Injection, delayed

The instantaneous equivalence ratio will follow the curve drawn in B. The drop of 40 % which occurs in the first second is well known (air delay is smaller than injection delay). The very wide oscillation occuring between 6 and 9 s deserves some comments. At the begiiming of the tlirottle opening rich mixture is measured by tlie lambda sensor. Naturally (C) the injection duration is adjusted. But even with a smaller injection duration the film continues to fill the cylinder (D). Then the C.P.U. interprets such behavior by continually reducing tlie injection duration. Just before 7 seconds, the injection has been halted and the equivalence ratio remain rich This lasts until the the liquid film disappears (D). 

Injection delay Injection begins 10 s after imaging begins... 

Injection delay Saline injection begins immediately after contrast injection... 

This is mainly a cold process, although the mold can also be preheated to speed up the proceedings. Pressure exerted by a liquid resin is uniformly spread out on the sheet to be produced, allowing for an even thickness and excellent product quality. Usually, the pressures exerted for injection range between 0.5 and 4 bar, and the injection delays range between 20 s and 2 min. 

In control rats total (72 and 83 %) and direct (52 and 63 %) recoveries, respectively after early and late proximal injections, significantly increased along the proximal tubule and were different from recoveries (94 and 69 %) following distal injections. Delayed recovery approximated 20 to 25 % irrespective of the site of puncture. 

Times for each of the cycle portions in Fig. 9-9 are relative. Optimum drying cycle time for the injection cycle is about 1.5 s. This drying cycle time includes opening of the molds, movement of the mold unit, and close time. Basically, plasticated polymer (after an injection delay) is injected into the mold (injection), where it is brought to holding pressure and conditioned. After the injection... 

Water-assisted injection molding experiments were carried out on newly developed equipment in our lab. The effects of four processing parameters, short-shot size, melt temperature, water injection delay time, and water pressure, on the water penetration length and residual wall thickness of water-assisted injeetion molded polypropylene curved pipe were investigated. The crystallization behavior difference between the beginning and the end of the water channel of the eurved pipe was analyzed using differential scanning calorimetry. 

The effects of four processing parameters on the residual wall thickness in molded parts are illustrated by the thicknesses measured at five positions (PI, P3, P5, P7, and P9) in Figure 4. On the whole, the residual wall thickness at position P5 is smaller than those at positions PI and P3. Furthermore, the thickness obviously decreases from positions P7 to P9. Position P9, that is, the position near the end of the water channel, exhibits the minimum wall thickness. This can be explained as follows. At 68.5% short-shot size used in this work, the injected polymer melt can only reach near position P7 in the mold cavity. The cavity downstream is filled with the melt pushed by the water. The time for the pushed melt to contact the cavity is short due to high filling rate of water. Moreover, the cavity warms up because of the contact with the hot melt during the water injection delay. So the water easily pushes the melt against the mold cavity. 

At position P9, the samples taken from the outer layer, middle, and inner layer show similar melting peak and crystallinity. This may be attributed to the fact that the mold cavity corresponding to this position is filled by melt pushed under the water. The mold cavity warms up because of the contact with the hot melt during the water injection delay (8 s). Moreover, the water also warms up when reaching position P9 because of the heat absorbed from hot melt during its penetration. So the cooling rate difference across the wall is not so obvious as that at position PI. 

Figure 3. Effects of (a) short-shot size, (b) melt temperature, (c) water injection delay time, and (d) water pressure on water penetration length.

In this work, an inverse nemal network model was proposed. That is, measured residual wall thicknesses at five different positions of molded parts as shown in Figure 2 are the input variables. The output variables consist of fom processing parameters, including melt tenperature, water injection pressure, water injection delay time, and short-shot size. Among 31 data sets obtained from experiments by changing processing parameters, 26 sets of data (data 1 26 shown in Table 1) were used as training patterns to train the LMBP neural network to ascertain its... 

Once trained, the neural network model has been identified and can be utilized to forecast the processing paramrters expected for new levels of residual wall thicknesses at five positions of molded part. For exarrtple, inputting thicknesses 3.75, 3.72, 3.48, 3.65, and 3.00 mm at positions PI to P5 to the model, it can predict the processing parameters as follows melt temperature 232.3°C, water injection pressure 8.6 MPa, water injection delay time 3.4 s, and short-shot size 88.1%. While the actual measrrrements are melt temperature 230°C, wato-injection pressme 9 MPa, water injection delay time 3 s, and short-shot size 85%. 

Water-assisted injection molding experiments were carried out on newly developed equipment in our lab to investigate the influence of melt temperature, water injeetion pressure, water injection delay time, and short-... 

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