Available online at www.sciencedirect.com
ScienceDirect Procedia CIRP 29 (2015) 508 – 513
The 22nd CIRP conference on Life Cycle Engineering
Application of Life Cycle Assessment (LCA) and Design of Experiments (DOE) to the monitoring and control of a grinding process Diogo A. L. Silvaa,*, Remo A. P. Filletia, André L. Christoforob, Eraldo J. Silvaa, Aldo R. Omettoa a
Department of Production Engineering, University of São Paulo, Av. Trabalhador São Carlense, 400, São Carlos - 13566-690, Brazil b Department of Civil Engineering, Federal University of São Carlos, Washington Luiís km 235, São Carlos - 13565-905, Brazil
* Corresponding author. Tel.: +55-16-3373-8608; fax:+55-16-3373-9425.E-mail address:
[email protected]
Abstract The number of studies on green manufacturing has increased over the past years and particular focus has been placed on machining processes and the application of Life Cycle Assessment (LCA). This paper reports the results of the use of a modeling approach that combines Life Cycle Assessment (LCA) and Design of Experiments (DOE) to investigate a cylindrical plunge grinding for 21-2N steel. The effect of two process parameters on the LCA results of the grinding machining was studied through an analysis of variance (ANOVA). The parameters investigated were type of CBN grinding wheel (JB126 K150 VSS and 8B126 K150 VT2) and different levels of specific material removal rate (50, 100, 150 and 200 mm³/mm.min). © 2015 2015 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. This is an open access article under the CC BY-NC-ND license © Peer-review under responsibility of the International Scientific Committee of the Conference “22nd CIRP conference on Life Cycle (http://creativecommons.org/licenses/by-nc-nd/4.0/). Engineering.under responsibility of the scientific committee of The 22nd CIRP conference on Life Cycle Engineering Peer-review Keywords:sustainable manufacutirng; green manufacturing; green machining; process modelling; environmental impacts; analysis of variance (ANOVA)
1. Introduction 1.1. LCA and DOE in manufacturing processes Sustainable manufacturing (SM) aims at the integration of sustainability aspects on a manufacturing level. According to the U.S. Department of Commerce, SM refers to the creation of products that minimize negative environmental impacts, conserve energy and natural resources, are safe for employees, communities and consumers and economically sound [1]. Green manufacturing (GM) is part of the SM [1, 2] related to the necessity of increases in material and energy efficiency and reduction in environmental impacts of manufacturing processes. The practical implementation of SM and GM strategies has faced some limitations, e.g., the manufacturing industry lacks scientifically-based decision support tools for their effective implementation [1, 3, 4]. However, in recent years, SM and GM studies have placed particular focus on the use of Life Cycle Assessment (LCA) as a tool to overcome such a technical barrier [2, 3, 4].
LCAs have been performed in unit processes on a factory level, mainly for machining operations, as milling [5, 6], turning [7], and grinding [3, 7-10]. However, the isolated use of LCA may not be enough to support SM and GM strategies [1, 3] and extend research on the topic. Thus, engineering concepts and techniques, such as Design of Experiments (DOE) have been recently applied towards the creation of cleaner production processes, and some examples will be addressed in the next paragraphs. DOE was introduced by Sir R. A. Fisher and is particularly important to investigate the effects of simultaneous and multiple variables (factors) on an output variable (response) [11]. When applying DOE to subsidize GM strategies, some researchers have analyzed the effects of a number of variables on the processes and/or products performance. Some recent applications of DOE to the development of GM strategies involve metal cutting, milling and turning [12-15], and wood
2212-8271 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of The 22nd CIRP conference on Life Cycle Engineering doi:10.1016/j.procir.2015.01.037
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cutting processes [16], as described in the following paragraphs. Fratila and Caizer [12] reduced the machining energy consumption by applying a Taguchi’s DOE approach and Analysis of Variance (ANOVA) to select optimal lubrication and cutting conditions in the face-milling of AIMg3. To achieve the minimum power consumption and the best surface quality simultaneously, Hanafi et al. [13] adopted a Taguchi’s DOE approach and ANOVA to optimize cutting parameters when machining PEEK-CF30 with TiN tools. Bhushan [14] applied a DOE based on response surface methodology (RSM) and ANOVA to optimize four cutting parameters, minimize power consumption and maximize the tool life in the machining of Al alloy SiC particle composites. Camposeco-Negrete [15] applied the Taguchi method and ANOVA to optimize cutting parameters and reduce electricity consumption in the AISI 6061 T6 turning process. More recently, Chompu-inwai et al. [16] studied a Fisher’s classical DOE approach and ANOVA to reduce materials consumption and minimize waste in the cutting process of a wood products manufacturing company. The above literature has shown most of the studies focused on the optimization of energy consumption in manufacturing operations. Thus, aiming to extend research on the topic the current study combines DOE and LCA as detailed below.
