Leonardo Vitaletti from FIME presented on applying Design for Six Sigma and Minitab applications to a motor project. He summarized using the DMAIC approach of define, measure, analyze, improve, and control. Key aspects included defining objectives, measuring current performance, analyzing the stamping process using DOE, improving through optimization, and controlling with control charts. Analysis found hot injection stamping reduced variability compared to other methods. DOE identified significant factors and their interactions. Optimization reduced oscillation and imbalance predictions. Future work includes improving correlation of intermediate parameters and defining acceptability windows.

1. Design for Six Sigma ed applicazioni Minitab in FIME
Elica Motors
Milano – 17 Maggio 2017
Leonardo Vitaletti – R&D Manager Fans & Motors

2. 2
Elica Corporation
Today N#1 Player
Worldwide in Hoods
2016 Turnover
439.3 € M
3,600 Employees

3. 3
19 Mln
Hoods + Motors
Cooking Net Sales:
Own Brand 44%
Client Brand 56 %
2’ & 3’ Player produce
respectively 30% & 60%
less than Elica

4. 4Elica Corporation Industrial Sites: 8
Unique world wide player

5. 5
Refrigeration: BLDC Motors Heating: Gas Blowers
Appliances: Induction Fans
and Motors
Ventilation: Induction Fans
and Motors
FIME Motor Business

6. 6
Esempio di applicazione dell’approccio Six Sigma su un progetto

7. 7
DEFINE
MEASURE
ANALYZE
IMPROVE / DESIGN
CONTROL / VERIFY
ANALISI DEL VALORE
DOE
R&R
PPAP
CONTROL PLAN
ANALISI DI PROCESSO
CONFRONTO TECNOLOGIE DI STAMPAGGIO
OBIETTIVI DA PROJECT CHARTER
CONFRONTO TECNOLOGIE DI STAMPAGGIO
Approccio progettuale

8. 8
DEFINE
MEASURE
ANALYZE
IMPROVE /
DESIGN
CONTROL
/ VERIFY
Project Charter: Obiettivi, Business Case e Specifica

9. 10
DEFINE
MEASURE
ANALYZE
IMPROVE /
DESIGN
CONTROL
/ VERIFY
Tecnologia e specifiche tecniche
Squilibrio statico max
Diametro mozzo
Oscillazione

10. 11
DEFINE
MEASURE
ANALYZE
IMPROVE /
DESIGN
CONTROL
/ VERIFY
Casi in esame

11. 12
DEFINE
MEASURE
ANALYZE
IMPROVE /
DESIGN
CONTROL
/ VERIFY
R&R equilibratrice

12. 13
DEFINE
MEASURE
ANALYZE
IMPROVE /
DESIGN
CONTROL
/ VERIFY
Analisi di del processo si stampaggio
Confronto tra le tecnologie (1)
S.G. S.G.
H.I. H.I.
1,27
0,83

13. 14
DEFINE
MEASURE
ANALYZE
IMPROVE /
DESIGN
CONTROL
/ VERIFY
Distribution ID Plot for ST
Descriptive Statistics
N N* Mean StDev Median Minimum Maximum Skewness
Kurtosis
Box-Cox transformation: λ = 0,5
Johnson transformation function:
-3,23071 + 3,54161 × Asinh( ( X + 0,00308819 ) / 0,0123969 )
Goodness of Fit Test
Distribution AD P LRT P
Normal 0,749 0,050
Box-Cox Transformation 0,361 0,443
Lognormal 2,796 <0,005
3-Parameter Lognormal 0,211 * 0,000
Exponential 16,396 <0,003
2-Parameter Exponential 13,704 <0,010 0,000
Weibull 0,328 >0,250
3-Parameter Weibull 0,315 >0,500 0,822
Smallest Extreme Value 4,299 <0,010
Largest Extreme Value 0,501 0,217
Gamma 0,920 0,022
3-Parameter Gamma 0,222 * 0,010
Logistic 0,518 0,148
Loglogistic 1,315 <0,005
3-Parameter Loglogistic 0,259 * 0,000
Johnson Transformation 0,202 0,878
H. I.
Analisi sulla del processo Hot Injection
Analisi sulla distribuzione

14. 15
DEFINE
MEASURE
ANALYZE
IMPROVE /
DESIGN
CONTROL
/ VERIFY
7654321
N Lotto
Interval Plot of ST vs N Lotto
95% CI for the Mean
standard deviations are used to calculate the intervals.
H. I.
H. I.
Analisi sulla del processo Hot Injection
ANOVA

15. 16
DEFINE
MEASURE
ANALYZE
IMPROVE /
DESIGN
CONTROL
/ VERIFY
Analisi sulla del processo Hot Injection
Control Charts
TEST 1. One point more than 3,00 standard
deviations from center line.
Test Failed at points: 5

16. 17
DEFINE
MEASURE
ANALYZE
IMPROVE /
DESIGN
CONTROL
/ VERIFY
Analisi sulla del processo Hot Injection
Ipotesi sulla variabilità tra lotti
Fluidità
(proporzionalea1/viscosità;misuratain
mmdiscorrimentodelmaterialesuuna
spirale)
Umidità relativa [%]
Fluidità vs Umidità relativa

17. 18
DEFINE
MEASURE
ANALYZE
IMPROVE /
DESIGN
CONTROL
/ VERIFY
Analisi di del processo si stampaggio
Confronto tra le tecnologie (2)
S.G. S.G.
H.I. H.I.
1,27
0,83
1,88
H.I. _ step1 H.I. _ step1

18. 19
DEFINE
MEASURE
ANALYZE
IMPROVE /
DESIGN
CONTROL
/ VERIFY
DOE
Ishikawa stampaggio girante

19. 20
DEFINE
MEASURE
ANALYZE
IMPROVE /
DESIGN
CONTROL
/ VERIFY
DOE
Schema funzionale stampaggio girante
T Pc Pm
Parametri di controllo (input e noise)
x1 x2 xn
Parametri intermedi di funzionamento
X1 X2 Xn
Prestazione finale da ottimizzare
Y1 Y2 Yn
t
t_Tmax
Pmax
Squilibrio ST Peso
H.I.PROCESS
Oscillazione
Tmax
T_Pmax
T_amb: costante
Materia Prima: singolo batch
Tempo di essiccamento
v
FULLFACTORIALPLAN – 5 factors
MEAN & VARIABILITYANALYSIS – 5 trials per factor

20. 21
DEFINE
MEASURE
ANALYZE
IMPROVE /
DESIGN
CONTROL
/ VERIFY
DOE
Analisi Correlazione parametri intermedi di funzionamento
Correlation: Stat; Oscill.; Weight; Tm; t_Tm; Pm; t_Pm
Stat Oscill. Weight Tmax t_Tmax Pmax
Oscill. 0,128
0,125
Weight -0,769 -0,125
0,000 0,133
Tmax -0,064 -0,289 0,243
0,424 0,000 0,002
t_Tmax -0,349 0,070 0,396 -0,070
0,000 0,400 0,000 0,380
Pmax -0,742 -0,400 0,859 0,299 0,297
0,000 0,000 0,000 0,000 0,000
T_Pmax 0,191 0,093 -0,178 -0,170 0,733 -0,178
0,016 0,264 0,024 0,032 0,000 0,024
Cell Contents: Pearson correlation
P-Value

21. 22
DEFINE
MEASURE
ANALYZE
IMPROVE /
DESIGN
CONTROL
/ VERIFY
DOE
Risultati Squilibrio:ANOVAInteraction & main effects
3010
1150950 7030
300270 1050500
Molding Temperature
Molding pressure (commutation)
Molding pressure (maintaining)
Injction speed
Cooling time
7
6
5
4
3
Molding Temperature Molding pressure (commutation) Molding pressure (maintaining) Injction speed Cooling time
T
Pc
Pm
t
v
T Pc Pm tv
T
Pc
Pm
t
v

22. 23
DEFINE
MEASURE
ANALYZE
IMPROVE /
DESIGN
CONTROL
/ VERIFY
DOE
Factorial DesignAnalisi
DOE STAT - 5 TERM
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0,0052046 54,88% 49,24% 40,91%
DOE OSCILL - 5 TERM
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0,0312392 89,54% 87,95% 86,00%
DOE WEIGHT - 5 TERM
Model Summary
S R-sq R-sq(adj) R-sq(pred)
0,166853 97,97% 97,64% 97,26%
Term
ABCD
ABDE
ABCDE
AD
ACDE
BDE
AB
E
ACE
BD
ABC
D
BC
C
AC
76543210
A Molding Temperature
B Molding pressure (commutation)
C Molding pressure (maintaining)
D Injction speed
E Cooling time
Factor Name
Standardized Effect
1,448
Pareto Chart of the Standardized Effects
(response is Stat; α = 0,15)
0,010,00-0,01
99,9
99
90
50
10
1
0,1
Residual
Percent
0,0300,0250,0200,0150,010
0,010
0,005
0,000
-0,005
-0,010
Fitted Value
Residual
0,0080,0040,000-0,004-0,008-0,012
20
15
10
5
0
Residual
Frequency
160
150
140
130
120
110
1009080706050403020101
0,010
0,005
0,000
-0,005
-0,010
Observation Order
Residual
Normal Probability Plot Versus Fits
Histogram Versus Order
Residual Plots for Stat
A: T
B: Pc
C: Pm
E: t
D: v

