Method and apparatus for automatically inspecting and classifying different objects

Univeral conveying apparatus and method for automated inspection and classification of a variety of natural or man made product classes, having various geometrical configurations, e.g. spheroidal, spherical, cylindrical, parallelepiped, disc and plate shaped objects, or massive and hollow amophous objects. Incorporating a plurality of sensors interfaced to a plurality of microcomputers, measuring different product features, while predetermined combinations thereof are used for separating the objects or products, into a plurality of categories. Part of the sensors are stationary, while another part thereof are located and rotating on revolving inverter wheels. The stationary and rotating sensor groups are interfaced respectively to one or more stationary or rotating microcomputers, attached to the inverter wheels. The revolving and stationary microcomputers are electrically interconnected via slip rings, on the shafts on the inverter wheels. Conveing system incorporates product inversion, providing means for computer vision of both sides of rapidly moving objects or products, on two synchronized bottomless cup or tray conveyors, stacked one on top of the other, while the inverter wheels with reciprocating product grippers, transfer and invert inspected objects, from one conveyor to the other. Combines computer vision by reflected and/or transmitted radiation, with self radiation if any, in concert with other sensors. Some sensors in the grippers, intermittently contact or engage objects in cups or trays, by a controlled force or pressure. The grippers may be rigid, flexible or semiflexible. They may incorporate actuators and sensors for measuring mechanical properties of products and probe connectors for electrical functionality analysis.

BACKGROUND OF THE INVENTION 
1. Field of the Invention 
This invention relates to a method and universal conveying apparatus for 
automated inspection and classification of a variety of different product 
classes, while using a plurality of sensors interfaced to computer means 
for measuring different product features and separating same into 
predetermined categories. Following are some examples of product classes 
that can be conveyed, inspected and classified: 
a. Spherical and spheroidal objects such as fruits and vegetables, ball 
bearings, billiard and bowling balls and the like. 
b. Cylindrical objects such as cans, bottles, jars, drug capsules, 
cigarettes, ordnance shells, bullets, and the like. 
c. Disc shaped objects such as wheels, gears, bottle or jar closure caps, 
round plates, cakes, precooked meals or pizzas in aluminum foil trays, 
tablets and the like. 
d. Parallelepiped shaped objects such as boxes and containers (empty or 
with variously packaged items within), box-shaped various manufactured 
products, small appliances, components and parts. 
e. Plate shaped objects such as electronic printed circuit boards. 
f. Amorphous objects such as dates, berries, potatoes, avocado, soil clods, 
radioactive ore pieces, cookies, bread and other bakery products. 
2. Description of Prior Art 
Most existing automatic inspection machines are designed to classify or 
sort only a particular product class, and in most cases the classification 
is based on a single product feature. When more than one feature is used 
(usually less than three), they are addressed serially, one at a time, by 
one or several machines in a row. Of all products that can be efficiently 
inspected and classified by the machine disclosed hereby, sorting and 
sizing fresh fruits and vegetables is one of the most demanding and 
difficult tasks. 
Color sorting machines for various specific fruits and vegetables, using 
yellow/green, red/green, blue/green etc. light reflectance ratios, have 
been used commercially for many years. The operating principle of these 
machines was based on optical filtration of reflected light, while the 
classification decision was performed by analog circuitry. Analog circuit 
color sorting is usually specific for each particular fruit type. It has 
largely been made obsolete by the introduction of digital image 
processing, such as in the machine disclosed herein, whereby color sorting 
is just one of several product features detected by reflected light, i.e. 
size, shape and surface blemishes. 
The key component of any inspection machine, designed to sort a stream of 
objects in a mass production environment, is the conveying system. Its 
main function is to efficiently carry and present the inspected objects to 
the sensors. Three main types of conveying systems have been used hitherto 
in inspection machines based on computer vision: 
a. Flat belt conveyors. 
b. Cup conveyors. 
The disadvantage of these conveyors is that the underside of the objects 
carried on them can not be seen by the camera. The flat belt conveyor has 
an additional deficiency since the exact placement of the objects on it is 
not known. 
c. Roller conveyors, as in (1980 U.S. Pat. No. 4,221,297), which strive to 
overcome the above deficiencies by rapidly spinning spheroidal objects 
such as fruits, in front of a line scan camera. 