P
Design of Experiments
A
D
C
Fig. 1. DOE and the PDCA cycle.
This paper evaluates a combined modeling approach of LCA and DOE to investigate a cylindrical plunge grinding process of 21-2N steel. DOE was used to evaluate how changes in the process parameters (factors) can affect the LCA results (responses) of the machining process.
A DOE begins by defining the null hypothesis (H0) and the significance level (α) in the Plan (P) step of a research. Experiments are then conducted for each treatment considering all factors and replicates during step Do (D). In the Check (C) step, H0 is tested to check if the mean value of a response from a population under investigation (“treatment”) is the same or different from the mean value of another population. Finally, in the Action (A) step, the conclusions are taken based on the aim and scope of the study. DOE was applied in this study by Minitab® software version 14 for the evaluation of the grinding process. The α value was assumed as 5% and the DOE method was based on the Fisher’s classical approach and the application of an ANOVA model. The Fisher’s classical approach was adopted as it is considered more in line for use in comparison to other methods [16]. Finally, a Tukey’s test method was used in the ANOVA results as a multiple comparison procedure. The experimental design was developed according to the two grinding parameters under study and resulted in a total of 120 experiments, as discussed in section 2.1.
2. Methodology
2.1. Factors (independent variables)
The present research aims at continuing prior studies on GM topics conducted by the research groups Life Cycle Management and Engineering (LCM&E) and Laboratory for Advanced Processes and Sustainability (LAPRAS), at the University of São Paulo (USP), Brazil. This study is based on the cylindrical plunge grinding process described by Silva and Oliveira [17]. All the dataset of the experiments were extracted from Silva and Oliveira [17] and used in the combined LCA and DOE approach. The workpieces were made of 21-2N steel, which is an austenitic nitrogen-strengthened stainless steel used in a variety of applications, e.g., aerospace turbine engines, exhausted valves. Test specimens of cylindrical shape, 150 mm long, and 25.4 mm external diameter were used [17]. The machine tool was a CNC external cylindrical grinder, coupled with measuring devices regarding electric energy consumption. The wheel cutting was 80 m/s, the workpiece rotation was 200 r/min and the grinding width was 5 mm [17]. The grinding parameters (or factors) selected for evaluation in the present research were type of grinding wheel and specific material removal rate (Q’w). Regarding the DOE approach, it followed the principle of circularity of the PDCA cycle, as shown in Fig. 1.
The factors and their levels were determined for the experiments, as shown in Table 1.
1.2. Objective
Treatments
Table 1.Treatments used in the grinding tests. Number of Type of Q’w replicates grinding wheel [mm³/mm.min]
1
15
VT2
50
2
15
VT2
100
3
15
VT2
150
4
15
VT2
200
5
15
VSS
50
6
15
VSS
100
7
15
VSS
150
8
15
VSS
200
Table 1 shows the grinding machining when two different grinding wheels – JB126 K150 VSS and 8B126 K150 VT2 were used, with, respectively, four different Q’w – 50, 100, 150 and 200 mm³/mm.min. Both wheels were 400 mm in diameter, and made of vitrified cubic boron nitride (CBN). Those CBN wheels will be cited in the next paragraphs as “VSS” and “VT2”.
