Objective Function of Motion Generation

As the simplest means, the dimension of a four-bar linkage can be roughly derived by choosing the resemblance in atlas through visual comparison with the prescribed trajectory, and followed by positioning the linkage relative to the reference, which if applicable refers to the mandible here. Approximating a pair of trajectories by atlas could not secure the choice with a required precision. Numeric solution that imposes a group of optimization objectives upon the

mechanism is preferable undertaken in the course of dimensional synthesis, the result of which will inevitably affect some kinematic parameters such as the velocity and the efficiency of the mechanism which could be utilized to impose the constraints in the course of synthesis the mechanism.

A B

Figure 3-17 Dyad illustration (A) original position (B) other positions

ܲ௝െ ܣ௝ ൌ ܳሺܲଵെ ܣଵሻ (3-20)

ܣ௝ൌ ܲ௝൅ ܳሺܲଵെ ܣଵሻ (3-21)

Where, ൌ ൤…‘• ߠ_{•‹ ߠ}ଵ௝ െ •‹ ߠଵ௝

ଵ௝ …‘• ߠଵ௝ ൨angle ߠଵ௝ around A from the initial position.

The four-bar linkage as shown in Figure 3-17 (A) can be decomposed into two groups of dyads, denoted as A0A1 and B0B1, which sharing the identical composition can be considered to have

each coupling point go through a string of the given locus. The left-side dyad A0A1 that is

picked out as shown in Figure 3-17 (B) represents the same formulation of the structural error at both sides of dyad, the feasible outcomes from which can be combined to construct into the four-bar linkage. The relationship of the coupling points amid prescribed positions in a dyad yields an Eq. with substitution of values in the form of the vector.

In Figure 3-17 (B) the coupler point is generally denoted as ܲ (note to differentiate the ones used in the jaw CP and IP), and ܲ௝ is denoted as the coordinates of the ݆th position, all of which spreading over the specified path are given in the specification and expected to be met by the coupler of four-bar linkage; ܣ_௝ is the ݆th position of the A correspondingly; the rotation of the coupler can be written in Eq. (3-20) which is converted to express floating crank tip with respect to the prescribed coupler points in Eq. (3-21).The distance between each crank floating tip ܣ௝ over all given coupler points and the stationary tip representing the length of the crank, should maintain constant in Eq. (3-22).

B0 A1 A0 B1 P Y X P1 A1 Pj Aj A0 X Y θ1j

ฮܣ௝െ ܣ଴ฮ ൌ ԡܣଵെ ܣ଴ԡ (3-22)

݆ ൌ ʹǡ͵ǡ ڮ ǡ ݉

Therefore the discrepancy between the subsequent crank length and of the initial one expressed in the form of least-square error in Eq. (3-23), formulates the structural error, which is expected to be minimized over all prescribed positions, as written in Eq. (3-24).

݂௝ሺݔሻ ൌ ฮܣ௝െ ܣ଴ฮ െ ԡܣଵെ ܣ଴ԡ ൌ ൫ܣ௝െ ܣ଴൯ ் ή ൫ܣ௝െ ܣ଴൯ െ ሺܣଵെ ܣ଴ሻ்ή ሺܣଵെ ܣ଴ሻ (3-23) ‹ ܨሺݔሻ ൌ ෍ ݂_௝ଶ_ሺݔሻ ௠ ௝ୀଶ (3-24)

The coordinate of stationary point ܣ_଴ in the dyad is denoted as ሾݔ_஺଴ǡ ݕ_஺଴ሿ, and the coordinate of floating tip ܣଵ at the initial position is ሾݔ஺ଵǡ ݕ஺ଵሿ; the coordinate of ܣ଴ and ܣଵ are the unknowns in all equations, as expressed to be ݔ ൌ ሾݔଵǡ ݔଶǡ ݔଷǡ ݔସሿ் ൌ ሾݔ஺଴ǡ ݕ஺଴ǡ ݔ஺ଵǡ ݕ஺ଵሿ்ǤSubstitute ܣ௝ of the form of Eq. (3-21) into Eq. (3-23), the length error can be written in Eq. (3-25) with respect to the ݔ (ሾݔ_ଵǡ ݔ_ଶǡ ݔ_ଷǡ ݔ_ସሿ்_).