Another popular way of presenting all sides of an object to a set of 
cameras is to view it simultaneously from different angles, as it free 
falls in a projectile like trajectory from one conveyor to another. 
Inherent inaccuracy of this method stems from inconsistency of object 
orientation due to variability in sizes and shapes, while significant 
damage may be incurred by sensitive products, e.g. fruits and vegetables. 
Rapidly spinning a product in front of a line scan camera has essentially 
the same disadvantages, since it is very difficult to ascertain exactly 
one revolution view when the products vary in size and shape. Rapid 
spinning may also damage a delicate products. 
The above conveying and inspection principles comprise "Reduction Sorting", 
whereby diversion mechanisms selectively deflect different product 
fractions form the main product stream, usually based on a single feature, 
e.g. color, blemishes, size etc. Even if more than one feature is 
inspected, it is done so serially by different machines, i.e. a color 
grading machine is followed by a sizing machine, while grading is mostly 
manual. The diversion mechanism is usually a solenoid operated baffle or 
an air stream blast. In most cases further inspection is required to 
finalize product grading according to other product features, by 
additional machines or human inspectors. In contrast, the machine 
disclosed hereby performs "Full Sorting" whereby several features of each 
and every piece are inspected, while a Bayesian type multiple feature 
decision-making algorithm may be used to optimally classify the product, 
whereupon it is deposited onto the appropriate side delivery conveyor. 
Needless to say that the former method is less cost effective. 
The machine disclosed herein allows viewing and inspection of opposite 
sides of an objects at high speed by means of a pair of stacked cup 
conveyors and retaining grippers which hold the objects in place while 
they are turned around by an inverter wheel, as described in detail in the 
preferred embodiment of the present invention. This inversion apparatus is 
different and superior to the dual drum arrangement for capsule color 
sorting, as described in (1978 U.S. Pat. No. 4,082,188). 
Another case in point is sorting by specific gravity, such as may be used 
for separating freeze damaged oranges from wholesome fruit, thick skinned 
fruit from thin skinned fruit, potatoes from soil clods etc. The machine 
disclosed herein can determine specific gravity of each inspected piece by 
its weight and volume, in conjunction with a plurality of other features. 
In addition to the above product properties, it is sometimes also required 
to classify products by their mechanical or rheological properties. Thus a 
patent has been granted for an elaborate single feature machine which 
produces indentations on fruits for assessing their firmness, (1977 U.S. 
Pat. No. 4,061,020). Here again, the preferred embodiment of the present 
invention provides several superior means for measurement of mechanical 
properties of the inspected products, i.e. resistance to applied forces, 
contact pressure, frequency response or vibration damping characteristics 
and internal energy dissipation "on the go", without stopping the conveyor 
belt or reducing throughput speed. 
In summary, we may cite the following deficiencies of commercially 
available inspection and classification machines: 
a. The conveying apparatus of each machine is suitable for inspecting only 
a particular product class. This precludes cost effective standardized 
mass production, wherein adaptation to different products is only a matter 
of choosing the appropriate sensors and software package, while the 
structure of the machine and most of its mechanical parts remain the same. 
b. While classifying only a specific product, the further restriction to 
one or at most two or three product features constitutes "Reduction 
Sorting", capable in most cases of performing only part of the entire 
sorting task, while the remainder is performed manually. 
c. There is no provision for viewing both sides of an object while it rests 
upon a rapidly moving conveyor, enabling "Computer Vision Inspection" 
including reflected as well as transmitted radiation types, in conjunction 
with a plurality of other sensor systems. 
d. There is no provision for accomplishing c. while handling the product 
gently, thereby minimizing potential mechanical damage to delicate 
products. 
e. There is no means for physical contact with the inspected products 
"On-The-Go" whereby electrical connectors can be automatically attached to 
the product for functionality inspections. 
f. There is no means for measurement of mechanical properties of inspected 
materials or products, wherein it may be important to measure resistance 
to applied forces, contact pressure, frequency response or vibration 
damping characteristics and internal energy dissipation, without stopping 
the conveyor or reducing throughput speed. 
