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Highly reliable and accurate melt temperature measurements in the barrel are necessary for stable injection molding. Conventional sheath-type thermocouples are insufficiently responsive for measuring melt temperatures during molding. Herein, machine learning models were built to predict the melt temperature after plasticizing. To supply reliably labeled melt temperatures to the models, an optimized temperature sensor was developed. Based on measured high-quality temperature data, three machine learning models were built. The first model accepted process setting parameters as inputs and was built for comparisons with previous models. The second model accepted additional measured process parameters related to material energy flow during plasticizing. Finally, the third model included the specific heat and part weights reflecting the material energy, in addition to the features of the second model. Thus, the third model outperformed the others, and its loss decreased by more than 70%. Meanwhile, the coefficient of determination increased by about 0.5 more than those of the first model. To reduce the dataset size for new materials, a transfer learning model was built using the third model, which showed a high prediction performance and reliability with a smaller dataset. Additionally, the reliability of the input features to the machine learning models were evaluated by shapley additive explanations (SHAP) analysis.

Elastic and dynamic characteristics of jumping-skis affect ski performance at all four ski jumping phases. Compared with the number of studies on alpine-skis, there have been few studies on the characteristics of jumping-skis. This article identifies design parameters that have the most influence on jumping-ski characteristics. To identify the elastic and dynamic characteristics of jumping-skis, previous research and testing methods for alpine-skis were modified. Spring constants and bending stiffness distributions for three jumping-skis were measured. Natural frequencies, modal shapes, and damping ratios were also measured. From these results, the bending stiffness distribution was identified as a more elastic characteristic of jumping-skis than the spring constant. The natural frequency and damping ratio were selected as the relevant dynamic characteristics. To determine the effective design parameter for elastic and dynamic characteristics of jumping-skis, a jumping-ski was modeled by finite element method, and the inner structures and material properties of components of a jumping-ski were measured and analyzed. By comparing the simulation with actual test results, the reliability of the finite element method simulation was verified. The geometrical feature of the ski thickness profile is the most significant design parameter for elastic characteristics of jumping-skis. A mechanical property of the face material and wood at the sidewall is a highly influencing design parameter for dynamic characteristics of jumping-skis.

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