EPDM橡胶密封条耐磨性能预测数据

橡胶耐磨性是决定橡胶制品寿命的关键因素，但是橡胶的耐磨性机理复杂、影响因素多，不易预测和调控，而橡胶的力学性能机理较明确，容易测得。因此，需要建立橡胶材料的耐磨性能和力学性能之间的关系模型，以达到利用力学性能预测和调控橡胶耐磨性能的目的。本数据是采用多元线性回归法建立了复合橡胶材料的耐磨性能和力学性能之间的模型关系，从而达到预测复合橡胶材料耐磨性能的目的。
橡胶制品（密封条、密封圈、油封、减震带、隔水带、改性橡胶材料等领域）研发、生产企业可以根据本申请的数据组作为参照，根据企业自己测得的自变量同数据组中的自变量数据进行比对，进而得知对应的耐磨性能，进一步快速知晓材料的耐磨性能。选用阿克隆磨耗量作为因变量y来表征复合橡胶材料的耐磨性能，阿克隆磨耗量值越大，即证明复合橡胶材料耐磨性越差；阿克隆磨耗量值越小，即证明复合橡胶材料耐磨性越好。选用邵氏A硬度x1，100%定伸x2，300%定伸X3，300%定伸/100%定伸x4，拉伸强度x5，扯断伸长率x6，撕裂强度x7，永久变形x8作为自变量，利用 MATLAB 语言regress 语句拟合样本数据，得到回归模型系数，建立了复合橡胶材料的耐磨性能和邵氏A硬度，100%定伸，300%定伸，300%定伸/100%定伸，拉伸强度，扯断伸长率，撕裂强度和永久变形的多元线性回归模型，用于预测复合橡胶材料的耐磨性能。

Rubber wear resistance is a critical factor determining the service life of rubber products. However, the wear resistance mechanism of rubber is complex with multiple influencing factors, posing challenges for prediction and regulation. By contrast, the mechanical performance mechanisms of rubber are relatively well-defined and easily measurable. Thus, it is necessary to establish a relational model between the wear resistance and mechanical properties of rubber materials, to achieve the goal of predicting and regulating rubber wear resistance using their mechanical performance.

This dataset constructs a model linking the wear resistance and mechanical properties of composite rubber materials via multiple linear regression, with the aim of predicting the wear resistance of composite rubber materials.

Enterprises engaged in the R&D and production of rubber products (including but not limited to sealing strips, sealing rings, oil seals, shock-absorbing belts, water-blocking belts, and modified rubber materials) can take the dataset provided herein as a reference. By comparing the self-measured independent variables with those in the dataset, enterprises can obtain the corresponding wear resistance, thereby quickly acquiring the wear resistance level of their materials.

The Akron abrasion loss was selected as the dependent variable y to characterize the wear resistance of composite rubber materials: a larger Akron abrasion loss indicates poorer wear resistance of the composite rubber material, while a smaller value indicates better wear resistance. The independent variables selected are: Shore A hardness (x1), 100% modulus (x2), 300% modulus (x3), 300% modulus / 100% modulus (x4), tensile strength (x5), elongation at break (x6), tear strength (x7), and permanent deformation (x8). The regress statement in MATLAB was utilized to fit the sample data and derive the regression model coefficients, thereby establishing a multiple linear regression model between the wear resistance of composite rubber materials and the aforementioned mechanical performance indicators for predicting the wear resistance of composite rubber materials.