23. 24
DEFINE
MEASURE
ANALYZE
IMPROVE /
DESIGN
CONTROL
/ VERIFY
DOE
Risultati ed ottimizzazione
Partendo dai valori nominali usati:
… (setup valori) …
Oscill prediction fit 0,320394
Stat prediction fit 0,0163544
Minimizzando OSCILL e ST:
… (setup valori) …
Oscill prediction fit 0,168631
Stat prediction fit 0,0062839
Pc
v
t
Pmax
t_Pma
1000900800700600500
Contour Plot of Weight; Oscill.; Stat
Molding
Molding
Weight =
Oscill. =
Stat = 0,
Nominal values
PRESS Max = 325,7 time PRESS Max = 3,777
T Pc Pm t v

24. 25
DEFINE
MEASURE
ANALYZE
IMPROVE /
DESIGN
CONTROL
/ VERIFY
Analisi di del processo si stampaggio
Confronto tra le tecnologie (3)
S.G. S.G.
H.I. H.I.
1,27
0,83
1,88
H.I. _ step1
0,0280,0240,0200,0160,0120,0080,004
LSL *
Target *
USL 0,03
Sample Mean 0,00939111
Sample N 90
StDev(Overall) 0,00310992
StDev(Within) 0,00311867
Process Data
Pp *
PPL *
PPU 2,21
Ppk 2,21
Cpm *
Cp *
CPL *
CPU 2,20
Cpk 2,20
Potential (Within) Capability
Overall Capability
PPM < LSL * * *
PPM > USL 0,00 0,00 0,00
PPM Total 0,00 0,00 0,00
Observed Expected Overall Expected Within
Performance
USL
Overall
Within
Process Capability Report for Squilibrio Statico
0,0200,0150,0100,0050,000
99
95
90
80
70
60
50
40
30
20
10
5
1
Percent
Probability Plot of Squilibrio St
Normal - 95% CI
2,21
H.I. _ step2

25. 26
DEFINE
MEASURE
ANALYZE
IMPROVE /
DESIGN
CONTROL
/ VERIFY
Analisi sulla Variabilità
ation) Molding pressure (maintaining) Injction speed Cooling time
1150
950
1150
950
1050
500
1050
500
1050
500
1050
500
70
30
70
30
70
30
30
70
30
70
30
70
30
30
30
30
10
30
10
30
10
30
30
10
30
10
10
30
10
30
10
30
10
30
30
10
30
10
0,60,50,40,30,20,10,0
P-Value 0,997
P-Value 0,859
Multiple Comparisons
Levene’s Test
ding Temperature; Molding pressure (commutation); Molding pressure (mai
Multiple comparison intervals for the standard deviation, α = 0,05
If intervals do not overlap, the corresponding stdevs are significantly different.
ation) Molding pressure (maintaining) Injction speed Cooling time
1150
950
1150
950
1050
500
1050
500
1050
500
1050
500
70
30
70
30
70
30
70
30
70
30
70
30
70
30
30
30
10
30
10
30
10
30
10
30
10
30
10
30
10
30
10
30
10
30
10
30
10
30
10
30
30
10
30
10
2,01,51,00,50,0
P-Value 0,000
P-Value 0,344
Multiple Comparisons
Levene’s Test
lding Temperature; Molding pressure (commutation); Molding pressure (ma
Multiple comparison intervals for the standard deviation, α = 0,05
If intervals do not overlap, the corresponding stdevs are significantly different.
e
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0,040,030,020,010,00
P-Value 0,095
P-Value 0,820
Multiple Comparisons
Levene’s Test
commutation); Molding pressure (main
or the standard deviation, α = 0,05
ignificantly different.
Pc Pm v t
Squilibrio ST
Oscillazione
Peso

26. 27
DEFINE
MEASURE
ANALYZE
IMPROVE /
DESIGN
CONTROL
/ VERIFY
Release progetto e Sviluppi
PPAP
Life test
Omologa processo interno
Carte di controllo
Sviluppi futuri
• Miglioramento correlazione parametri intermedi – CTQ
• Definizione finestre di accettabilità
Pressione[Pa]
Tempo [s]

27. 28
The future belongs
to those who believe in the beauty of their dreams
Eleanor Roosevelt
Thank You
Leonardo Vitaletti – R&D Manager Fans & Motors