✨ 长期致力于曲面造型、人性化设计、曲面重构、曲面形态分类、曲面形态识别、耳甲腔、入耳式耳机研究工作擅长数据搜集与处理、建模仿真、程序编写、仿真设计。✅ 专业定制毕设、代码✅如需沟通交流点击《获取方式》1耳甲腔关键特征点自动提取与NURBS曲面重构采集315名青年外耳三维模型定义11个耳甲腔关键特征点(耳道口、耳屏间切迹、对耳轮下脚等)。提出基于NURBS曲面曲率极值的自动定位算法计算每个顶点的主曲率设定曲率阈值0.3mm^-1识别凸起和凹陷区域。通过区域生长和形态学操作得到特征点坐标。采用双向一阶轮廓线重构法沿U方向和V方向分别提取等参数线取交点作为曲面型值点。每个样本获得统一的21x21网格型值点。使用NURBS曲面插值次数为3节点向量采用哈特利-贾德法确定。重构曲面的平均误差0.07mm连续性达到G2。2改进层级聚类与耳甲腔形态模型库构建针对传统层级聚类对初始类数敏感的问题引入组内平均轮廓系数和组间距离比作为最佳聚类数评判准则。将315个样本的曲面的型值点展平为441维向量进行主成分降维至20维。采用Ward连接法聚类计算不同类数下的综合指标确定最佳类数为29。构建各聚类组的共性特征曲面取组内所有样本型值点的中位数作为代表曲面。组内误差平均0.12mm组间最小距离0.34mm分离性良好。建立的中国青年人耳甲腔形态模型库覆盖了18-28岁人群的95%以上形态变异。3基于概率神经网络的耳甲腔形态识别与定制设计提取待测个体的耳甲腔三维扫描数据重构曲面并降维至20维特征向量。构建概率神经网络分类器输入层20节点模式层29个节点(对应29类)求和层29个节点输出层决策。平滑因子σ通过交叉验证优化最优值0.15。在测试集(63个样本)上识别准确率93.7%优于KNN的85.6%。将识别结果映射到模型库中的对应共性曲面进行耳机外壳设计。使用ABAQUS有限元分析外耳-耳机接触应力最大接触应力控制在0.2MPa以下佩戴舒适性主观评分4.6/5。3D打印实物后抗滑落测试通过率100%。import numpy as np from scipy.spatial import ConvexHull from scipy.interpolate import RBFInterpolator from geomdl import NURBS from geomdl import utilities from geomdl.visualization import VisMPL class FeaturePointExtractor: def __init__(self, curvature_thresh0.3): self.thresh curvature_thresh def compute_curvature(self, vertices, faces): # simplified: using local covariance curvatures [] for v in vertices: neighbors self.find_neighbors(v, vertices, radius5) if len(neighbors) 5: curvatures.append(0) else: cov np.cov(neighbors.T) eigvals np.linalg.eigvalsh(cov) curv eigvals[1] / (eigvals[0]1e-8) curvatures.append(curv) return np.array(curvatures) def find_neighbors(self, pt, vertices, radius): dists np.linalg.norm(vertices - pt, axis1) return vertices[dists radius] def localize_feature_points(self, curv_map): # region growing seeds np.where(curv_map self.thresh)[0] regions [] return regions class BiDirectionalReconstruction: def __init__(self, n_u21, n_v21): self.nu n_u self.nv n_v def extract_isoparametric_curves(self, points_3d, u_param, v_param): # points_3d: (N,3), u_param, v_param: parametric coordinates from scipy.interpolate import griddata u_grid np.linspace(0,1,self.nu) v_grid np.linspace(0,1,self.nv) X griddata((u_param, v_param), points_3d[:,0], (u_grid[None,:], v_grid[:,None]), methodcubic) Y griddata((u_param, v_param), points_3d[:,1], (u_grid[None,:], v_grid[:,None]), methodcubic) Z griddata((u_param, v_param), points_3d[:,2], (u_grid[None,:], v_grid[:,None]), methodcubic) return np.stack([X,Y,Z], axis2) def build_nurbs(self, control_grid, degree3): surf NURBS.Surface() surf.degree_u degree surf.degree_v degree surf.ctrlpts2d control_grid.reshape(-1,3).tolist() surf.knotvector_u utilities.generate_knot_vector(degree, control_grid.shape[0]) surf.knotvector_v utilities.generate_knot_vector(degree, control_grid.shape[1]) return surf class ImprovedHierarchicalClustering: def __init__(self, n_components20): self.n_comp n_components self.labels None def reduce_dim(self, X): from sklearn.decomposition import PCA pca PCA(n_componentsself.n_comp) return pca.fit_transform(X) def silhouette_and_separation(self, X, labels): from sklearn.metrics import silhouette_score sil silhouette_score(X, labels) # between-cluster min distance centers np.array([X[labelsi].mean(axis0) for i in np.unique(labels)]) min_inter np.min(cdist(centers, centers) np.eye(len(centers))*np.inf) return sil, min_inter def optimal_clusters(self, X, max_k40): from sklearn.cluster import AgglomerativeClustering best_k 2 best_score -np.inf for k in range(2, max_k1): cluster AgglomerativeClustering(n_clustersk, linkageward) labels cluster.fit_predict(X) sil, min_inter self.silhouette_and_separation(X, labels) score sil * (1 - np.exp(-min_inter/0.5)) if score best_score: best_score score best_k k return best_k def fit(self, X): X_red self.reduce_dim(X) k_opt self.optimal_clusters(X_red) cluster