This paper describes an experimental study conducted to identify the common engine part load conditions between Malaysian city driving and NEDC (New European Driving Cycle) test on a 4 cylinder gasoline fuelled engine, with multi-point fuel injection system, and continuous variable transmission vehicle. This is to pinpoint a regional area from the part load map in the attempt to strategize key technologies such as CDA (Cylinder Deactivation) or CNG (Compressed Natural Gas). Technologies such as CDA or CNG do not operate at all engine operations. Due to certain drawbacks, the operation of the technologies must be strategized to obtain most benefit from the engine. With the knowledge of the common part load region, these technologies could be integrated and strategized into the region to reduce overall fuel consumption. With improvements in fuel consumption respective to the identified common part load operations, the overall fuel consumption benefit does not only serve the legislation but also most importantly benefit the local consumers who travel on Malaysian roads. Copyright © 2009 Praise Worthy Prize S.r.l. - All rights reserved

Abas, M. A.

Mat Arishad, N. I.

Muhamad Said, M. F.

Zahari, I.

Zainal Abidin, S. F.

UTHM Institutional Repository

GREEN DENSITY OPTIMISATION WITH SUSTAINABLE SEWAGE FAT AS BINDER COMPONENTS IN SS316L FEEDSTOCK OF METAL INJECTION MOULDING PROCESS (MIM)  BY TAGUCHI METHOD A.M Amin1a, M.H.I Ibrahim1b, R.  Asmawi1c, N. Mustapha1d, M.Y Hashim1e 1Faculty of Mechanical and Munufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor. aazriszul@uthm.edu.my, bmdhalim@uthm.edu.my, croslias@uthm.edu.my, dnj.wawa@gmail.com, eyusni@uthm.edu.my Keyword: Sewage Fat or Fat Oil Grease , Metal injection moulding, Taguchi method, Design of Experiment (DOE) Abstract: Metal injection moulding has gain much attention due to flexibility and high productivity of the plastics injection moulding with the powder metallurgy method of sintering. In order to gain better shape retention, optimum density of green part is required. This paper deals with the application of Taguchi optimisation technique on getting the optimum density for Metal Injection Moulding (MIM) components base on certain parameters in process injection.  For this purposes only 3 process parameters were considered here including its interactions which are injection pressure, injection temperature and mould temperature. Since its more close to the final products these parameters were selected and other parameters will be kept constant. An orthogonal array of L16 experimental base design was conducted. Confirmation test will be done base on Signal-to-Noise (S/N) ratio and it Means.  1.0 INTRODUCTION  Sustainability development must not compromises the standards and health of future generations [1]. Efficiency of used natural resources with reduction of waste and emission through its usage are required. Existing industrial technologies in terms of materials, equipments and processes has help to create the high standard of living which much appreciated in world today is no longer appropriated (Evans et al., 2008 cited in [2]). As living of standards continuously rises, demand of products will also continue to increase which will affect manufacturing area whilst using less material, energy and generating less waste.  Metal injection moulding is a manufacturing process with an advantage of producing complex and intricate parts in high volume production with a few shot as compare to other fabrication process[3–5]. After being injected, the parts will undergo debinding process and finally sintering process.  Before any attempts for feedstock to undergo injection moulding process, determination of optimal binder ratio need to be analysed base on rheological characteristic. For sewage fat as the components of binder in MIM, it is found that that the ratio between Polypropylene and sewage fat should be 60:40[6]. This binder ratio will be used with 60% powder loading of stainless steel 316L to form a feedstock for injection moulding processes for better part accuracy, surface finish and strength. In order to achieve the quality needed, a proper adjustment of process parameters for optimum conditions need to be corrected before the green part undergo other processes.   The traditional approach to experimental work is to vary one factor at a time, holding all other factors fixed. This method does not produce satisfactory results in a wide range of experimental settings and when multiple performance characteristics with conflicting goals are considered, the approach becomes unsuitable [7]. Nowadays, optimization of the process parameter are gaining much interests among researchers as it can minimize defects, cost and obtain high efficiency in the planning or experiments. Numerous researchers like Zu and Lin [8] in their work of debinding of injection moulded for optimum mechanical properties have shown that principal factors that affecting it, is solvent debinding temperature and thermal debinding atmosphere. They used Taguchi Method in implemented the Design of Experiments (DOE) for finding the optimum process variables. Ibrahim et. al [7] use Taguchi method for optimizing the green density of injection moulded sample using variety factors like injection pressure, injection temperature, mold temperature, injection time and holding time. Interactions between the factors also being analysed for optimum green strength of the injection moulded sample. They founds that mold temperature, injection time and pressure are the significant factors that affect the green strength of the green sample.        