SUMMARY OF THE INVENTION 
It is therefore an object of the present invention to provide a novel 
universal conveying apparatus for presenting both sides of objects or 
products to reflective and penetrating radiation sensors, in concert with 
other sensor types and particularly retaining grippers and sensors for 
contacting inspected items while measuring their mechanical properties and 
or electrical funtionality, as well as methods and computing hardware, for 
implementing Bayesian type optimal multifeature automatic inspection and 
classification into predetermined grades, while being easily readjustable 
for performing the above on a plurality of different product classes. 
The main tool in automatic inspection is "Computer Vision", whereby a 
digitized video camera image of an item is processed by a computer. 
Computer manipulation of the digitized image provides a means of 
extracting distinguishing features which may serve for categorizing or 
classifying the product. Apart from the visual light spectrum used by 
human inspectors "Computer Vision" may include other types of reflected 
radiation, e.g. Infra Red or Ultra Violet light, or transmitted radiation, 
when hidden attributes of a product are inspected via X-rays, Y-rays, 
Lasers or NMR. To be cost effective, automated inspection must be 
performed at high speed, while all sides of the product are presented to 
the camera and a plurality of other sensors. 
To detect reflected radiation, the machine disclosed hereby provides a 
simple means of viewing both sides of objects i.e. two 180 degree viewing 
angles, while they are consecutively transported on two cup conveyors, 
stacked one on top of the other. An inverter wheel incorporating special 
grippers, transfers the conveyed objects from one cup conveyor to the 
other while inverting same. Suitable openings in the bottoms of the cups 
or rays permit detection of transmitted radiation, through two opposite 
sides of an object. Self emitted radiation from both sides of an object 
can also be detected, which is particularly advantageous in automatic 
radiometric inspection of radioactive ores. 
A further desirable feature of this machine is the ability to transport and 
invert the object, while handling it gently, thereby minimizing potential 
mechanical damage to delicate products such as fruits and vegetables, 
processed food products, or fragile parts and components of manufactured 
products. 
Most manual inspection procedures comprise classification of products based 
on several distinguishing features simultaneously e.g. manufacturing or 
material defects, electrical functionality, blemishes, geometrical 
patterns, colors, dimensions, size, shape, weight, firmness, surface 
roughness etc. To imitate and surpass human inspectors capabilities, the 
machine disclosed hereby, enables multifeature/multisensor inspection 
without reducing the throughput speed or increasing potential product 
damage. 
Apart from visually viewing the product, human inspectors may also pick up 
inspected products while gently squeezing them with their fingers to asses 
firmness, e.g. fruit ripeness, its maturity or decay. Processed food 
products may have to be similarly handled for estimating their rheological 
properties. 
Inspection of manufactured products may also require measurement of 
mechanical properties, such as resistance to applied forces, contact 
pressure, frequency response or vibration damping characteristics and 
internal energy dissipation. The multiple sensing inspection and 
classification machine disclosed hereby affords automatic measurement of 
such mechanical properties "on the go", without stopping the conveyor belt 
or reducing throughput speed. Nevertheless a "stop and go" intermittent 
conveyor motion may be utilized, whenever a mechanical properties 
inspection of a product requires that the actuators remain stationary 
during the test, such as in relatively longer MIL-SPEC's vibration 
response tests. 
In some cases the inspection of a product also requires an operational 
functionality check, wherein a power source is connected to the product, 
while electrical signals emanating from it are indicative of its proper or 
faulty operation. A case in point are electronics printed circuit boards 
and IC chips mounted thereon. The conveying apparatus of the present 
invention also supports implementation of probe connectors, which can be 
automatically attached to selected leads, junctions or to the edge 
connector of the circuit board on-the-go. Thus, while both sides of the 
board are inspected by computer vision, the appropriate tell-tale signals 
may be analyzed in the same time, by the inspection machine's computer to 
assess its functionality. 
Many product classification features may be statistically distributed, 
while high speed automatic inspection requires classification based on 
only one or a limited number of measurements of each feature. To minimize 
classification errors, the present machine includes computing hardware for 
implementing Bayesian type multifeature decision making computer 
algorithms developed by the inventor, which may significantly enhance 
classification accuracy. These may include, on line continuous sampling 
and statistical inference as to the probability densities of the inspected 
and the classified products, while assessing the probabilities of 
classification errors i.e. the accuracy of the machine. These 
classification error probabilities may then be used as feedback data to 
said machine learning algorithm enabling automatic optimal classification 
scale adjustment. To further improve classification accuracy in terms of 
consistency, repeatability, and enhance optimal calibration of the feature 
measuring devices and sensors, the present machine features an array of 
classified products sampling stations. By periodically sampling and manual 
close precise inspection of the classified products, one can verify 
correct sensor calibrations and assess the actual classification errors or 
accuracy of the machine. 
In summary, the most important novel key components of the present multiple 
feature inspection and classification machine are: 
1. Product Inversion Conveying System. Comprising two chain conveyors with 
cups or trays attached thereon, stacked one on top of the other, while a 
special inverter wheel with object grippers is used to gently transfer 
said objects from the end of said top conveyor to the beginning of the 
said bottom conveyor, while simultaneously inverting same. To further 
enhance access to both sides of a product, the bottoms of said cups or 
trays may incorporate differently shaped apertures. The main function of 
this system is to provide a means of viewing both sides of objects while 
they rapidly move in evenly spaced rows, in said cups or trays, in a 
continuous motion or in a stop-and-go intermittent mode. The system 
handles the product gently, thereby minimizing potential mechanical damage 
to delicate products. This affords improved inspection by "Computer 
Vision", utilizing reflected and/or transmitted radiation, as well as self 
radiation emitted by the inspected object, if any. 
Another unique feature of this conveying system is the provision for 
product classification based on several distinguishing features 
simultaneously (including mechanical properties), by enabling multisensor 
inspection without reducing the throughput speed or increasing potential 
product damage. It also enables "Full Sorting" rather than "Reduction 
Sorting" of products whereby each piece is deposited or transported to its 
assigned destination, according to a plurality of predetermined features. 
In addition to the above, this conveying system also provides a means for 
periodically sampling and close precise inspection of the classified 
products, enabling assessment of the actual classification errors or 
accuracy of the machine. These classification errors are then used as 
feedback data to a computer algorithm enabling optimal classification 
scale calibration and automatic adjustment or resetting. 
2. Stepper Motor Driven Revolving Object Retaining Grippers. 
In addition to retaining the product in the cup or tray by a controlled 
force or pressure during the object inversion stage as described above, 
the grippers may be outfitted with electrical probe connectors for product 
functionality analysis. 
Different gripper configurations may be easily implemented to suit a wide 
variety of product classes. Rigid or flexible grippers may be used, 
depending on product type and inspection task at hand. They may 
incorporate different sensors, providing a means for measuring the 
mechanical properties of the inspected products. Depending on the sensor 
type used, resistance to applied forces, contact pressure, frequency 
response or vibration damping characteristics and internal energy 
dissipation may be measured "on the go", without stopping the conveyor or 
reducing throughput speed. Intermittent stop-and-go conveyor motion may 
also be easily implemented, if the inspection task at hand so requires. 
When outfitted with a force load cell, the rigid gripper is most useful for 
measuring quasi-static force-deflection characteristics of the inspected 
product, in order to assess its mean stiffness. When pressure-deflection 
is desired, or when it is required to obtain information about the contact 
surface characteristics and pressure distribution thereon, a tactile 
sensor may be employed in place of the load cell. 
3. Rotating Vibration Actuators and Associated Sensors. 
Electrodynamic and or Piezoelectric vibration actuators may be mounted on 
the periphery of the inverter wheel, for applying vibration excitation to 
the objects, through the apertures in the bottoms of the cups or trays. In 
conjunction with flexible grippers and associated sensors, this permits 
measuring mechanical product properties by dynamic loading, e.g. measuring 
product vibration response characteristics in a wide range of frequencies. 
Low frequency excitation in the subsonic range, is provided by an 
electrodynamic actuator. Input and output acceleration, to and from the 
product is measured by an electronically matched pair of acceleration 
transducers, mounted in the vibration actuator head and in the gripper. A 
flexible finger gripper in the form of a leaf spring may be additionally 
instrumented with strain gauges for measuring the gripping force and 
dynamic response of the vibrationally excited object. The signals from the 
acceleration transducers and other sensors are fed into an electronics 
package mounted on and revolving with the inverter wheel, providing signal 
conditioning and amplification as well as measurement of relative 
displacement, power and energy dissipation in the product. High frequency 
energy in the sonic and ultrasound band may also be applied to the product 
and measured simultaneously or separately by a piezoelectric actuator, 
which may be attached to the end of the electrodynamic vibration actuator. 