݂_௝ሺݔሻ ൌ ܯ௝ଵݔଵݔଷ൅ ܯ௝ଶݔଵݔସ൅ ܯ௝ଷݔଶݔଷ൅ ܯ௝ସݔଶݔସ

൅ܯ_௝ହݔ_ଵ൅ ܯ_௝଺ݔ_ଶ൅ ܯ_௝଻ݔ_ଷ൅ ܯ_௝଼ݔ_ସ൅ ܯ_௝ଽ (3-25)

Where all the coefficients are denoted as follows:

ە ۖ ۖ ۔ ۖ ۖ ۓ ܯ_ܯ௝ଵൌ ܯ௝ସൌ ͳ െ …‘• ߠଵ௝ ௝ଶൌ െܯ௝ଷൌ •‹ ߠଵ௝ ܯ_௝ହൌ ݔ_௉ଵ…‘• ߠ_ଵ௝െ ݕ_௉ଵ•‹ ߠ_ଵ௝െ ݔ_௉௝ ܯ_௝଺ൌ ݔ_௉ଵ•‹ ߠ_ଵ௝൅ ݕ_௉ଵ…‘• ߠ_ଵ௝െ ݕ_௉௝ ܯ௝଻ൌ െܯ௝ହ…‘• ߠଵ௝െ ܯ௝଺•‹ ߠଵ௝ ܯ_௝଼ൌ ܯ_௝ହ•‹ ߠ_ଵ௝െ ܯ_௝଺…‘• ߠ_ଵ௝ ܯ௝ଽൌ ൫ܯ௝ହଶ൅ ܯ௝଺ଶ൯Ȁʹ

The ൣݔ௉௝ǡ ݕ௉௝൧ and ߠଵ௝ as known parameters are denoted as the coordinates of the coupler in a natural coordinate system and each corresponding rotated angle with respect to the initial position, respectively provided by the specification by referring to Figure 3-17 (B). The mandible for training is placed to contact with the linkage exactly on the coupler to do the rigid-body guidance movement; the coordinates of the points in the condyle and incisor trajectories are herein given in the relative frame that 15 pairs of the points are linked linearly

on each own track and incremental rotation of each position relative to the initial one is given after calculation as well, and no other interpolation is provided upon the existing data.

To adapt the synthesis process, the CP at the rest position establishes the origin of the coordinate frame for this application, and the size of the jaw for initializing the IP coordinates applies the one referred in section, whose details can be acquired in great precision. Accordingly the initial position of the incisor can be written in the format of ሾͺͻǤͷͳǡ െͶͷǤͲͳሿ with respect to the condyle of ሾͲǡͲሿ.

The coordinates of the condyle and incisor that are travelling along each trajectory are respectively denoted as ൣܥ̴ܲݔ௝ǡ ܥ̴ܲݕ௝൧ and ൣܫ̴ܲݔ௝ǡ ܫ̴ܲݕ௝൧ at each positionሾ݆ሿ, sequentially starting from the closed state of the jaw. The IP coordinates are invoked in the optimization equations of Eq. (3-25) to replace ൣݔ_௉௝ǡ ݕ_௉௝൧; and the incremental rotation angles at each step of which is denoted as ݍ௝ଵൌ ݍ௝െ ݍଵ, calculated from the ݍ௝ ൌ

ூ௉̴௬ೕି஼௉̴௬ೕ

ூ௉̴௫ೕି஼௉̴௫ೕ and ݍଵൌ

ூ௉̴௬భି஼௉̴௬భ

ூ௉̴௫భି஼௉̴௫భ are invoked to substitute all of the ߠଵ௝.

Typical performances of the mechanism in terms of the mechanical system rely on its kinematic parameters, which form the constraints directly or indirectly with respect to the synthesis variables. The constraints the mechanism is subject to encircle a set of ranges for each variable in seeking a desirable combination that satisfies the requirements, and are incorporated into the process of the minimization of the objective function in two ways, depending on adopting the form of penalty function attached behind. The constraints are set up in aspects of kinematics and the process of synthesis that specifically embodies on the application based on the current synthesis model, among them the commonly referred ones are listed up in the aspects of the construction.

