COVER PAGE (1 PAGE)
Program Name: MTConnect Challenge
Title of Idea:
Development using MTConnect
Organization (if applicable):
650 812 4720
Date of Proposal:
SECTION I: ABSTRACT
(200 WORDS MAXIMUM),
centric enterprise needs rapid communication
between engineering, planning and
This is typically accomplished by
organizational structure dedicated to
s through their life
paradigm doesn’t extend
mix low volume or agile production facilities.
a solution for operators in the shop floor
interfacing seamlessly with MTConnect enabled
to provide feedback to designers and manufacturi
planners with minimal effort. This
enable rapid process improvements/redesigns
and build an enterprise knowledge base for
defects and best practice identification
approach utilizes MTConnect to establish
In software parlance, “a stack trace” would be automatically created for
This can be
and emailed/made available for retrieval based on a
workflow established in the PLM system.
supervised learning will
resulting in a self
reporting intelligent machine
The primary benefits of the proposed system would be to accelerate
This is accomplished by creating a h
igher level of supply chain
including cost reduction
and improving manufacturing
SECTION II: PROPOSED
The goal is to bring “
intelligence” to machines
using MTConnect to provide the
with intelligence provided from multiple sources including the human operator,
during planning, and
algorithms supported by a library of signatures of
This idea is best illustrated with some uses cases of this solution.
Operator hears a chatter and
uses speech to
report that he heard chatter.
software can create a label to
Now, the solution
would pull the MTConnect data associated with location during program execution,
status of various components, assets used including specific tool, current life of the tool, part
number, lot sizes,
from previous 5 parts at the
me location, any reports on same part
on other machine at the same location
and email it to the manufacturing process engineer.
Operator applies feed rate override. This can be identified without reporting and
packaged along with component an
d location in tool path to manufacturing process planner to
revise the program supported by simulations to provide feedback to shop floor operator.
would be connected to a machine learning system to learn the model for shop floor operation.
There are other situations involving alternate tool recommendations, lack of chip clearance,
excess power drawn, small clearance for part, worn tools,
, rapid tool wear
where this information can be used to re
fine manufacturing process pla
he operator could
provide feedback on excessive burring for certain features, poor finish observed on certain
location to enable designers consider redesign alte
This will enable introducing agile development concepts commonly employed in
development and system level problem solving to product design and supply chain enabled by
SECTION III: TECHNIC
solution will be implemented in 2 phases:
In the first phase, information to supplement observed defect or suggested process improvement
could be a verbal description of the condition from the operator
through speech or selection
a set of labels
A standard set of or custom defined manufacturing stack trace
would be collected and bundled along with the operator description of the condition.
This will be
routed to the appropriate manufacturing/design engineer bas
ed on label classification.
information would be
used as a training set for
a machine learning framework that analyzes the
MTConnect data to automatically detect signatures that are associated with “labels”
Additional information can be obtained from simulations to predict
expected values of parameters monitored with MTConnect
to train a better model
. This builds
model specific to the machine
representing enterprise knowledge
in a supervised learning
while providing immediate pay back for the factory. At the end of phase1, there would
be a library of models that are machine specific for various common defects encountered during
Agile Product and Process Development using MTConnect
operation. This information can also be mined in other forms, where the la
bels can be used to
prior solutions applied for similar solutions.
Intelligent Machine Tool
An automated diagnosis from machine specific models is utilized and validated by operator
when probability of detection i
s below a threshold.
In the second phase of this implementation,
the role of human operator would be to provide secondary feedback. The knowledge would be
supplemented with relevant MTConnect information from part genealogy and machine history to
intelligent machine that would self
report process improvement/defect prevention
An analogy of the first phase of proposed idea would be a program crash reporting tool for
computer aided design (CAD) platform that bundles the component, st
age of operation,
necessary diagnostic information and an automated workflow to direct these reports for bug
fixing and product improvements. The second phase would be similar to automated development
of software design patterns.
SECTION IV: BENEF
Shop operator utilization
four percent of the survey respondents reported that workforce shortages or skills
deficiencies in production roles are having a significant impact on their ability to expand
operations or improve productivity .
Proposed approach will improve the utilization of
operators in shop floor
, increasing availability of operator while expanding their sphere of
influence within the enterprise
The knowledge worker in a factory would now be able to
participate in a
larger segment of product development cycle improving their impact.
Accelerating learning curve
Learning curves can vary significantly in a given industry or even within a given organization
across different facilities. Studies by Lapre on organizatio
nal learning cites case studies in
manufacturing where organizational learning curve with in an industry can vary by a factor of 2.
Epple et al.  study showed that labor productivity stayed the same for first shift when switched
from operating one shif
t to two shifts at roughly the same rate as the plant did before the switch,
whereas almost no learning occurred on the night shift. Similar studies exist in semiconductor
manufacturing where the starting yields could be off by a factor of 8 and significan
rates of learning . MTConnect provides a significant opportunity to facilitate learning in the
shop and democratize operator contributions by combining user input with a supervised learning
This could lead up to a 2 fold increase i
n rate of learning.
There is also a direct impact of learning in the shop floor that facilitates acceleration of learning
A second study by Lapre states that the operationally (shop floor) validated theories
accelerate the learning curve by 3.2
, obtaining reduced costs earlier in product life cycle
Rapid inclusion of feedback from shop floor operators
planning and product redesign would accelerate
operators and shops
by providing necessary information for decision makers by integrating this
to PLM systems and creation of new enterprise knowledge by development of machine models
and fault signatures based on MTConnect data/intelligence.
Lapré, Michael A. "Inside the organizational learning curve: Understanding the
organizational learning process."
Foundations and Trends® in Technology, Information and
4.1 (2010): 1
Michael A., and Luk N. Van Wassenhove. "Managing learning curves in factories by
creating and transferring knowledge."
California Management Review
46.1 (2003): 53
D. Epple, L. Argote, and K. Murphy, “An empirical investigation of the
knowledge acquisition and transfer through learning by doing,”
, vol. 44, no.
1, pp. 77
 N. W. Hatch and J. H. Dyer, “Human capital and learning as a source of sustainable
, vol. 25, no. 12, pp. 1155