Fig. 1. Step casting geometry and location of samples are pictured. 1 Background
Te current technological method to design crankcases is to use computer-aided engineering methods, which
assume homogenous behavior. To integrate process simulation into computer-aided lifetime predic- tion, a quantitative correlation between the process dependent microstructures and the local durability must be determined and included in the process simulation tool. At the same time, lifetime prediction pro- grams need to be enabled to consider local durability values. Te researchers’ first approach was based on durability-rele- vant microstructure parameters for CGI, such as graphite shape, size and pearlite/ferrite ratio, for the Audi 3.01 V56 crankcase. Te mechanical proper- ties and microstructures of cast iron are, to a high degree, driven by the metallurgy. To predict the microstructure, the following physical and chemi- cal parameters needed to be considered: • Inoculation of the graph- ite under consideration of the potency of the inoculants and the phase equilibrium
• Mode, distribution and morphology of the graphite
• Austenitic growth • Segregation behavior of
Fig. 2. Image analysis was used to evaluate nodularity.
the alloying elements
• Solid phase transition austenite • Ferrite/pearlite dependency on segregation conditions and diffusion distances
• Te moving condensation zone in the mold sand.
While the solidification phe-
nomena already are considered in CGI simulation, the team needed to develop a model for the ferrite/ pearlite distribution. The creation of ferrite and pearlite is driven by the graphite distribution estab- lished during the solidification and eutectoid transformation. CGI solidification leads to two kinds of eutectic cells—ductile iron cells and CGI cells. Both grow accord- ing to unique mechanisms. Besides the chemical composition and in- oculation, the cooling determines whether more sphaerolites (ductile iron cells) or compacted graphite will be present at the end. Researchers aimed to recreate
typical microstructure distributions in tensile bars to find a representa- tive correlation between microstruc- ture and fatigue values. Additional step castings also were poured. Te geometry of the step castings and the locations of the fatigue test samples are shown in Fig- ure 1. Te melts poured were conditioned with two specific magnesium treatments. Te resulting nodularity values represent typical nodularity distributions found in critical areas of engine blocks. Te various wall thicknesses of the step casting provided an addi- tional variation of the ferrite/ pearlite ratio.
Te fatigue experiments
included tension-compression as well as alternating bending loads. Because lifetime predic- tion analysis tools use tension and elongation concepts to calculate fatigue values, the researchers conducted tension and elongation-regulated experiments. Te fracture surfaces were
evaluated after the failure of the fatigue sample, and the local microstructure was deter- mined using automatic image analysis (Fig. 2). Te research- ers analyzed the number, shape and size of graphite particles, ferrite/pearlite ratio and chemical compositions.
Jul/Aug 2013 | METAL CASTING DESIGN & PURCHASING | 43
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60