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2.2. Responses (dependent variables) The GaBi Professional version 6 software was used to model the LCA study and the dependent variables investigated were the environmental impacts calculated by the CML 2001 method [18]. It was only inventoried the electrical energy consumption at the wheel spindle motor during the grinding experiments, and the inventory data of the electricity generation was extracted from the GaBi database for the Brazilian context. After that, five environmental impact categories were selected: abiotic depletion (fossil) potential (ADP), acidification potential (AP), freshwater aquatic toxicity potential (FAEP), human toxicity potential (HTP), and global warming potential (GWP). The functional unit was not defined, and only measurable values for the reference flow were established. Thus, the LCA results were compared in terms of: x Two different reference flows: a) 1 s of grinding machine operation; and b) 20,000 mm³ removed material per grinding test, i.e., a complete machining of one workpiece. This comparison showed if the statistical conclusions of the DOE approach would be affected by the change in the LCA’s reference flow for the same experiment. According to the ANOVA results, p-values higher than 5% accept H0 and reject it otherwise. If H0 has been rejected, it means the factor levels are statically significant for the response-variable analyzed. Finally, to validate the ANOVA model, residual analyses were performed to check normality, homogeneity and independence [11]. A check of the normality and homogeneity assumptions can be made by the tests of Anderson-Darling and Bartlett, both to 5% of α. P-values higher than 5% validate normality and homogeneity assumptions. Regarding the independence test, a plot of residuals in the order in which data was collected can be used to detect any correlation between the residuals [11, 16]. 3. Results and discussion
In Table 2, the higher the Q’w, the lower the consumption of electrical energy for both VT2 and VSS wheels. On the other hand, regarding the specific consumption of electrical energy [KJ/s], the higher the Q’w, the higher the specific energy consumption. Therefore, the effect of environmental impacts of grinding should be verified when the reference flow is 1 s of machining versus one machined workpiece. 3.2. Results for 1 s of grinding machining Table 3 shows the mean results of ADP, AP, FAEP, HTP and GWP for 1 s of grinding. Such results were used in the ANOVA model and the p-values are given in Table 4. According to Table 3, for both VT2 (treatments 1, 2, 3 and 4) and VSS (treatments 5, 6, 7 and 8) grinding wheels, the higher the Q’w, the higher the impacts, which is in line with the inventory results discussed in Table 2 for the specific consumption of electrical energy. Table 3.Environmental impact results for each treatment – 1 s of grinding. AP
FAEP
HTP
GWP
[MJ]
[kg SO2eq.]
[kg DCBeq.]
[kg DCBeq.]
[kg CO2eq.]
1
8.75E-05
1.20E-07
2.53E-07
3.06E-06
5.50E-05
2
1.77E-04
2.43E-07
5.12E-07
6.18E-06
1.11E-04
3
2.61E-04
3.57E-07
7.55E-07
9.12E-06
1.64E-04
4
3.06E-04
6.49E-07
1.37E-06
1.66E-05
2.99E-04
5
1.22E-04
1.67E-07
3.53E-07
4.26E-06
7.66E-05
Treat ments
ADP
6
1.78E-04
2.44E-07
5.16E-07
6.23E-06
1.12E-04
7
2.18E-04
2.98E-07
6.30E-07
7.61E-06
1.37E-04
8
2.62E-04
3.59E-07
7.59E-07
9.17E-06
1.65E-04
Table 4.ANOVA results for each environmental impact category. Normality test Homogeneity Grinding Impact test ANOVA (Andersonwheel categories Darling) (Bartlett)
3.1. Overall inventory data VT2
Table 2 shows the average data of energy consumption as well as the processing time for each treatment. The processing time was used to calculate the environmental impacts per both one second of grinding machining and one workpiece. Table 2. Consumption of energy and processing time for each treatment. Electrical energy Processing time Treatments [KJ] [s] 1
1,158.54
4,474.84
2
1,171.68
2,237.42
3
1,152.13
1,491.61
4
1,011.11
1,118.71
5
1,613.72
4,474.84
6
1,180.78
2,237.42
7
961.36
1,491.61
8
868.50
1,118.71
VSS
ADP
0.315
0.102
0.055
AP
0.315
0.102
0.055
FAEP
0.315
0.102
0.055
HTP
0.315
0.102
0.055
GWP
0.315
0.102
0.055
ADP
0.061
0.339
0.075
AP
0.061
0.339
0.075
FAEP
0.061
0.339
0.075
HTP
0.061
0.339
0.075
GWP
0.061
0.339
0.075
Based on Table 3, the environmental hotspots were associated with the electricity supply chain due to consumption of electrical energy in the grinding process. Furthermore, to validate the ANOVA model, all the p-values of the tests of normality and homogeneity of residuals variance were higher than 5%, as shown in Table 4.
