Patent Publication Number: US-11378520-B2

Title: Auto focus function for vision inspection system

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims benefit to Chinese Application No. 202010265772.3, filed Apr. 7, 2020, the subject matter of which is herein incorporated by reference in its entirety. 
     BACKGROUND OF THE INVENTION 
     The subject matter herein relates generally to vision inspection systems. 
     Inspection systems are used for inspecting parts during a manufacturing process. Conventional inspection systems use personnel to manually inspect parts as the parts move along a conveyor. Defective parts are removed from the conveyor. Such manual inspection systems are labor intensive and high cost. The manual inspection systems have low detection accuracy leading to poor product consistency. Additionally, manual inspection systems suffer from human error due to fatigue, such as missed defects, wrong counts, misplacing of parts, and the like. 
     Some known inspection systems use machine vision for inspecting parts. The machine vision inspection systems use cameras to image the parts. The images are processed to detect defects with the parts. Capturing quality images is important for analysis during inspection and training during machine learning. Image quality may be affected by the distance of the imaging device from the part. 
     A need remains for a vision inspection system that may be operated in a cost effective and reliable manner. 
     BRIEF DESCRIPTION OF THE INVENTION 
     In an embodiment, a vision inspection system is provided including a platform supporting parts for inspection at an inspection zone, an inspection station positioned adjacent the platform at the inspection zone including an imaging device to image the parts on the platform, and a vision inspection controller receiving images from the imaging device. The vision inspection controller includes an auto focus module for orienting the imaging device relative to the inspection zone. The auto focus module determines a working distance for the imaging device from the inspection zone. The auto focus module calculates an image contrast score of pixel values of the images at various working distances from the inspection zone. The vision inspection controller causes the inspection station to operate the imaging device at an imaging working distance corresponding to the working distance associated with the highest image contrast score. 
     In an embodiment, a vision imaging system is provided including a platform supporting parts for inspection at an inspection zone, an inspection station positioned adjacent the platform including an imaging device to image the parts in a field of view above the upper surface, and a vision inspection controller receiving images from the imaging device. The vision inspection controller includes an auto focus module for orienting the imaging device relative to the inspection zone. The auto focus module includes one or more processors configured to move the imaging device to a first working distance from the inspection zone, one or more processors configured to capture a first image at the first working distance, and one or more processors configured to calculate a first image contrast score of pixel values of the first image. The auto focus module includes one or more processors configured to move the imaging device to a second working distance from the inspection zone, one or more processors configured to capture a second image at the second working distance, and one or more processors configured to calculate a second image contrast score of pixel values of the second image. The auto focus module includes one or more processors configured to compare the first image contrast score and the second image contrast score to determine which has a higher image contrast score. The inspection station operates the imaging device at an imaging working distance equal to the working distance associated with the higher image contrast score to image the parts. 
     In an embodiment, a method of inspecting parts is provided including moving an imaging device to a first working distance from an inspection zone, capturing a first image at the first working distance, and calculating a first image contrast score of pixel values of the first image. The method includes moving the imaging device to a second working distance from the inspection zone, capturing a second image at the second working distance, and calculating a second image contrast score of pixel values of the second image. The method includes comparing the first image contrast score and the second image contrast score to determine which has a higher image contrast score value. The method includes operating the imaging device at an imaging working distance equal to the working distance associated with the higher image contrast score value to image the parts. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic illustration of a machine  10  for manufacturing parts  50 , such as parts used in electrical connectors. 
         FIG. 2  is a side view of a portion of the vision inspection system  100  showing the imaging device  106  relative to the part  50  on the platform  102 . 
         FIG. 3  is a side view of a portion of the vision inspection system  100  showing the imaging device  106  relative to the part  50  on the platform  102 . 
         FIG. 4  is a side view of a portion of the vision inspection system  100  showing the imaging device  106  relative to the part  50  on the platform  102 . 
         FIG. 5  is a flow chart of a method of inspecting the parts  50  in accordance with an exemplary embodiment. 
         FIG. 6  is a chart showing image sharpness at various working distances in accordance with an exemplary embodiment. 
