
W
MIN
≅
SQm
2
⁢
(
1
-
X
-
m
⁡
(
P
~
/
(
1
-
M
)
)
)
+
Q
wherein S represents the workstation setup time, Q represents the workstation number of different part numbers, m represents the units produced by the workstation per unit time, X represents the workstation scrap percentage, {tilde over (P)} represents the workstation machining time per unit, and M represents the machine downtime;
receiving input including (i) information identifying process improvement projects and corresponding predictive performance parameter values, and (ii) information identifying resources available for process improvement;
for each workstation (i) determining an achievable minimum WIP using the corresponding predictive performance parameter values, and (ii) determining a reduction in minimum WIP based on the difference between the achievable minimum WIP for the current values of performance parameters and the predictive performance parameter values;
and
determining one or more process efficiency improvements associated with the determined reductions in minimum WIP, the accounting data and the calculated standard deviation.

36 more elements

A computer implemented method, comprising:

generating a set of sensor data, wherein the sensor data includes measured transit time information for items flowing through a work process;
accessing a set of production control data for one or more machine tool workstations and a set of accounting data for the work process, wherein one or more of the machine tool workstations comprise (i) a cutting tool, (ii) a drilling machine, (iii) a lathe, (iv) a boring machine or (v) a grinding machine;
calculating a standard deviation of the measured transit time information;
calculating an achievable minimum work in progress (WIP) for at least one of the one or more workstations using current values of workstation performance parameters, comprising calculating an achievable minimum WIP for each workstation using
W
MIN
≅
SQm
2
⁢
(
1
-
X
-
m
⁡
(
P
~
/
(
1
-
M
)
)
)
+
Q
wherein S represents the workstation setup time, Q represents the workstation number of different part numbers, m represents the units produced by the workstation per unit time, X represents the workstation scrap percentage, {tilde over (P)} represents the workstation machining time per unit, and M represents the machine downtime;
receiving input including:
information identifying process improvement projects and corresponding predictive performance parameter values, and
information identifying resources available for process improvement;
for the at least one of the one or more workstations:
determining an achievable minimum WIP using the corresponding predictive performance parameter values, and
determining a reduction in minimum WIP based on the difference between the achievable minimum WIP for the current values of performance parameters and the predictive performance parameter values;
and
determining one or more process efficiency improvements associated with the determined reductions in minimum WIP, the accounting data and the calculated standard deviation.

36 more elements

A computer-readable storage device encoded with a computer program, the program comprising instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising:

generating a set of sensor data, wherein the sensor data includes measured transit time information for items flowing through a work process;
accessing a set of production control data for one or more machine tool workstations and a set of accounting data for the work process, wherein one or more of the machine tool workstations comprise (i) a cutting tool, (ii) a drilling machine, (iii) a lathe, (iv) a boring machine or (v) a grinding machine;
calculating a standard deviation of the measured transit time information;
calculating an achievable minimum work in progress (WIP) for at least one of the one or more workstations using current values of workstation performance parameters, comprising calculating an achievable minimum WIP for each workstation using
W
MIN
≅
SQm
2
⁢
(
1
-
X
-
m
⁡
(
P
~
/
(
1
-
M
)
)
)
+
Q
wherein S represents the workstation setup time, Q represents the workstation number of different part numbers, m represents the units produced by the workstation per unit time, X represents the workstation scrap percentage, {tilde over (P)} represents the workstation machining time per unit, and M represents the machine downtime;
receiving input including:
information identifying process improvement projects and corresponding predictive performance parameter values, and
information identifying resources available for process improvement;
for the at least one of the one or more workstations:
determining an achievable minimum WIP using the corresponding predictive performance parameter values, and
determining a reduction in minimum WIP based on the difference between the achievable minimum WIP for the current values of performance parameters and the predictive performance parameter values;
and
determining one or more process efficiency improvements associated with the determined reductions in minimum WIP, the accounting data and the calculated standard deviation.

36 more elements

Dependencies are inferred from the claim text.

Facts

Application: 15/056,024Filed 29 Feb 2016 · us provisional application
Priority: 2 Jul 2015
Granted: 21 Aug 2018
Term ends: Not estimatedThe source records don’t support an estimate.

Classification6 codes

G05B 19/41835
Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible…:…
G05B 2219/31412
From computer integrated manufacturing till monitoring: calculate machining time, update as function of load, speed
G05B 2219/31449
From computer integrated manufacturing till monitoring: monitor workflow, to optimize business, industrial processes
Y02P 90/02
Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation: total factory control, e.g. smart factories,…