4. Automatic Product Sampling Stations for Machine Training Sets, Enabling 
Optimal Classification Scale Calibration and Adjustment. 
A means is provided for automatic sampling and manual close precise 
inspection and feature measurements, of trial sets of classified products 
samples. This data comprises updated machine training sets whereby the 
actual classification errors or accuracy of the machine may be 
automatically computed. These classification errors are then used to 
automatically calibrate or readjust the classification scale settings, 
i.e. update the lookup tables in the decision-making computer algorithm.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
1. Structure and Operation 
The general embodiment of the invention comprising the conveying systems 
layout, of the multiple sensor computerized inspection and classification 
machine are shown in FIG. 1 and FIG. 2. This configuration of the machine, 
is particularly suitable for inspection and classification of fruits and 
vegetables according to size and quality grades, i.e. simultaneous grading 
and sizing. Nevertheless the basic features and principles incorporated in 
this version are readily adaptable to a wide variety of other products as 
well, by appropriate choice of singulating means, cup-gripper 
configuration, sensors and software options. 
In operation a stream of objects, examplified herein by fruits, is 
delivered to the machine by a suitable means such as a conveyor belt (1), 
followed by a singulator grommet roller conveyor (2), which arranges the 
objects into evenly spaced rows. 
From the singulator roller conveyor (2) the objects (4) are delivered into 
specially shaped cups or trays (15) of the upper cup-conveyor (3). For 
non-spheroidal and non-cylindrical objects, which can not roll, another 
type of appropriate singulating mechanism would have to be employed, for 
placing the products into said trays or cups. Depending on the product 
type and inspection task at hand, the bottoms of the cups or trays may or 
may not have variously shaped apertures, as explained in detail later. 
The arrows on the delivery conveyor belt (1), singulator conveyor (2) and 
the upper cup-conveyor (3) show the direction of inspected objects 
movement from right to left. As the objects (4) travel in the cups or 
trays of the upper cup-conveyor (3), they pass by a plurality of 
inspection stations such as (5), (6) and (7), each measuring a different 
product feature. 
In the first group various radiation sources (5), which have the capability 
to penetrate the inspected objects (4), are used in conjunction with 
suitable sensors (6) viewing the objects from beneath, through the 
apertures in the bottoms of said cups or trays (15). The attenuation 
pattern of the radiation transmitted through the objects (4) as detected 
by the sensors (6) is correlated to specific internal product features or 
defects, by a suitable pattern recognition computer program. Examples of 
radiation types which may be employed are X-rays, Y-rays, Lasers etc. If 
an object is not detected in a given cup, a flag is set in the controlling 
computer (block ** in FIG. 12), to signal the rest of the inspection 
stations to ignore it, i.e. to pass it without inspection. 
When self emitting objects, such as radioactive ore pieces are inspected, 
there is no need for a radiation source and both (5) and (6) may comprise 
detecting devices, for classification by the emitted radiation from both 
sides of the ore piece. 
The second group of inspection stations (7), comprise reflected radiation 
sensors e.g. visible light, ultraviolet light, infrared light etc. which 
can be reflected from the upper sides of the objects in the cups (15). 
These may be used for detecting the color of the object, external defects, 
unique geometrical patterns or various blemishes on its surface, 
dimensions, shape, contour, surface roughness, presence and proper 
mounting of components etc. 
The third group of sensors in the product grippers (9) and vibration 
actuators (10), located on the inverter wheels (11), comprise a means for 
measuring the mechanical properties of a product. 
An enlarged and more detailed view of a cup inverter wheel (11) and object 
grippers (9) is shown in FIGS. 3 and 4, while the exploded views in FIGS. 
5, 6, 7, 8, 9, 10 and 13, examplify several gripper-cup configuration 
possibilities and associated sensors. 
Consider first the object inversion operation only, leaving for later, the 
detailed description of the simultaneously executed inspections, while the 
cup or tray hugs the periphery of the inverter wheel (11). 
