diff --git a/pm2d/auto_tune.py b/pm2d/auto_tune.py
index 3c83b7b..4c715f3 100644
--- a/pm2d/auto_tune.py
+++ b/pm2d/auto_tune.py
@@ -152,14 +152,27 @@ def _cache_key(template_bgr: np.ndarray, mask: np.ndarray | None) -> str:
     return h.hexdigest()
 
 
-def auto_tune(template_bgr: np.ndarray, mask: np.ndarray | None = None) -> dict:
+def auto_tune(
+    template_bgr: np.ndarray,
+    mask: np.ndarray | None = None,
+    angle_tolerance_deg: float | None = None,
+    angle_center_deg: float = 0.0,
+) -> dict:
     """Analizza template e ritorna dict parametri suggeriti.
 
     Chiavi compatibili con edit_params PARAM_SCHEMA.
 
+    angle_tolerance_deg: se != None, restringe angle_range a
+    (center - tol, center + tol). Usare quando l'orientamento del
+    pezzo e' noto a priori (feeder con guida, posizionamento
+    meccanico): training molto piu rapido (24x meno varianti per
+    tol=15° vs 360° pieno).
+
     Risultato cachato in-memory (LRU): ri-chiamare con stessa ROI è O(1).
     """
     ck = _cache_key(template_bgr, mask)
+    if angle_tolerance_deg is not None:
+        ck = f"{ck}|tol={angle_tolerance_deg}|c={angle_center_deg}"
     cached = _TUNE_CACHE.get(ck)
     if cached is not None:
         _TUNE_CACHE.move_to_end(ck)
@@ -208,8 +221,13 @@ def auto_tune(template_bgr: np.ndarray, mask: np.ndarray | None = None) -> dict:
     # spread_radius proporzionale a risoluzione + pyramid (tolleranza ~1% dim)
     spread_radius = int(np.clip(max(3, min_side * 0.02), 3, 8))
 
-    # angle range ridotto se simmetria rotazionale
-    angle_max = 360.0 / sym["order"] if sym["order"] > 1 else 360.0
+    # angle range: priorita' a tolerance hint utente, poi simmetria rotazionale.
+    if angle_tolerance_deg is not None:
+        angle_min = float(angle_center_deg - angle_tolerance_deg)
+        angle_max = float(angle_center_deg + angle_tolerance_deg)
+    else:
+        angle_min = 0.0
+        angle_max = 360.0 / sym["order"] if sym["order"] > 1 else 360.0
 
     # min_score: se entropia orient alta → template distintivo → soglia alta ok
     # se entropia bassa → template ambiguo → soglia più permissiva
@@ -228,7 +246,7 @@ def auto_tune(template_bgr: np.ndarray, mask: np.ndarray | None = None) -> dict:
 
     result = {
         "backend": "line",
-        "angle_min": 0.0,
+        "angle_min": angle_min,
         "angle_max": angle_max,
         "angle_step": angle_step,
         "scale_min": 1.0,
diff --git a/pm2d/line_matcher.py b/pm2d/line_matcher.py
index a5195df..a4e4a59 100644
--- a/pm2d/line_matcher.py
+++ b/pm2d/line_matcher.py
@@ -192,6 +192,26 @@ class LineShapeMatcher:
                 np.array(picked_y, np.int32),
                 np.array(picked_b, np.int8))
 
+    def set_angle_range_around(
+        self, center_deg: float, tolerance_deg: float,
+    ) -> None:
+        """Restringe angle_range a (center - tol, center + tol).
+
+        Comodo helper per scenari in cui l'orientamento del pezzo e'
+        noto a priori entro ±tolerance_deg (es. feeder vibrante con
+        guida meccanica). Riduce drasticamente le varianti generate
+        in train(): es. ±15° vs 360° = 24x meno varianti, training
+        e matching molto piu veloci.
+
+        Esempio:
+            m.set_angle_range_around(0, 20)   # cerca solo in [-20, +20]
+            m.train(template)
+        """
+        self.angle_range_deg = (
+            float(center_deg - tolerance_deg),
+            float(center_deg + tolerance_deg),
+        )
+
     def _scale_list(self) -> list[float]:
         s0, s1 = self.scale_range
         if s0 >= s1 or self.scale_step <= 0: