METHOD FOR DETECTION OF MANUFACTURE DEFECTS

• US 20050149282A1
• Filed: 12/27/2003
• Published: 07/07/2005
• Est. Priority Date: 12/27/2003
• Status: Active Grant

• Current Assignee: National Chiao Tung University (Government of The Republic of China)
• Original Assignee: Shian-Shyong Tseng
• Inventors: Wang, Ching-Yao, Tseng, Shian-Shyong, Chen, Wei-Chou
• Granted Patent: US 6,944,561 B2
• Time in Patent Office: Days
• US Class Current: 702/82
• CPC Class Codes: G05B 19/41875 characterised by quality surveillance of production
• CPC Class Codes: G05B 2219/32179 Quality control, monitor production tool with multiple sensors
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

A novel method for detection of manufacture defects is disclosed. The invented method is designed to discover root cause machine sets that cause defects in product manufactured in a process wherein a plurality of machines is involved. The method comprises obtaining manufacture data that relate to manufacture of products in the manufacture process, generating a candidate list of machine set by analyzing the manufacture data wherein the machine set includes machines relative to defects in the products, and identifying root cause machine sets from the list of machine set wherein the root cause machine sets include machines highly related to the defects.

Claims

1. A method for detection of manufacture defects, comprising:

obtaining manufacture data including number and sequence of stage and machine in a manufacture process, number, sequence and quality of products as manufactured, and respective manufacture sequences of products involved;

organizing machine sets consisted of at least one machine in said manufacture process;

calculating defect coverage values of respective machine set;

wherein said defect coverage value comprises ratio of number of defected product processed by element machine of said machine set to total number of product;

comparing said defect coverage values with a predetermined threshold to select candidate machine sets with defect coverage values greater than said threshold; and

outputting result of said selection.

4. The method according to claim 1 or 3, wherein defect coverage values of machine sets having one element are first calculated to select candidate machine sets and wherein defect coverage values of machine sets having one more element belonging to any candidate machine set selected in the previous step are then calculated to select candidate machine sets and so on, until defect coverage value of machine set having all elements of machine sets selected form the previous step is calculated.

2. A method for detection of manufacture defects, comprising:

obtaining manufacture data including number and sequence of stage and machine in a manufacture process, number, sequence and quality of products as manufactured, and respective manufacture sequences of products involved;

organizing machine sets consisted of at least one machine in said manufacture process to generate candidate machine sets;

calculating interestingness values φ

′

of said candidate machine sets;

wherein said calculation comprises calculation according to the following formula;

$\begin{array}{cc}{\mathrm{\varphi <br><br>}}^{\mathrm{\prime <br><br>}}=\frac{<br><br>A\&<br><br><br><br>B<br><br>-<br><br>A<br><br><br><br><br><br>B<br><br>/N}{\sqrt{<br><br>A<br><br><br><br><br><br>B<br><br><br><br>\left(1-<br><br>A<br><br>/N\right)<br><br>\left(1-<br><br>B<br><br>/N\right)}}*\mathrm{Continuity}& \left(1\right)\end{array}$

wherein N is the total number of products from a manufacture procedure, A represents a machine set, B represents a defect, |A| is the number of products that involve A, |B| is the number of products that involve B, and |A&

B| be the number of products that involve A and B;

“

Continuity”

represents sequential relation between defects of product and a candidate machine set and is calculated according to the following formula;

$\begin{array}{cc}\{\begin{array}{cc}\mathrm{Continutiy}=0& \mathrm{if}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>X<br><br>\le <br><br>1\\ \mathrm{Continutiy}=\frac{1}{\underset{i=1}{\overset{i=<br><br>X<br><br>-1}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>d<br><br>\left(\mathrm{\alpha <br><br>}<br><br>\left(x_i\right),\mathrm{\alpha <br><br>}<br><br>\left(x_{i+1}\right)\right)/<br><br>x<br><br>-1}& \mathrm{if}<br><br>\text{\hspace{1em}<br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>X<br><br>><br><br>1\end{array}<br><br>\text{\hspace{1em}<br><br>}& \left(2\right)\end{array}$

where X=(x₁, x₂, . . . ) denotes a sequence consisting of the defective products in the involved product sequence P=(p₁, p₂, . . . ) for a machineset (i.e., X is a subsequence of P), |X| denotes the number of defective products, α

(x_i) denotes the defective product x_i is the k-th product in the involved product sequence if α

(x_i)=k, and d(α

(x_i),α

(x_i+1)) is the distance of α

(x_i) and α

(x_i+1) and it can be easily calculated by α

(x<sub>i+1)−

α

(xi</sub>);

comparing the obtained interestingness values with a predetermined value to select root cause machine sets with interestingness values greater than said predetermined value; and

outputting result of said selection.

3. A method for detection of manufacture defects, comprising:

obtaining manufacture data including number and sequence of stage and machine in a manufacture process, number, sequence and quality of products as manufactured, and respective manufacture sequences of products involved;

organizing machine sets consisted of at least one machine in said manufacture process;

calculating defect coverage values of respective machine set;

wherein said defect coverage value comprises ratio of number of defected product processed by element machine of said machine set to total number of product;

comparing said defect coverage values with a predetermined threshold to select candidate machine sets with defect coverage values greater than said threshold;

calculating interestingness values φ

′

of said candidate machine sets;

wherein said calculation comprises calculation according to the following formula;

$\begin{array}{cc}{\mathrm{\varphi <br><br>}}^{\mathrm{\prime <br><br>}}=\frac{<br><br>A\&<br><br><br><br>B<br><br>-<br><br>A<br><br><br><br><br><br>B<br><br>/N}{\sqrt{<br><br>A<br><br><br><br><br><br>B<br><br><br><br>\left(1-<br><br>A<br><br>/N\right)<br><br>\left(1-<br><br>B<br><br>/N\right)}}*\mathrm{Continuity}& \left(1\right)\end{array}$

wherein N is the total number of products from a manufacture procedure, A represents a machine set, B represents a defect, |A| is the number of products that involve A, |B| is the number of products that involve B, and |A&

B| be the number of products that involve A and B;

“

Continuity”

represents sequential relation between defects of product and a candidate machine set and is calculated according to the following formula;

$\begin{array}{cc}\{\begin{array}{cc}\mathrm{Continutiy}=0& \mathrm{if}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>X<br><br>\le <br><br>1\\ \mathrm{Continutiy}=\frac{1}{\underset{i=1}{\overset{i=<br><br>X<br><br>-1}{\sum <br><br>}}<br><br>\text{\hspace{1em}<br><br>}<br><br>d<br><br>\left(\mathrm{\alpha <br><br>}<br><br>\left(x_i\right),\mathrm{\alpha <br><br>}<br><br>\left(x_{i+1}\right)\right)/<br><br>x<br><br>-1}& \mathrm{if}<br><br>\text{\hspace{1em}<br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>X<br><br>><br><br>1\end{array}<br><br>\text{\hspace{1em}<br><br>}& \left(2\right)\end{array}$

wherein X=(x1, x2, . . . ) and denotes a sequence consisted of the defective products in the involved product sequence P=(p1, p2, . . . ) for a candidate machine set, |X| denotes number of defected products, α

(x₁) denotes the order of defected product x₁ in the sequence of X and d(., .) denotes difference in order between two defected products;

comparing the obtained interestingness values with a predetermined value to select root cause machine sets with interestingness values greater than said predetermined value; and