Referring to FIGS. 1, 2, 3, 4 and the examples of cup-gripper and 
tray-gripper configurations in FIGS. 5 through 10 and 13, it is seen that 
as the conveyor (3) carrying the objects (4) in the cups (15) approaches 
the top of the inverter wheel (11), the gripper arm (26) or (26') driven 
by the stepper motor (25) quickly rotates until the gripper pad (44), 
(44'), (51) or leaf spring (44") contacts the object (4). If the 
object-in-cup absence flag is set for a given cup, the stepper motor (25) 
rotates the gripper arm (26) or (26') to a preset fixed position, 
approximating an average object size. If an object is present in the cup, 
the first contact point between the gripper and the object may be detected 
by five different means i.e. by the flexible gripper pad sensors (46) in 
the gripper pads (44) or pressure transducer (31) as shown in FIGS. 5, 7, 
and 9, or by the strain gauges (46') on the flexible finger gripper as 
shown in FIG. 13, or by one of the tactile sensor's sensitive sites (52) 
as examplified in FIGS. 6 and 8, or by the establishment of electrical 
contact by probes on the gripper pad (44') as examplified in FIG. 10 Once 
the first contact point is detected further movement of the gripper arm 
(26) or (26'), proceeds at a slower rate until the object (4) is retained 
in the cup (15) by a preset maximal force or pressure, which is known to 
be uncapable of inflicting damage to the inspected object. Usually this 
maximal force or pressure is only large enough to retain the object (4) in 
the cups (15) as the inverter wheel rotates the cup from the top to the 
bottom position, approximately 180 degrees. At this point the stepper 
motor (25) is reversed quickly, whereby the gripper (9) gently deposits 
the inverted object (12) into a corresponding cup or tray (15), in the 
lower cup-conveyor (18), as examplified in FIGS. 3 and 4. To assure 
precise cup synchronization for gentle and accurate deposition of the 
object, both the inverter wheel (11) and lower cup-conveyor drive wheel 
(13) are driven by synchro-motors controlled by the stationary slave 
computer, (block ** in FIG. 12). The gripper arm stepper motors (25), on 
the other hand are controlled by smaller slave microcomputers (32) mounted 
on the inverter wheels (11), (one per inverter wheel, see block *** in 
FIG. 12). A subsequent section of this disclosure, contains a detailed 
description of the operation and peripheral hardware implemented by the 
stationary slave microcomputer and the slave microcomputers mounted on the 
inverter wheels, as well as the master microcomputer incorporating the 
operators interface software module. 
The arrow on the lower cup-conveyor drive wheel (13) denotes that the lower 
cup-conveyor (18) moves in the opposite direction of the upper 
cup-conveyor (3). Thus the inverted objects move to the lower reflected 
radiation inspection stations (8), whose construction is identical to 
stations (7), located above the upper cup-conveyor (3), as described 
above. 
This arrangement permits inspection of both sides of variously shaped 
objects precisely, by different computer vision systems. A continuous 
conveyor motion mode is provided, wherein a line-scan camera may be 
employed for inspection of fast moving objects. An alternative 
intermittent stop-and-go conveyor motion mode is also available for 
employing frame-scan cameras, whereby the product is kept stationary 
during the frame grabbing time interval. Computer vision inspection 
employing transmitted radiation types, using line or matrix detector 
arrays may similarly be employed in the two conveyor motion modes. Such 
mode of computer vision inspection is much more efficient than spinning a 
object in front of a line scan camera, for viewing its entire surface, as 
utilized in some produce sorting machines or conventional inspection 
schemes of cylindrical objects, e.g. products in cans, bottles or jars. 
Inspection station pairs (5) and (7) or (7) and (8) may also be combined 
for object inspection by delayed light emission. To this end the objects 
may be irradiated at inspection stations (5) or (7), while the light 
emitted from them after the delay is detected at stations (7) or (8) 
respectively. 
Inspection stations (14) provide a means for weighing the products in the 
cups or trays on-the-go, as may be desirable in produce sorting. They 
comprise load cells, such as strain gauge bridges acting as force 
transducers for measuring the weight of each cup or tray, i.e. their 
output signals are proportional to product mass, while volume or size may 
be computed if the specific gravity and geometrical shape of the product 
is known. Cup or tray weighing may be implemented on the lower conveyors 
(18), before or after the lower reflected radiation inspection stations 
(8), wherein the cups or trays are hinged and incorporate sliding surfaces 
on their under-sides corresponding to the said strain gauge bridges (14). 