1) The length of each link ܮ௖௥௔௡௞, ܮ௖௢௨௣௟௘௥ is restricted within a range of ሾͷǡʹͲሿ for the crank, and ሾͷǡ͸Ͳሿ for the other, and the relation in terms of the length

ܮ_{௖௢௨௣௟௘௥}൐ ܮ௖௥௔௡௞;

2) Rotational angle of the crank Ƚ over all prescribed points is limited within ሾͲǡͳͺͲሿ; 3) ߠଵ௝ is arranged in sequence of the points concerning ݆;

4) Structural error ȁߝȁ is specified within a range.

All of the k constraints are arranged in the form of inequality, as …௜ሺݔଵǡ ݔଶǡ ݔଷǡ ݔସሻ ൑ Ͳǡ ݅ ൌ ͳǡʹǡ ڮ ǡ ݇. Therefore, the complete problem can be formulated by tailing above constraints into the structural error in Eq. (3-26)

‹ ܨሺݔሻ ൌ ෍ ݂_௝ଶ_ሺݔሻ ௠ ௝ୀଶ ൅ ෍ …_௝ሺݔሻ ௞ ௜ୀଵ (3-26)

There are four unknowns in the Eq. (3-25) of each position, all of which compose of amount of

(n-1) equations over the given n positions. without consideration of any technical constraints in the four-bar linkage, the number of given points for the exact synthesis should be less than five, more than the number of which the problem turns to approximate synthesis.

Instead of using the derivatives of the completed function that can signify a searching direction in each step of iteration to find the local optima, the nonlinear LSQ equations are optimized by the built-in function in MATLAB. Newton algorithm based iteration is configured in the process of the optimization, which is too sensitive to the initial value to occasionally drive equations falling into a local optimum. As to the precise synthesis, four equations that are formed with five points illustrative of both trajectories on condyle and incisor can be theoretically solvable subject to four unknowns with little error, so the original LSQ function can be simplified to the minimization of each Eq., all of which compose of multi-objective functions, as written in Eq. (3-27). Still, it is also equivalent to minimize the worst-case among multivariable functions in the circumstance of precise synthesis, as expressed in Eq. (3-28), without avoiding of starting at an initial estimate. The solving process takes advantage of built-in functions in MATLAB while being subject to the constraints in the form of equality and inequality. Regarding the approximate synthesis, the optimization is simply undertaken by going through the solving process of LSQ function in MATLAB in order to find the global minimum; any conversion of the objective function is avoided here since local optima could be brought into the multivariable minimization, in which the error could not be globally satisfied.

‹ ܨሺݔሻ ൌ ‹ሼሾȁ݂ଵሺݔሻȁǡ ȁ݂ଶሺݔሻȁǡ ڮ ȁ݂௡ሺݔሻȁሿ்ሽ (3-27)

‹ ܨሺݔሻ ൌ ‹ ቄƒšሼሾȁ݂ଵሺݔሻȁǡ ȁ݂ଶሺݔሻȁǡ ڮ ȁ݂௡ሺݔሻȁሿ்ሽቅ (3-28)

Suppose ܨሺݔሻ stands its globally minimum point, which is the necessary conditions of the LSQ objective function, i.e., the Jacobian equals to zero, which is written in Eq. (3-29).

߲ܨ ߲ݔ ൌ Ͳ (3-29) Namely, σ௠ ݂_௝ିଵ൫ܯ_௝ଵݔ_ଷ൅ ܯ_௝ଶݔ_ସ൅ ܯ_௝ହ൯ ௝ୀଶ ൌ Ͳ σ௠௝ୀଶ݂௝ିଵ൫ܯ௝ଷݔଷ൅ ܯ௝ସݔସ൅ ܯ௝଺൯ൌ Ͳ σ௠௝ୀଶ݂௝ିଵ൫ܯ௝ଵݔଵ൅ ܯ௝ଷݔଶ൅ ܯ௝଻൯ൌ Ͳ σ௠ ݂_௝ିଵ൫ܯ_௝ଶݔ_ଵ൅ ܯ_௝ସݔ_ଶ൅ ܯ_௝଼൯ ௝ୀଶ ൌ Ͳ