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In Fig. 2, plots regarding the ANOVA validation process for normality, homogeneity and independence can be seen for the GWP category and the VT2 grinding wheel. Equivalent plots were also obtained for all the other impact categories and the VSS grinding wheel. Probability Plot of GWP Normal
99,9
Mean StDev N AD P-Value
99 95 90
Percent
80 70 60 50 40 30 20
-4,96926E-21 0,000005393 60 0,420 0,315
After the ANOVA model had been validated, tests of hypothesis were performed and the results in Table 4 show pvalues higher than 5% for both the VT2 and VSS wheels. In summary, the Q`w factor was not statistically effective in changing the environmental impacts in the grinding tests with the use of both the grinding wheels. In this sense, a Tukey test was not performed because the factor levels of Q`w were not significantly different for 95% of confidence level. A full factorial design was made for the analysis of the effect of the type of grinding wheels on the overall results of the environmental impacts. The VT2 and VSS wheels were compared for the same Q’w values. The ANOVA and Tukey test results are provided in Tables 5 and 6, respectively. Table 5. ANOVA results for each environmental impact category – comparison of the VT2 and VSS wheels. ANOVA Impact Normality Homogeneity
10 5 1 0,1 -0,00002
-0,00001
0,00000
0,00001
0,00002
(a)
Residual Test for Equal Variances for GWP Bartlett's Test
50
Test Statistic P-Value
6,21 0,102
categories
test
test
Q’w
Type
Q’w x type
ADP
0.806
0.532
0.668
0.000
0.223
AP
0.063
0.068
0.070
0.030
0.083
FAEP
0.771
0.255
0.301
0.021
0.113
HTP
0.709
0.373
0.456
0.000
0.191
GWP
0.209
0.753
0.703
0.001
0.323
Qw
100
According to Table 5, the type of wheel is an important factor because the ANOVA results were lower than 5%. The Q’w factor and its interactions with the type of wheel were not statistically relevant, as all p-values were higher than 5%.
150
200 02 00 00 0,
06 00 00 0,
04 00 00 0,
08 00 00 0,
10 00 00 0,
12 00 00 0,
14 00 00 0,
(b)
95% Bonferroni Confidence Intervals for StDevs
categories
VT2
VSS
50
100
150
200
ADP
A
B
A
A
A
A
AP
A
B
A
A
A
A
0,000005
FAEP
A
B
A
A
A
A
0,000000
HTP
A
B
A
A
A
A
GWP
A
B
A
A
A
A
Versus Order
(response is GWP)
0,000010
Residual
Table 6. Tukey test results for each environmental impact category – comparison of the VT2 and VSS wheels. Type of wheel Q’w [mm³/mm.min] Impact
-0,000005 -0,000010 -0,000015 -0,000020 1
5
10
15
20
25
30
35
40
Observation Order
45
50
55
60
(c)
Fig. 2. (a) Normal probability plot, (b) homogeneity of confidence intervals, (c) independence of residuals versus observation order – illustrative example for the GWP impact category and VT2 grinding wheel.