Note that both cup-conveyors (3) and (18) may be made long enough to 
accommodate various additional product feature measuring devices or 
sensors, not explicitly mentioned herein. 
After all the desired product features have been measured and the data 
assimilated in the memory of the stationary slave microcomputer (block ** 
in FIG. 12), a suitable program determines the product category and 
executes a command signal for tripping the cup (15) over the appropriate 
side delivery conveyor (17) carrying the classified objects (16). Most 
suitable for this task is a special Bayesian type machine learning 
multiple feature product classification program available from the 
inventor, however any other classification strategy may be employed as 
well. 
Referring to FIGS. 1 and 2, it may be seen that FIG. 1 is a cross section 
through one object conveying lane, while FIG. 2 depicts a four lane 
machine configuration. From the cross sections in FIGS. 3 and 4, it may be 
seen that each pair of inverter wheels (11) are mounted and rigidly 
attached to a common shaft (33), forming an independent self contained 
unit. Although it is possible to build a single lane machine, or machines 
with odd lane numbers, it is recommended that the minimal number of lanes 
per machine should be two, while additional lanes may be added in pairs as 
required. The preferred embodiment examplified in FIG. 4 shows two chain 
wheels on each inverter wheel, whereby each cup or tray lane runs between 
two chains. However more economical configurations are also possible, 
wherein two cup or tray lanes are supported by three or even two chains 
only, while using three or two chain wheels per two lanes respectively, in 
the upper or lower conveyors (3) and (18). 
The object throughput limit per lane, is determined by the linear speed of 
the cup-conveyors (3) and the number of grippers (9) on the inverter wheel 
(11). Another limiting factor may be software execution time per inspected 
object, which tends to increase proportionally with the number 
classification features. Since the data acquisition at the inspection 
stations is performed serially it is not detrimental in limiting 
throughput. 
For fresh produce sorting, eight grippers per wheel as shown in FIG. 3 
seems to be optimal, however more or less grippers per inverter wheel may 
be similarly implemented for other product types if required. 
Commercially available automatic weight sizing machines for fresh produce, 
utilizing cup-conveyors similar to (3), employ throughputs of up to about 
four cups per second. Considering new advances in high speed computing 
hardware and software, similar throughputs may be attained in the multiple 
sensor machine, utilizing an eight gripper inverter wheel (11), as shown 
in FIG. 3. 
For sensor evaluations and calibrations as well as determination of the 
actual classification efficiency, by the various product features, the 
machine disclosed hereby is equipped with classified products sampling 
stations (23), as shown in FIG. 2. 
In the configuration examplified in FIG. 2, the product may be classified 
into 12 categories at most, as determined by the number of side delivery 
conveyors (17) for carrying the classified products to the packing 
stations. More than one side delivery conveyor (17) may be employed for 
any particular grade, if required to accomodate its quantitative 
predominance in the raw material entering the machine on the conveyor belt 
(1). Apart from total machine length, there is no restriction on the 
number of side delivery conveyors (17), which may be used for a given 
product classification task. 
When it is desired to draw a sample of products classified by the machine, 
the drive motor of the side delivery conveyors (17) is stopped momentarily 
and reversed as shown by the bottom arrows line in FIG. 2. This diverts 
the products to the sampling bins (23), rather than in the normal 
direction to the packing stations. In the hypothetical configuration of 
FIG. 2, the first three bins (19) may represent three sizes of grade C, 
the next four bins (20), four sizes of grade B and the last five bins 
(21), five consecutive sizes of grade A fruits. Once a sufficient amount 
of fruits is accumulated in the bins for statistically significant sample 
sizes, the above belt drive motor is reversed again and normal product 
distribution is resumed. 
The sampled products may then be closely examined manually by expert 
inspectors, measuring all the classification features of each product, 
according to a preset scale, while feeding the data into the master 
microcomputer, by a remote console stationed at the sampling bins, (not 
shown in FIG. 2). A computer program, may then be implemented to process 
this data. The output of this proram may be utilized in several ways: 
a. Evaluation of new sensors, as to the feasibility of measuring different 
product features. 
b. Calibration of sensors and product classification scales. 
c. Comparing machine product classification to manual precise 
classification by an expert inspector. 
d. Quality control checks by federal or other agents if required. 
e. Determination of actual feature scale probability densities, as required 
for implementation of Bayesian type decision making algorithms. 
