Numpy, Dot Products on Multidimensional Arrays

I have some doubts on the numpy.dot product.

I define a matrix 6x6 like:

C=np.zeros((6,6))
C[0,0], C[1,1], C[2,2] = 129.5, 129.5, 129.5
C[3,3], C[4,4], C[5,5] = 25, 25, 25
C[0,1], C[0,2] = 82, 82
C[1,0], C[1,2] = 82, 82
C[2,0], C[2,1] = 82, 82

Then I recast it in a 4-rank tensor by using a multidimensional array

def long2short(m, n):
    """
    Given two indices m and n of the stiffness tensor the function
    return i the index of the Voigt matrix
    i = long2short(m,n)
    """
    if m == n:
        i = m
    elif (m == 1 and n == 2) or (m == 2 and n == 1):
        i = 3
    elif (m == 0 and n == 2) or (m == 2 and n == 0):
        i = 4
    elif (m == 0 and n == 1) or (m == 1 and n == 0):
        i = 5      
    return i

c=np.zeros((3,3,3,3))
for m in range(3):
    for n in range(3):
        for o in range(3):
            for p in range(3):
                i = long2short(m, n)
                j = long2short(o, p)
                c[m, n, o, p] = C[i, j]

And then I would like to change the coordinate reference system of the tensor by using the rotation matrix that I define like:

Q=np.array([[sqrt(2.0/3), 0, 1.0/sqrt(3)], [-1.0/sqrt(6), 1.0/sqrt(2), 1.0/sqrt(3)], [-1.0/sqrt(6), -1.0/sqrt(2), 1.0/sqrt(3)]])        
Qt = Q.transpose()

The matrix is orthogonal (althought the numerical precision is not perfect):

In [157]: np.dot(Q, Qt)
Out[157]: 
array([[  1.00000000e+00,   4.28259858e-17,   4.28259858e-17],
       [  4.28259858e-17,   1.00000000e+00,   2.24240114e-16],
       [  4.28259858e-17,   2.24240114e-16,   1.00000000e+00]])

But why then if I perform:

In [158]: a=np.dot(Q,Qt)
In [159]: c_mat=np.dot(a, c)
In [160]: a1 = np.dot(Qt, c)
In [161]: c_mat1=np.dot(Q, a1)

I get the expected value for c_mat (=c) but not for c_mat1? Is there some subtility to use dot on multidimensional arrays?