Fig. 2 (a) shows a normal probability plot for the variance residuals of the GWP category calculated by the AndersonDarling (AD) test. Fig. 2 (b) shows that the homogeneity assumption was satisfied as the p-value was higher than 5% for the Bartlett’s test. Finally, Fig. 2 (c) shows a plot of residuals versus observation order for the GWP category and VT2 wheel, and no obvious grouping or bunching of residuals was found, validating the independence assumption.
The Tukey test showed the highest environmental impacts occurred for the VT2 wheel, which explains why this group was denoted as group A. The grinding process using the VSS wheel showed lowest environmental impacts (group B), while the Q’w factor showed equivalent impacts for all levels (group A). Therefore, for 1 s of grinding, the optimal process parameters were the use of VSS wheel with any Q’w value from 50 to 200 mm³/mm.min, because they were the factors that achieved the lowest levels of environmental impacts in the grinding experiments. 3.3. Results for one machined workpiece The same analysis and comparisons reported in section 3.2 are shown in section 3.3. However, here the results were all normalized for the 20,000 mm³ of removed volume of material as a reference flow. Table 7 shows the mean results of ADP, AP, FAEP, HTP and GWP for each treatment under
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study. The results of the ANOVA and Tukey test are provided in Tables 8 and 9, respectively.
Table 9. Tukey test results for each environmental impact category. Q’w [mm³/mm.min] Grinding Impact wheel
Table 7. Environmental impact results for each treatment – one machined workpiece. Treat ADP AP FAEP HTP GWP ments [MJ] [kg SO [kg DCB[kg DCB[kg CO -
VT2
categories
50
100
150
200
ADP
A
A
A
B
AP
A
A
A
B
FAEP
A
A
A
B
eq.]
eq.]
eq.]
eq.]
HTP
A
A
A
B
GWP
A
A
B
B
50
100
150
200
2
2
1
2.62E-02
3.58E-05
7.59E-05
9.16E-04
1.65E-02
2
2.64E-02
3.61E-05
7.64E-05
9.23E-04
1.66E-02
3
2.59E-02
3.54E-05
7.50E-05
9.05E-04
1.63E-02
ADP
A
B
C
D
4
2.28E-02
3.08E-05
6.52E-05
7.87E-04
1.42E-02
AP
A
B
C
D
5
3.64E-02
4.98E-05
1.05E-04
1.27E-03
2.29E-02
FAEP
A
B
C
D
6
2.66E-02
3.64E-05
7.70E-05
9.30E-04
1.67E-02
HTP
A
B
C
D
7
2.17E-02
2.96E-05
6.27E-05
7.57E-04
1.36E-02
GWP
A
B
C
D
8
1.95E-02
2.67E-05
5.65E-05
6.82E-04
1.23E-02
Table 7 shows the higher the Q’w, the lower the environmental impacts for both VT2 (treatments 1, 2, 3, and 4) and VSS wheels. It was a controversial observation in comparison to the results in section 3.2, Table 3, where the highest impacts occurred for the highest value of Q’w. Therefore, when the reference flow time of processing versus volume of material removed is changed, all the environmental impacts can significantly change. Table 8. ANOVA results for each environmental impact category. Grinding Impact Homogeneity Normality test ANOVA wheel categories test
VT2
ADP
0.278
0.303
0.000
AP
0.532
0.551
0.038
FAEP
0.532
0.551
0.038
HTP
0.529
0.532
0.000
GWP
0.501
0.500
0.030
ADP VSS
AP FAEP
0.660 0.660 0.293
0.075 0.075 0.084
VSS
Tables 10 and 11 show the statistical results regarding the effect of the type of grinding wheel on the impact potentials. Table 10. ANOVA results for each environmental impact category – comparison of the VT2 and VSS wheels. ANOVA Impact Homogeneity Normality test categories test Q’w Type Q’w x type ADP
0.200
0.058
0.000
0.030
0.000
AP
0.242
0.058
0.000
0.030
0.000
FAEP
0.242
0.058
0.000
0.030
0.000
HTP
0.242
0.058
0.000
0.030
0.000
GWP
0.242
0.058
0.000
0.030
0.000
Table 11. Tukey test results for each environmental impact category – comparison of the VT2 and VSS wheels. Type of wheel Q’w [mm³/mm.min] Impact categories
VT2
VSS
50
100
150
200
ADP
A
B
A
B
C
D
0.000
AP
A
B
A
B
C
D
0.000
FAEP
A
B
A
B
C
D
0.000
HTP
A
B
A
B
C
D
GWP
A
B
A
B
C
D
HTP
0.660
0.075
0.000
GWP
0.293
0.084
0.000
From results in Table 8, the ANOVA model was validated and the effect of Q’w factor on the environmental impact results was statistically significant for both VT2 and VSS wheels, because all the p-values were lower than 5%. These results have contradicted those in section 3.2, Table 4, where the Q’w factor was not relevant. According to the Tukey test in Table 9, for the VT2 wheel, the lowest impacts occurred for the highest Q’w value (200 mm³/mm.min), and the impacts for Q’w from 50 to 150 mm³/mm.min were higher and statistically equivalent (group A). For the VSS wheel, the lowest impacts were also observed for grinding with Q’w = 200 mm³/mm.min.
