Smart and Connected Machines to Deliver More Value to Customers Pierangelo Dolera and Cesare Reina, Atom (Italy)

1. Moving towards
sustainability
through innovation

2. SMART AND CONNECTED MACHINES TO DELIVER MORE
VALUE TO THE CUSTOMERS
ATOM S.p.A.
Pierangelo Dolera
Sales Director
Cesare Reina
R&D Engineer

3. Intervention (often on site)
of the technician for trouble
shooting
It was just a broken
electrical wire!
Minor issue -> big loss
Operator clearly
disappointed….!!!!
Long waiting time
Contact
supplier
service center
PLEASE CHECK THAT CHUCK IS
PLUGGED IN
IF THE PROBLEM PERSISTS
REQUEST MAINTENANCE
INTERVENTION
Machine failure and consequent downtime
AS-IS
Machine stops working

4. Machine failure and consequent downtime
AS-IS
Customer satisfaction
Loss of money
Productivity is reduced
Loss of time

5. Machine components status is
continuously monitored while
machine is working
Data from machine’s sensors are
constantly compared with data
stored on a cloud database.
Cloud database is fed by
connected machines working
globally.
Machine failure in a Zero downtime environment
A warning is displayed when
critical machine components are
about to fail.
Spare parts can be
purchased in advance
and maintenance
intervention is scheduled
before the component
failure.
Machine component is
replaced timely without
causing downtime.

6. The machine self-diagnosis system for critical parts is capable of:
• Avoiding Unexpected machine stop
• Enabling proactive diagnosis
• Allowing cost effective components purchase
• Improving maintenance planning
The system acts in a preventive way thanks to a centralized coordination at ATOM service centers. Customer’s fleet of
working machines is kept under control directly by dedicated Atom cloud infrastructure.
PLEASE CHECK THAT CHUCK IS
PLUGGED IN
IF THE PROBLEM PERSISTS
REQUEST MAINTENANCE
INTERVENTION
Machine failure in a Zero downtime environment

7. Production planning
AS-IS
It’s too late...our
competitor was
faster!
Monday
8:45
Production manager starts
computing
Wednesday
16:30
Results about production
planning are analised. Production
manager decide to send orders...
Monday
8:00
Monday
8:00
Production manager in HQs receives
order to be processed: he needs to
decide where to produce
Monday
8:45
Wednesday
16:30

8. Monday
8:00
Production manager in HQs receives
orders to be processed: he needs to
decide where to produce
Monday
13:00
Production orders are received
from factories
Production planning and machine use
Machine/Plant simulation and expert support
Monday
8:45
Production manager fills in the
system with the received
information, directly from order
form.
Monday
10:00
Production planning and
production process is
generated.
Monday
8:00
Monday
8:45
Monday
10:00
Monday
13:00

9. Support system provides expert driving of the machine and/or to the planning of production
activities. Thanks to a historical database of processing parameters (materials, layout,
machine), the system guides the operators in choosing the optimal process parameters. Data
is collected from all customers and the best-practices on machine usage and machine
optimization are shared towards customers.
Total production time
Number of machines
Number of pieces
Energy consumption
Production planning
Knife type
Cutting speed
Overcut
...
...
Optimal process parameters
Production planning and machine use
Machine/Plant simulation and expert support

10. Real-time performance monitoring
Synthesize process know-how in a
structured and reusable way
BUY
BUY
CONFIRM
ADDED
AUTOMATIC ORDER
SPARE PARTS
Machine sensors and function
Condition monitoring and condition-based
maintenance (CBM)
BUY

11. Activity Goal Variable
Knife-material
interaction
identification
Machine
performance
Net time/total time
Knife condition
monitoring
Cutting quality
Cutting edge condition
Knife breakage and or/partial chipping
Replace the operator evaluation with an
automatic, repeatable and reliable
evaluation
Automatic knife
identification
Correct data
collection
Knife model
Cutting knife monitoring

12. Machine components condition monitoring
Motion system
Cutting head and tools
Chuck

13. Vacuum system
Pneumatic
automation
system
Conveyour belt
Machine components condition monitoring

14. Cutting process
monitoring system
Process parameters
knowledge-base
creation
Actual
production
Expected production
100
75
50
25
0
Cutting quality
Data from
sensors
Production drift
Production
orders
Process
parameters
Energy and air consumption
Cutting process monitoring

15. Architecture

16. Cloud PAAS applications reference architecture

17. THANK YOU FOR YOUR ATTENTION