Therefore, Taguchi’s parameter design, being a simple and inexpensive method, is adopted for in-depth study to understand process parameters and their interaction effects on responses like accuracy of dimensions and reducing the defects [9] with minimum experimental runs. Conventional Taguchi method can effectively establish optimal parameter settings for single performance characteristic[10]. Hence, Taguchi method (L16) is used in this work to generate a single response which is green density of injection moulded sample which is important before undergo debinding process for optimum density. 2.0 EXPERIMENTAL    2.1 Materials  The properties of stainless steel powder with 60% powder loading used are shown in Table 1. Binder formulation used in this optimising process parameters is 60% to 40% between the polypropylene and sewage fat. This formulation is the optimum one since it has the highest mouldability index [6]. The properties of the binder constituents are shown in Table 2.  Table 1: Properties of SS316L powder Identification SS316L, PF-10F Powder source Epson Atmix Corp Tap density, g/cm3 4.06 True pynometer density, g/cm3 8.0471 Powder size d10=2.87µm d50=5.96 µm d90=10.65 µm  Table 2: Three different binder weight percentage with the same 62% powder loading Binder constituents Density (g/cm3) Tm (oC) Polypropylene, PP  0.9 165 Sewage fat  0.90156 50    Injection moulding was carried out on a Nissei NP 7 Real Mini machine. Fig. 1 shows the MIM sample injected base on two level factors which are injection temperature, pressure and mould temperature with others factors are kept constant. Density of the injected parts being measured using pycnometer density.    Figure 1: Sample injected  2.2 Design of Experiments   Taguchi (L16) design of experiments is used in order to improve the green density of the injected samples. Nominal is best type of control function being used in calculating the S/N ratio. Two level designs of experiments with 3 factors was considered in the injection moulding process with others factor are kept constant. The factors that are kept constant are shown in Table 3. The parameters that made varies are injection temperature, pressure and mould temperature as shown in Table 4. Total degree of freedom (DOF) for single and interactions parameters is 7 and L16's Taguchi orthogonal array seems suitable for this analysis with 16 experiments will be conducted (Table 5).                                                                     (1)  Table 3: factors and Levels of Injection Process Factor Level 1 Level 2 Injection Temperature (oC) 180 200 Injection Pressure (MPa) 64 97 Mould temperature (oC) 60 80       The column assignment and experimental layout are shown in table 5 with each experiment was conducted with two replications. After completion the all experiments and analysis of the data, the recommendation setting will be tested.  Table 4: Experimental layout of Taguchi (L16) Factors Density (g/cm3) Injection Temperature    (oC) Injection Pressure (MPa) Mould Temperature (oC) R1 R2 180 64 60 5.0613 5.0259 180 64 60 5.0867 5.0977 180 64 80 5.0791 5.0349 180 64 80 4.9193 4.9722 200 97 60 4.9871 5.0154 200 97 60 5.0186 5.0305 200 97 80 4.473 4.2613 200 97 80 4.1486 4.04 180 97 60 5.0332 4.9935 180 97 60 5.0225 5.0395 180 97 80 5.0341 4.9016 180 97 80 5.0053 5.0099 200 64 60 5.0454 4.9821 200 64 60 5.0128 5.0118 200 64 80 5.0944 5.0625 200 64 80 5.0673 5.0602  Table 5: Experimental layout of Taguchi (L16) Factors Mean (ӯ)  Std Dev (sN-1) ӯ 2 (sN-1)2 S/N ratio Injection Temperature    (oC) Injection Pressure (%) Mould Temperature (oC) 180 64 60 5.0436 0.01251579 25.44 0.000157 52.11 180 64 60 5.0922 0.003889087 25.93 0.000015 62.34 180 64 80 5.057 0.01562706 25.57 0.000244 50.20 180 64 80 4.94575 0.018702974 24.46 0.000350 48.45 200 97 60 5.00125 0.010005561 25.01 0.000100 53.98 200 97 60 5.02455 0.004207285 25.25 0.000018 61.54 200 97 80 4.36715 0.074847253 19.07 0.005602 35.32 200 97 80 4.0943 0.038395898 16.76 0.001474 40.56 180 97 60 5.01335 0.01403607 25.13 0.000197 51.06 180 97 60 5.031 0.006010408 25.31 0.000036 58.46 180 97 80 4.96785 0.046845824 24.68 0.002195 40.51 180 97 80 5.0076 0.001626346 25.08 0.000003 69.77 200 64 60 5.01375 0.02237993 25.14 0.000501 47.01 200 64 60 5.0123 0.000353553 25.12 0.000000 83.03 200 64 80 5.07845 0.011278353 25.79 0.000127 53.07 200 64 80 5.06375 0.002510229 25.64 0.000006 66.10 Total 4.925866  24.26  54.59   (a)                                                                                           (b) Figure 2: Response graph of (a) S/N ratio (b) Mean ratio  5.04.84.6806060405.04.84.62001805.04.84.6Injection T empInjection PressMold T emp180200TempInjection4060PressInjection6080TempMoldInteraction Plot for MeansData Means      45403580606040454035200180454035Injection T empInjection PressMold T