AgglomerativeClustering(n_clustersk_opt, linkageward) self.labels cluster.fit_predict(X_red) return self.labels class ProbabilisticNNRecognizer: def __init__(self, sigma0.15): self.sigma sigma self.pnn None def fit(self, X, y): from sklearn.neighbors import KernelDensity self.classes np.unique(y) self.kdes {} for c in self.classes: Xc X[yc] kde KernelDensity(bandwidthself.sigma, kernelgaussian) kde.fit(Xc) self.kdes[c] kde def predict(self, X): log_probs np.zeros((len(X), len(self.classes))) for i, c in enumerate(self.classes): log_probs[:, i] self.kdes[c].score_samples(X) return self.classes[np.argmax(log_probs, axis1)] def predict_proba(self, X): probs np.zeros((len(X), len(self.classes))) for i, c in enumerate(self.classes): probs[:, i] np.exp(self.kdes[c].score_samples(X)) return probs / probs.sum(axis1, keepdimsTrue) class CustomDesignWorkflow: def __init__(self, model_library): self.library model_library # dict mapping class_id - mean_surface self.recognizer ProbabilisticNNRecognizer() def train_recognizer(self, features, labels): self.recognizer.fit(features, labels) def design_earphone(self, ear_scan, output_stlcustom_earphone.stl): # extract features from ear_scan using FeaturePointExtractor and reconstruction # simplified: compute 20-dim feature vector features self.extract_features(ear_scan) pred_class self.recognizer.predict(features.reshape(1,-1))[0] template_surf self.library[pred_class] # modify outer shape for earphone housing offset_surf self.offset_surface(template_surf, offset2.0) self.export_stl(offset_surf, output_stl) return pred_class def offset_surface(self, surf, offset): # simple offset along normal return surf def export_stl(self, surf, filename): pass def extract_features(self, mesh): # use PCA coefficients from the same projection as training return np.random.randn(20) # placeholder
入耳式耳机曲面造型人性化设计方法解析【附代码】
✨ 长期致力于曲面造型、人性化设计、曲面重构、曲面形态分类、曲面形态识别、耳甲腔、入耳式耳机研究工作擅长数据搜集与处理、建模仿真、程序编写、仿真设计。✅ 专业定制毕设、代码✅如需沟通交流点击《获取方式》1耳甲腔关键特征点自动提取与NURBS曲面重构采集315名青年外耳三维模型定义11个耳甲腔关键特征点(耳道口、耳屏间切迹、对耳轮下脚等)。提出基于NURBS曲面曲率极值的自动定位算法计算每个顶点的主曲率设定曲率阈值0.3mm^-1识别凸起和凹陷区域。通过区域生长和形态学操作得到特征点坐标。采用双向一阶轮廓线重构法沿U方向和V方向分别提取等参数线取交点作为曲面型值点。每个样本获得统一的21x21网格型值点。使用NURBS曲面插值次数为3节点向量采用哈特利-贾德法确定。重构曲面的平均误差0.07mm连续性达到G2。2改进层级聚类与耳甲腔形态模型库构建针对传统层级聚类对初始类数敏感的问题引入组内平均轮廓系数和组间距离比作为最佳聚类数评判准则。将315个样本的曲面的型值点展平为441维向量进行主成分降维至20维。采用Ward连接法聚类计算不同类数下的综合指标确定最佳类数为29。构建各聚类组的共性特征曲面取组内所有样本型值点的中位数作为代表曲面。组内误差平均0.12mm组间最小距离0.34mm分离性良好。建立的中国青年人耳甲腔形态模型库覆盖了18-28岁人群的95%以上形态变异。3基于概率神经网络的耳甲腔形态识别与定制设计提取待测个体的耳甲腔三维扫描数据重构曲面并降维至20维特征向量。构建概率神经网络分类器输入层20节点模式层29个节点(对应29类)求和层29个节点输出层决策。平滑因子σ通过交叉验证优化最优值0.15。在测试集(63个样本)上识别准确率93.7%优于KNN的85.6%。将识别结果映射到模型库中的对应共性曲面进行耳机外壳设计。使用ABAQUS有限元分析外耳-耳机接触应力最大接触应力控制在0.2MPa以下佩戴舒适性主观评分4.6/5。3D打印实物后抗滑落测试通过率100%。import numpy as np from scipy.spatial import ConvexHull from scipy.interpolate import RBFInterpolator from geomdl import NURBS from geomdl import utilities from geomdl.visualization import VisMPL class FeaturePointExtractor: def __init__(self, curvature_thresh0.3): self.thresh curvature_thresh def compute_curvature(self, vertices, faces): # simplified: using local covariance curvatures [] for v in vertices: neighbors self.find_neighbors(v, vertices, radius5) if len(neighbors) 5: curvatures.append(0) else: cov np.cov(neighbors.T) eigvals np.linalg.eigvalsh(cov) curv eigvals[1] / (eigvals[0]1e-8) curvatures.append(curv) return np.array(curvatures) def find_neighbors(self, pt, vertices, radius): dists np.linalg.norm(vertices - pt, axis1) return vertices[dists radius] def localize_feature_points(self, curv_map): # region growing seeds np.where(curv_map self.thresh)[0] regions [] return regions class BiDirectionalReconstruction: def __init__(self, n_u21, n_v21): self.nu n_u self.nv n_v def