The type of wheel was relevant for all impact categories, because its p-values were lower than 5%, which has confirmed the findings reported in section 3.2, i.e., type of grinding wheel can directly effects the environmental impacts, and the Tukey results (Table 11) showed the VSS wheel caused the lowest environmental impacts (group B). Furthermore, the lowest impacts occurred for the highest Q’w value. The Q’w values were relevant for the environmental impacts studied, while in section 3.2, the same Q’w values were statically not representative. The interaction of Q’w and the type of wheel was representative for all impact categories according to the results in Table 10. Therefore, Fig. 3 shows an interaction plot of GWP results versus Q’w and type of grinding wheel. It is important to highlight that similar curves were also obtained for all the other impact categories in this study.
Diogo A. L. Silva et al. / Procedia CIRP 29 (2015) 508 – 513
513
Acknowledgements
Interaction Plot for GWP [kg CO2-Equiv.] Data Means
0,024
Grinding wheel
VT2 VSS
Mean results of GWP
0,022
Financial support provided by FAPESP (São Paulo Research Foundation) through Grant no. 2013/06736-9. References
0,020 0,018 0,016 0,014 0,012 50
100
150
200
Q'w
Fig. 3. Interaction plot for GWP versus Q’w and type of grinding wheel.
According to Fig. 3, there is an intersection point of results when grinding with Q’w = 100 mm³/mm.min. At this intersection point, the GWP impacts of both VT2 and VSS wheels were equivalent, and for higher values of Q’w, lower GWP impacts were specially found for the VSS wheel. Thus, based on the statistical results, the optimal process parameters for the reference flow of one machined workpiece, were the use of the VSS grinding wheel with Q’w = 200 mm³/mm.min. 4. Conclusions The process of choosing an LCA’s reference flow is very important for the development of SM and GM strategies. The results showed for 1 s of grinding machining as a reference flow, the optimal process parameters were the use of VSS wheel with any Q’w value from 50 to 200 mm³/mm.min. On the other hand, when the reference flow was one machined workpiece, the optimal set of parameters was the use of the VSS wheel with Q’w value of 200 mm³/mm.min. Moreover, the interaction of Q’w and type of wheel was also an important factor when assuming one machined workpiece as reference flow. The authors agree that the reference flow of one workpiece was the best choice because it enabled a more indepth evaluation of the statistical effects of changing grinding parameters on the overall environmental impacts. For example, to calculate the environmental impacts for 1 s of grinding, the coefficients of variation of results were almost constant and lower than 5%, which indicate that changes in the grinding parameters exerted a low influence on results. However, when the grinding process was studied for one machined workpiece, higher coefficients of variation of the results were obtained. Thus, to support SM and GM strategies and a better controlling and monitoring of the grinding process studied, it is recommended to use number of machined workpieces as an LCA reference flow instead of time of processing. More studies should be conducted for the evaluation of other grinding parameters (e.g. cutting fluid flow, cutting velocity), while also including this DOE and LCA approach to study other manufacturing processes.
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