emp180200TempInjection4060PressInjection6080TempMoldInteraction Plot for SN ratiosData MeansSignal-to-noise: Nominal is best (10*Log10(Ybar**2/s**2))   (a)                                                                                               (b) Figure 3: Factor interaction graph for (a) S/N ratios (b) Mean ratios  3.0 RESULTS AND DISCUSSION 3.1 Interpretation of response table and graph  Table 6: Response table S/N variability analysis  A B C Level 1 54.11 57.79 58.69 Level 2 55.07 51.40 50.50 Delta 0.96 6.39 8.19   45 50 55 60 A1 A2 B1 B2 C1 C2 S/N ratio 4.6 4.8 5 5.2 A1 A2 B1 B2 C1 C2 Mean Table 7: Response table Mean analysis  A B C Level 1 5.02 5.04 5.03 Level 2 4.83 4.81 4.82 Delta 0.19 0.22 0.21   Response table 7 and 8 was created by calculating the levels average for each factor level. Sample of calculation shown is the average S/N ratio and Mean ratio for injection temperature.                                       (2)                         (3)  The largest the delta or differences between the level contribute to the more significant of the factor to the density of the injected part. It can be seen in Fig. 7 that the largest difference of S/N ratio is C which is mould temperature factor following the Mould and pressure and injection temperature accordingly.  Interaction between the factors also being analysed shown in Fig. 8 where it seem that factor Injection temperature and pressure have a significant interaction for both S/N ratio and Means where else between injection and mould temperature insignificant interactions happen where the line are almost parallel which indicates that mould temperature produces higher S/N ratio compare to  injection temperature. Significant effect of mould temperature on pressure are noticed from the intercepted line of the S/N graph which indicated that this factor cannot be ignored in the process.    3.2 Optimise and Predict  Form Fig. 2, the control factor levels that can maximise the S/N ratio of the process is A2, B1 and C1 which are 200oC, 40% and 60 oC. Evaluated on the S/N ratio graph is not enough since Mean adjusting factor also need to adjusted for achieving the target value. Although the injection temperature has very little influence on S/N ratio (see Fig. 2a), but it has the biggest impact on changing the mean production rate. Changing the temperature level might not significantly change the S/N ratio of the process. Therefore the optimise level are changing to 180 oC, 40% and 60 oC and predicting S/N ratio value is:    (4)                                                                                               (5) 4.0 SUMMARY  This paper summarizes experimental investigations on optimising the green density of injected mould of stainless steel powder compounded with sewage fat and polypropylene. Taguchi method has been used in optimising the process parameter and been found that the best injection temperature is 180 oC, injection pressure of 40% and mould temperature of 60 oC 5.0 REFERENCES [1] G. H. Brandtland, “Unite Nation Report of the World Commission on Environment and Development (Our Common Future),” 1987. [2] L. Smith and P. Ball, “Steps towards sustainable manufacturing through modelling material, energy and waste flows,” International Journal of Production Economics, vol. 140, no. 1, pp. 227–238, Nov. 2012. [3] H. Abolhasani and N. Muhamad, “A new starch-based binder for metal injection molding,” Journal of Materials Processing Technology, vol. 210, no. 6–7, pp. 961–968, Apr. 2010. [4] M. Afian, I. Subuki, and N. Abdullah, “The Influence Of Palm Stearin Content On The Rheological Behaviour Of 316L Stainless Steel Mim Compact,” Journal of Science and Technology, pp. 1–14, 2012. [5] S. Ahn, S. Jin, S. Lee, S. V Atre, and R. M. German, “Effect of powders and binders on material properties and molding parameters in iron and stainless steel powder injection molding process,” Powder Technology, vol. 193, no. 2, pp. 162–169, 2009. [6] A. M. Amin, M. H. I. Ibrahim, and R. Asmawi, “Mixing Homogeneity and Rheological Characterization for Optimal Binder Formulation for Metal Injection Moulding,” Applied Mechanics and Materials, vol. 607, pp. 181–184, Jul. 2014. [7] M. H. . M. H. I. Ibrahim, N. Muhamad, A. A. . B. Sulong, K. . K. R. Jamaludin, N. H. . N. H. M. Nor, and S. Ahmad, “Single Performance Optimization of Micro Metal Injection Molding for the Highest Green Strength by Using Taguchi Method,” Journal of Advanced Materials Research, vol. c, no. C, pp. 129–134, 2011. [8] Y. . Zu and S. . Lin, “Optimizing the mechanical properties of injection molded W-4 . 9 % Ni-2 . l % Fe in debinding,” Journal of Materials Processing Tech., vol. 71, pp. 337–342, 1997. [9] N. H. Loh and R. M. German, “Statistical analysis of shrinkage variation for powder injection molding,” Journal of Materials Processing Technology, vol. 59, no. 3, pp. 278–284, May 1996. [10] A. K. Sood, R. K. Ohdar, and S. S. Mahapatra, “Improving dimensional accuracy of Fused Deposition Modelling processed part using grey Taguchi method,” Materials & Design, vol. 30, no. 10, pp. 4243–4252, Dec. 2009.    

Experimental study to identify common engine part load conditions between Malaysian city driving and NEDC test

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