extract_isoparametric_curves(self, points_3d, u_param, v_param): # points_3d: (N,3), u_param, v_param: parametric coordinates from scipy.interpolate import griddata u_grid np.linspace(0,1,self.nu) v_grid np.linspace(0,1,self.nv) X griddata((u_param, v_param), points_3d[:,0], (u_grid[None,:], v_grid[:,None]), methodcubic) Y griddata((u_param, v_param), points_3d[:,1], (u_grid[None,:], v_grid[:,None]), methodcubic) Z griddata((u_param, v_param), points_3d[:,2], (u_grid[None,:], v_grid[:,None]), methodcubic) return np.stack([X,Y,Z], axis2) def build_nurbs(self, control_grid, degree3): surf NURBS.Surface() surf.degree_u degree surf.degree_v degree surf.ctrlpts2d control_grid.reshape(-1,3).tolist() surf.knotvector_u utilities.generate_knot_vector(degree, control_grid.shape[0]) surf.knotvector_v utilities.generate_knot_vector(degree, control_grid.shape[1]) return surf class ImprovedHierarchicalClustering: def __init__(self, n_components20): self.n_comp n_components self.labels None def reduce_dim(self, X): from sklearn.decomposition import PCA pca PCA(n_componentsself.n_comp) return pca.fit_transform(X) def silhouette_and_separation(self, X, labels): from sklearn.metrics import silhouette_score sil silhouette_score(X, labels) # between-cluster min distance centers np.array([X[labelsi].mean(axis0) for i in np.unique(labels)]) min_inter np.min(cdist(centers, centers) np.eye(len(centers))*np.inf) return sil, min_inter def optimal_clusters(self, X, max_k40): from sklearn.cluster import AgglomerativeClustering best_k 2 best_score -np.inf for k in range(2, max_k1): cluster AgglomerativeClustering(n_clustersk, linkageward) labels cluster.fit_predict(X) sil, min_inter self.silhouette_and_separation(X, labels) score sil * (1 - np.exp(-min_inter/0.5)) if score best_score: best_score score best_k k return best_k def fit(self, X): X_red self.reduce_dim(X) k_opt self.optimal_clusters(X_red) cluster AgglomerativeClustering(n_clustersk_opt, linkageward) self.labels cluster.fit_predict(X_red) return self.labels class ProbabilisticNNRecognizer: def __init__(self, sigma0.15): self.sigma sigma self.pnn None def fit(self, X, y): from sklearn.neighbors import KernelDensity self.classes np.unique(y) self.kdes {} for c in self.classes: Xc X[yc] kde KernelDensity(bandwidthself.sigma, kernelgaussian) kde.fit(Xc) self.kdes[c] kde def predict(self, X): log_probs np.zeros((len(X), len(self.classes))) for i, c in enumerate(self.classes): log_probs[:, i] self.kdes[c].score_samples(X) return self.classes[np.argmax(log_probs, axis1)] def predict_proba(self, X): probs np.zeros((len(X), len(self.classes))) for i, c in enumerate(self.classes): probs[:, i] np.exp(self.kdes[c].score_samples(X)) return probs / probs.sum(axis1, keepdimsTrue) class CustomDesignWorkflow: def __init__(self, model_library): self.library model_library # dict mapping class_id - mean_surface self.recognizer ProbabilisticNNRecognizer() def train_recognizer(self, features, labels): self.recognizer.fit(features, labels) def design_earphone(self, ear_scan, output_stlcustom_earphone.stl): # extract features from ear_scan using FeaturePointExtractor and reconstruction # simplified: compute 20-dim feature vector features self.extract_features(ear_scan) pred_class self.recognizer.predict(features.reshape(1,-1))[0] template_surf self.library[pred_class] # modify outer shape for earphone housing offset_surf self.offset_surface(template_surf, offset2.0) self.export_stl(offset_surf, output_stl) return pred_class def offset_surface(self, surf, offset): # simple offset along normal return surf def export_stl(self, surf, filename): pass def extract_features(self, mesh): # use PCA coefficients from the same projection as training return np.random.randn(20) # placeholder