         FIG. 7  is a chart showing image sharpness at various working distances in accordance with an exemplary embodiment. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       FIG. 1  is a schematic illustration of a machine  10  for manufacturing parts  50 , such as parts used in electrical connectors. For example, the parts  50  may be contacts, housings, circuit boards, or other types of parts. The machine  10  may be used for manufacturing parts used in other industries. The machine  10  includes one or more forming machines  20  used to form various parts  50 . For example, the forming machines  20  may include a molding machine, a press, a lathe, and the like. The machine  10  includes a vision inspection system  100  used to inspect the various parts  50 . The machine  10  includes one or more processing machines  30  used for processing the various parts  50 . For example, the processing machines  30  may include assembly machines, packaging machines, and the like. For example, in various embodiments, the parts  50  may be assembled, such as at an assembly station, prior to packaging, such as at a packing station. The parts  50  are transported between the forming machines  20  and the vision inspection system  100 . The vision inspection system  100  is used for quality inspection of the parts  50  and removes defective parts for scrap or further inspection. The acceptable parts  50  that have passed inspection by the vision inspection system  100  are transported between the vision inspection system  100  and the processing machines  30 . 
     The vision inspection system  100  includes a platform  102  that supports the parts  50  through the vision inspection system  100 . The parts  50  may be sorted on the platform  102 . For example, defective parts and/or acceptable parts may be removed from the platform  102  and placed in bins or containers or moved to another station, such as to the processing machines  30 . The platform  102  may be used to feed or move the parts  50  through the vision inspection system  100 . In various embodiments, the platform  102  may support the parts without the need for fixturing, which increases the throughput of the parts  50  through the vision inspection system  100 . 
     The vision inspection system  100  includes an inspection station  104  having one or more imaging devices  106  that image the parts  50  on the platform  102  within a field of view of the imaging device(s)  106  at an inspection zone (for example, an upper surface of the platform  102 ). The vision inspection system  100  includes a vision inspection controller  108  that receives the images from the imaging device  106  and processes the images. For example, the vision inspection controller  108  may be used to determine inspection results. The vision inspection controller  108  determines if each part  50  passes or fails inspection. The vision inspection controller  108  may reject parts  50  that are defective. In an exemplary embodiment, the vision inspection controller  108  includes an artificial intelligence (AI) learning module used to update image analysis based on the images received from the imaging device  106 . The vision inspection controller  108  may be updated and trained in real time during operation of the vision inspection system  100 . 
     In an exemplary embodiment, the vision inspection controller  108  includes an auto focus module  120  for orienting the imaging device relative to the inspection zone. The auto focus module  120  determines an imaging working distance for the imaging device  106  from the inspection zone for achieving enhanced images. For example, the auto focus module  120  determines the working distance that provides the sharpest images of the parts  50  within the inspection zone. The auto focus module  120  may be trained during a calibration process by imaging at various working distances, processing each of the images, and selecting the operating working distance that corresponds to the working distance associated with the sharpest image. In an exemplary embodiment, the auto focus module  120  calculates an image contrast score of pixel values of the images at the various working distances to determine the working distance associated with the highest image contrast score. The higher image contrast corresponds to images having higher transition in the pixel values. The higher transition in pixel values are typical of sharp, high quality images. The images with higher transition, and higher image contrast scores are clearer images. The images with lower transition, and lower image contrast scores are blurrier images. In an exemplary embodiment, the calibration process may be performed automatically to determine the optimum working distance for the imaging device  106 . 
     The vision inspection system  100  includes a sorting device  110  for sorting the parts  50  based on the inspection results. For example, the sorting device  110  may separate the acceptable parts from the defective parts. The sorting device  110  may be multi-axis robot manipulator configured to grip and pick the parts off of the platform  102 . In other various embodiments, the sorting device  110  may include a pusher or ejector configured to push the acceptable and/or the defective parts off of the platform  102 . 
     In an exemplary embodiment, the vision inspection system  100  may be used to inspect different types of parts  50 . For example, the vision inspection system  100  may be used to inspect different sized parts, different shaped parts, parts in different orientations, and the like. The auto focus module  120  may be calibrated for each of the different types of parts  50  to determine different working distances for the different types of parts  50 . 
     In an exemplary embodiment, the vision inspection system  100  includes a part feeding device  112 . The parts  50  are loaded onto the platform  102  by the part feeding device  112 , which may include a hopper, a conveyor, or another type of feeding device. The parts  50  are presented to the inspection station  104  on the platform  102 . The parts  50  may be advanced or fed along the platform  102  to or through the inspection station  104  for inspection of the parts  50 . The parts  50  are removed from the platform  102  by the sorting device  110 . The parts  50  may be guided to a particular location of the platform  102 , such as proximate to a center of the platform  102  between a first side  122  and a second side  124  of the platform  102 . The parts  50  may be loaded onto the platform  102 , such as proximate to a rear  126  of the platform  102 , and advanced forward by or on the platform  102  toward the front  128  of the platform  102 . Other types of part feeding devices  112  may be provided in alternative embodiments. The platform  102  includes a plate  130  having an upper surface  132  used to support the parts  50 . The plate  130  may be a vibration tray that is vibrated to advance the parts  50  from the rear  126  toward the front  128 . The plate  130  may be rectangular. However, the plate  130  may have other shapes in alternative embodiments. 
     The imaging device  106  is located relative to an inspection zone for the parts  50  to view and image the parts  50 . For example, the imaging device  106  may be located above the upper surface  132  and views the parts  50  arranged on the upper surface  132  at an inspection zone (for example, directly below the imaging device  106 ). The imaging device  106  may be located at other locations, such as along a side of the plate  130 , below the plate  130  viewing through the plate  130  (for example, using a clear plate), or at another location. Optionally, multiple imaging devices  106  may be used viewing the parts  50  from different sides or for viewing different areas of the plate  130 . The imaging device  106  may be a camera, such as a visible light camera. The field of view of the imaging device  106  may be approximately centered between first and second sides  122 ,  124  of the platform  102 . The field of view of the imaging device  106  may be approximately centered between the rear  126  and the front  128  of the platform  102 . The imaging distance of the imaging device  106  above the inspection zone affects the image quality. For example, if the imaging device  106  is too close to the inspection zone, the image may be unclear. If the imaging device  106  is too far from the inspection zone, the image may be unclear. When the imaging device  106  is located at the proper imaging distance, the image is clear. In an exemplary embodiment, the auto focus module  120  of the vision inspection controller is used to determine the proper imaging distance for the imaging device  106  for achieving enhanced, clear images. 
     In an exemplary embodiment, the imaging device  106  is mounted to a position manipulator  140  for moving the imaging device  106  relative to the platform  102 . The position manipulator  140  may be an arm or a bracket that supports the imaging device  106 . In various embodiments, the position manipulator  140  may be positionable in multiple directions, such as in two-dimensional or three-dimensional space. The position manipulator  140  may be automatically adjusted, such as by a controller that controls an electric actuator to position the position manipulator  140  and the imaging device  106 . The position manipulator  162  may be adjusted by another control module, such as an AI control module. The position manipulator  162  may be mounted to the frame of the machine  10 , such as from below the platform  102 , above the platform  102 , at locations outside of the platform  102  or from inside the opening in the platform  102 , when present. The position manipulator  162  may be supported by other structures around the machine  10 . In other various embodiments, the position manipulator  140  may be manually adjusted. The position of the imaging device(s)  106  may be adjusted based on the types of parts  50  being imaged. For example, when a different type of part  50  is being imaged, the imaging device  106  may be moved based on the type of part being imaged. The auto focus module  120  may determine the proper imaging distance based on the type of part being imaged. 
     The sorting device  110  is positioned adjacent the platform  102 . The sorting device  110  may be used to separate acceptable parts from defective parts based on input from the imaging device  106 . Quality, high contrast images are used for inspecting the parts  50  based on the imaging device  106  being located at a proper working distance from the inspection zone. The high contrast images are used for training the vision inspection controller  108  using an AI learning module. In an exemplary embodiment, the sorting device  110  includes a robot arm  150  and a gripper  152  at a distal end  154  of the robot arm  150 . In various embodiments, the robot arm  150  is a four-axis robot arm or a six-axis robot arm. Other types of robot arms  150  may be used in alternative embodiments. The parts  50  are picked off of the platform  102  by the gripper  152 . The sorting device  110  is operated to remove the parts  50  from the platform  102 , such as the acceptable parts and/or the defective parts. The parts  50  may be moved to collection bins, such as a first collection bin  156  for the acceptable parts and a second collection bin  158  for the defective parts. In various embodiments, the sorting device  110  is operated to remove all of the parts and place each of the parts in the corresponding collection bin  156 ,  158 . In other various embodiments, the sorting device  110  is operated to remove only the acceptable parts to the first collection bin  156 , leaving the defective parts to be dropped into the second collection bin  158  (located at the front  128  of the platform  102 ) as the parts  50  are advanced in the feed direction. In other various embodiments, the sorting device  110  is operated to remove only the defective parts to the second collection bin  158 , leaving the acceptable parts to be dropped into the first collection bin  156  (located at the front  128  of the platform  102 ) as the parts  50  are advanced in the feed direction. Other types of part removal devices may be used in alternative embodiments, such as pushers, ejectors, and the like. 
     The vision inspection controller  108  is operably coupled to the imaging device  106  and the sorting device  110  for controlling operation of the sorting device  110 . The imaging device  106  communicates with the vision inspection controller  108  through machine vision software to process the data, analyze results, record findings, and make decisions based on the information. The vision inspection controller  108  provides consistent and efficient inspection automation. The vision inspection controller  108  determines the quality of manufacture of the parts  50 , such as determining if the parts  50  are acceptable or are defective. The vision inspection controller  108  identifies defects in the parts, when present. The auto focus module  120  of the vision inspection controller  108  determines the proper imaging distance for the imaging device  106 . The vision inspection controller  108  controls operation of the sorting device  110  based on the identified orientation of the parts  50 . 
     The vision inspection controller  108  receives the images from the imaging device  106  and processes the images to determine inspection results. In an exemplary embodiment, the vision inspection controller  108  includes one or more processors  180  for processing the images. The vision inspection controller  108  determines if each part  50  passes or fails inspection. The vision inspection controller  108  controls the sorting device  110  to remove the parts  50 , such as the acceptable parts and/or the defective parts, into the collection bins  156 ,  158 . Once the images are received, the images are processed based on an image analysis model. The images are compared to the image analysis model to determine if the part  50  has any defects. The image analysis model may be a three-dimensional model defining a baseline structure of the part being imaged. In other various embodiments, the image analysis model may be a series of two-dimensional models, such as for each imaging device  106 . The image analysis model may be based on images of known or quality passed parts, such as during a learning or training process. The image analysis model may be based on the design specifications of the part  50 . For example, the image analysis model may include design parameters for edges, surfaces, and features of the part. The image analysis model may include tolerance factors for the parameters, allowing offsets within the tolerance factors. During processing, the images may be individually processed or may be combined into a digital model of the part, which is then compared to the image analysis model. The images are processed based on the image analysis model to detect defects, such as short shot defects, flash defects, black dots, dirt, dents, scratches, or other types of defects. The images may be processed by performing pattern recognition of the images based on the image analysis model to compare patterns or features in the images to patterns or features in the image analysis model. The images may be processed by performing feature extraction of boundaries and surfaces detected in the images and comparing the boundaries and surfaces to the image analysis model. The vision inspection controller  108  may identify lines, edges, bridges, grooves, or other boundaries or surfaces within the image. The vision inspection controller  108  may perform contrast enhancement and or noise reduction of the images during processing. The vision inspection controller  108  may identify areas of interest within the image for enhanced processing. The vision inspection controller  108  may perform image segmentation during processing. 
     In an exemplary embodiment, the vision inspection controller  108  includes an artificial intelligence (AI) learning module  190 . The AI learning module  190  uses artificial intelligence to train the vision inspection controller  108  and improve inspection accuracy of the vision inspection controller  108 . Providing high quality images, such as by positioning the imaging device  106  at the proper working distance, improves the training and learning of the AI learning module  190 . The AI learning module  190  update image analysis model based on the images received from the imaging device  106 . For example, the image analysis model may be customized and configured by the AI learning module  190 . The images forming the basis of the image analysis model may be revised or updated based on images taken by the imaging devices  106 , using the AI learning module  190 . For example, the image analysis model may be based on multiple images, which are updated or expanded based on images from the AI learning module  190 . As the AI learning module expands the image analysis model, the quality of the image processing may be improved. The vision inspection controller  108  is updated and trained in real time during operation of the vision inspection system  100 . The AI learning module  190  of the vision inspection controller  108  may be operable in a learning mode to train the vision inspection controller  108  and develop the image analysis model. The image analysis model changes over time based on input from the AI learning module  190  (for example, based on images of the parts  50  taken by the imaging device  106 ). The AI learning module  190  may be used to update the auto focus module  120 . In alternative embodiments, the AI learning module  190  may be a separate module from the vision inspection controller  108  independently operable from the vision inspection controller  108 . For example, the AI learning module  190  may be separately coupled to the imaging devices  106  or other components of the machine  10 . 
     In an exemplary embodiment, the vision inspection controller  108  includes a user interface  192 . The user interface  192  includes a display  194 , such as a monitor. The user interface  192  includes one or more inputs  196 , such as a keyboard, a mouse, buttons, and the like. An operator is able to interact with the vision inspection controller  108  with the user interface  192 . 
       FIG. 2  is a side view of a portion of the vision inspection system  100  showing the imaging device  106  relative to the part  50  on the platform  102 . The imaging device  106  is shown at a first working distance  200 . The imaging device  106  is operably coupled to the vision inspection controller  108 . The vision inspection controller  108  receives the images from the imaging device  106  and processes the images. The auto focus module  120  is used to determine the clarity of the images by calculating an image contrast score of the pixel values of the images at the first working distance  200 . 
       FIG. 3  is a side view of a portion of the vision inspection system  100  showing the imaging device  106  relative to the part  50  on the platform  102 . The imaging device  106  is shown at a second working distance  202 . The imaging device  106  is operably coupled to the vision inspection controller  108 . The vision inspection controller  108  receives the images from the imaging device  106  and processes the images. The auto focus module  120  is used to determine the clarity of the images by calculating an image contrast score of the pixel values of the images at the second working distance  202 . 
       FIG. 4  is a side view of a portion of the vision inspection system  100  showing the imaging device  106  relative to the part  50  on the platform  102 . The imaging device  106  is shown at a third working distance  204 . The imaging device  106  is operably coupled to the vision inspection controller  108 . The vision inspection controller  108  receives the images from the imaging device  106  and processes the images. The auto focus module  120  is used to determine the clarity of the images by calculating an image contrast score of the pixel values of the images at the third working distance  204 . 
     With reference to  FIGS. 2-4 , the vision inspection controller  108  may be operable in a calibration mode to determine a proper imaging distance for the imaging device  106  based on the clarity of the images at the various working distances  200 ,  202 ,  204 . The vision inspection controller  108  uses the auto focus module  120  to determine the proper imaging distance. In an exemplary embodiment, the auto focus module  120  includes one or more processors configured to move the imaging device  106  to the first working distance  200  ( FIG. 2 ) from an inspection zone  210 , one or more processors configured to capture a first image at the first working distance  200 , and one or more processors configured to calculate a first image contrast score of pixel values of the first image. The auto focus module  120  includes one or more processors configured to move the imaging device  106  to the second working distance  202  ( FIG. 3 ) from the inspection zone  210 , one or more processors configured to capture a second image at the second working distance  202 , and one or more processors configured to calculate a second image contrast score of pixel values of the second image. The auto focus module  120  includes one or more processors configured to move the imaging device  106  to the third working distance  204  ( FIG. 4 ) from the inspection zone  210 , one or more processors configured to capture a third image at the third working distance  204 , and one or more processors configured to calculate a third image contrast score of pixel values of the third image. 
     In an exemplary embodiment, the imaging device  106  may capture color images, such as using a red/green/blue (RGB) additive primary color model. The auto focus module  120  may include one or more processors configured to converting the RGB images to grayscale images. The image contrast scores may be calculated based on the grayscale images. In an exemplary embodiment, the auto focus module  120  may calculate the image contrast scores by calculating an absolute difference between the pixel values of the images. The auto focus module  120  may calculate the image contrast scores by calculating a sum of squared difference (SSD) of the pixel values for the images. The pixel values may be a matrix of pixel values, such as a 3×3 matrix of pixel values having values of: 
     
       
         
           
               
             
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     The auto focus module  120  may calculate the SSD using the formula:
 
(A n,m −B n,m+1 ) 2 +(B n,m+1 −C n,m+2 ) 2 +(D n+1,m −E n+1,m+1 ) 2 +(E n+1,m+1 −F n+1,m+2 ) 2 +(G n+2,m −H n+2,m+1 ) 2 +(H n+2,m+1 −I n+2,m+2 ) 2 .
 
     The auto focus module  120  includes one or more processors configured to compare the image contrast scores to determine which has a higher image contrast score. The vision inspection controller  108  provides an output, such as to the display  194  and/or to the position manipulator  140  to operate the imaging device  106  at the imaging working distance equal to the working distance associated with the higher image contrast score to image the parts  50 . The AI learning module  190  may be used to update the auto focus module  120 . For example, images processed by the AI learning module  192  are used to update the auto focus module  120 . 
       FIG. 5  is a flow chart of a method of inspecting the parts  50  in accordance with an exemplary embodiment. The method, at  500 , includes positioning the imaging device  106  at a first working distance (WD 1 ) from the inspection zone. The method, at  502 , includes capturing a first RGB image (M×N×3). The first RGB image includes an M row by N column matrix, having red, green and blue values. The RGB image is a true color matrix where the first two indexes (M, N) are the coordinates of the pixel and the third index is the color component. For example, (M,N,1) is the red pixel value, (M,N,2) is the green, and (M,N,3) is the blue component. The method, at  504 , includes converting the first RGB image to a first greyscale image (M×N×1, where the first two indexes (M, N) are the coordinates of the pixel and the third index represents a monochrome or greyscale intensity component ranging from black to white). The greyscale image has a matrix (M×N) of pixel values. The method, at  506 , includes calculating a first image contrast score of pixel values for the first greyscale image at the first working distance. The image contrast score may be calculated by calculating an absolute difference between the pixel values of the images. The image contrast score may be calculated by calculating a sum of squared difference (SSD) of the pixel values for the images. After the first image contrast score is determined, the system triggers for a new imaging position. 
     The method, at  510 , includes positioning the imaging device  106  at a second working distance (WD 2 ) from the inspection zone. The second working distance WD 2  is located at a spacing (S) from the first working distance (WD 1 +S). The method, at  512 , includes capturing a second RGB image (M×N×3). The RGB image has a matrix (M×N) of red pixel values, a matrix (M×N) of green pixel values, and a matrix (M×N) of blue pixel values. The method, at  514 , includes converting the second RGB image to a second greyscale image (M×N×1). The greyscale image has a matrix (M×N) of pixel values, such as a 3×3 matrix of pixel values. The method, at  516 , includes calculating a second image contrast score of pixel values for the second greyscale image at the second working distance WD 2 . The second image contrast score may be calculated by calculating an absolute difference between the pixel values of the images. The second image contrast score may be calculated by calculating the SSD of the pixel values for the images. After the second image contrast score is determined, the system triggers for a new imaging position. 
     The method, at  520 , includes positioning the imaging device  106  at a third working distance WD 3  from the inspection zone. The third working distance is located at a spacing (S) from the second working distance WD 2  (WD 2 +S or WD 1 +S+S). The third working distance WD 3  from the inspection zone is located the first working distance 1S and another spacing 1S from the second working distance to the third working distance (for example, 1S+1S=2S) The spacings may be different in alternative embodiments. The method, at  522 , includes capturing a third RGB image (M×N×3). The RGB image has a matrix (M×N) of red pixel values, a matrix (M×N) of green pixel values, and a matrix (M×N) of blue pixel values. The method, at  524 , includes converting the third RGB image to a third greyscale image (M×N×1). The greyscale image has a matrix (M×N) of pixel values, such as a 3×3 matrix of pixel values. The method, at  526 , includes calculating a third image contrast score of pixel values for the third greyscale image at the third working distance WD 3 . The third image contrast score may be calculated by calculating an absolute difference between the pixel values of the images. The third image contrast score may be calculated by calculating the SSD of the pixel values for the images. After the third image contrast score is determined, the system triggers for a new imaging position. 
     The method, at  530 , includes positioning the imaging device  106  at an n th  working distance WD 4  from the inspection zone. The n th  working distance is located at any distance beyond the third working distance by n th  spacing (S). The WD 4  may be a spacing S from the third working distance WD 3  or any other spacing (for example, 2S, 3S or another multiple of the spacing S). The method, at  532 , includes capturing an n th  RGB image (M×N×3). The RGB image has a matrix (M×N) of red pixel values, a matrix (M×N) of green pixel values, and a matrix (M×N) of blue pixel values. The method, at  534 , includes converting the n th  RGB image to an n th  greyscale image (M×N×1). The greyscale image has a matrix (M×N) of pixel values, such as a 3×3 matrix of pixel values. The method, at  536 , includes calculating an n th  image contrast score of pixel values for the n th  greyscale image at the n th  working distance. The n th  image contrast score may be calculated by calculating an absolute difference between the pixel values of the images. The n th  image contrast score may be calculated by calculating the SSD of the pixel values for the images. 
     The method, at  540 , includes comparing the image contrast scores to determine which has a higher image contrast score value. The method, at  542 , includes operating the imaging device  106  at an imaging working distance equal to the working distance associated with the higher image contrast score value to image the parts  50 . 
       FIG. 6  is a chart showing image sharpness at various working distances in accordance with an exemplary embodiment. The vision inspection controller  108  performs an auto focus process to determine an imaging working distance for the imaging device  106 . In the illustrated embodiment, images are captured at five working distances (WD 1 , WD 2 , WD 3 , WD 4 , WD 5 ). The vision inspection controller  108  is configured to calculate image contrast scores at each of the image working distances. In the illustrated embodiment, the image contrast scores are normalized. The vision inspection controller  108  determines which image contrast scores has the highest image contrast score value, which is the image contrast score associated with the second working distance (WD 2 ) in the illustrated embodiment. The vision inspection controller  108  outputs the imaging working distance as corresponding with the working distance having the highest image contrast score. The vision inspection controller  108  causes the imaging device  106  to operate at the working distance associated with the highest image contrast score value, namely WD 2 . For example, the vision inspection controller  108  adjusts the position manipulator  140  ( FIG. 1 ) to position the imaging device  106  at the second working distance WD 2 . The controller  108  may operate control of an electric actuator, such as one or more servo motors, to control the position of the imaging device  106 . The position manipulator  162  may be adjusted by another control module, such as the AI control module. 
       FIG. 7  is a chart showing image sharpness at various working distances in accordance with an exemplary embodiment. The vision inspection controller  108  performs an auto focus process to determine an imaging working distance for the imaging device  106 . In the illustrated embodiment, images are captured at five working distances (WD 1 , WD 2 , WD 3 , WD 4 , WD 5 ). The vision inspection controller  108  is configured to calculate image contrast scores at each of the image working distances. In the illustrated embodiment, the image contrast scores are normalized. The vision inspection controller  108  determines which image contrast scores has the highest image contrast score value, which is the image contrast score associated with the fifth working distance (WD 5 ) in the illustrated embodiment. The vision inspection controller  108  outputs the imaging working distance as corresponding with the working distance having the highest image contrast score. The vision inspection controller  108  causes the imaging device  106  to operate at the working distance associated with the highest image contrast score value, namely WD 5 . In various embodiments, the vision inspection controller  108  may continue to perform the auto focus process since the last imaging position corresponds to the highest image contrast score to determine if a working distance even further than WD 5  has a higher image contrast score. The vision inspection controller  108  may adjust the position manipulator  140  ( FIG. 1 ) to position the imaging device  106  at the appropriate working distance based on the imaged working distances. The controller  108  may operate control of an electric actuator, such as one or more servo motors, to control the position of the imaging device  106 . The position manipulator  162  may be adjusted by another control module, such as the AI control module. In various embodiments, if the image at the sixth working distance has a lower image contrast score, then the vision inspection controller  108  has determined the working distance corresponding to the highest image contrast score. For example, such curve may be a polynomial graph, such as a quadratic curve (for example, a parabola) having a local maximum. The vision inspection controller  108  compares the images at each of the working distances to adjust the position manipulator  140  to position the imaging device  106  at the appropriate working distance. 
     It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-described embodiments (and/or aspects thereof) may be used in combination with each other. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from its scope. Dimensions, types of materials, orientations of the various components, and the number and positions of the various components described herein are intended to define parameters of certain embodiments, and are by no means limiting and are merely exemplary embodiments. Many other embodiments and modifications within the spirit and scope of the claims will be apparent to those of skill in the art upon reviewing the above description. The scope of the invention should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Moreover, in the following claims, the terms “first,” “second,” and “third,” etc. are used merely as labels, and are not intended to impose numerical requirements on their objects. Further, the limitations of the following claims are not written in means-plus-function format and are not intended to be interpreted based on 35 U.S.C. § 112(f), unless and until such claim limitations expressly use the phrase “means for” followed by a statement of function void of further structure.