Once the manual product inspection is completed, the products in the 
sampling bins (23) may be released onto the sampled products return 
conveyor belt (22) and returned to the raw material conveyor belt (1) via 
the sampled products return roller conveyor (24). 
In summary note that although the above machine configuration and operating 
cycle was described for sizing and grading fresh produce, only marginal 
modifications are required to adapt it for inspection and classification 
of other items, such as processed food products, avionics components, 
industrial products or small electronics components, integrated circuit 
boards and chips, radioactive ore etc. 
For large products the cup-conveyors (3) and (18) may be made larger while 
the cups (15) may be substituted by suitably shaped large trays to fit the 
contour of the product. Similarly for tiny items, the whole machine and 
cup-conveyors may be miniaturized. Conveying speeds may also be decreased 
or increased, or an intermittent stop-and-go conveying mode may be 
implemented as appropriate to the products and inspection task at hand. In 
all cases the same principles of the double conveyor and inverter wheel 
augmented by suitable product grippers applies. 
2. Object Grippers and Associated Sensors 
The embodiment of the invention permits adapting the grippers and cups or 
trays, to suit inspections of a wide range of product classes. Thus FIGS. 
5, 6 and 13, depict three possible cup-gripper configurations for 
inspecting spherical, spheroidal or horizontally placed cylindrical 
objects. The tray-gripper configurations in FIG. 7 and FIG. 8, may be used 
for inspecting parallelepiped shaped and upright cylindrical objects, or 
thick discs. Similarly the cup-gripper configuration in FIG. 9 depicts 
inspection of an amorphous object, while the tray and semi-flexible 
gripper configuration in FIG. 10 shows a typical arrangement for 
inspecting plate shaped objects, such as printed circuit boards. 
With reference to FIG. 1 note that identically shaped cups or trays (15) in 
both the upper and lower conveyors (3) and (18) may be employed for 
inspecting substantially symmetrical objects such as examplified in FIGS. 
5, 6, 8, 9 and 10. Differently shaped cups or trays are required in the 
upper and lower conveyors, when the shape of the upper side of the product 
is substantially different from its under side, as examplified in FIG. 7. 
Apart from retaining the products in the cups during the inversion process, 
the grippers (9) may simultaneously measure various mechanical properties 
thereof, as well as provide a means for conducting automated electrical 
functionality diagnostics of the inspected product. To this end, the 
gripper pad contacting the inspected object may be rigid or flexible. Also 
the gripper body attached to the gripper arm (26) or (26'), may be 
essentially rigid or flexible. Thus the grippers in FIGS. 5, 7, 9 and 10 
comprise flexible bodies in the form of air-tight rubber bellows boots 
(38), which may be outfitted with flexible gripper pads in the form of a 
diaphragm (44), as in FIGS. 5, 7 and 9, or with rigid gripper pads (44') 
as in FIG. 10. On the other hand, the grippers shown in FIGS. 6 and 8 
comprise a rigid body (51) and thin rubber pad attached thereon. Flexible 
pads, made of resilent materials may be similarly attached to the rigid 
gripper body (51) if required. The rigid gripper body (51) may incorporate 
a load cell for measuring the gripping force, or a tactile sensor pad (52) 
may be employed as shown in FIG. 6. The rigid gripper version in FIGS. 6 
and 8 is most useful when it is desired to obtain accurate quasi-static 
force-deflection characteristics of the inspected product, in order to 
assess its mean stiffness, e.g. firmness or ripeness of fruits, stiffness 
of engine mounts, elastomers etc. The flexible finger gripper in FIG. 13 
utilizes a leaf spring (44") for directly engaging the product (4). 
In all gripper configurations the movement of the gripper arm (26) or (26') 
is constantly measured by an optical shaft encoder which is an integral 
part of the stepper motor (25). In configurations such as in FIG. 6 or 8 
the zero deflection e.g. the initial undeformed size of the product is 
detected by the load cell or tactile sensor when the pad first contacts 
the product. The initial sudden increase in the load cell output also 
signals the stepper motor (5) to reduce the approach speed, while force 
deformation data collection begins and continues until a preset maximal 
gripping force is attained. The removal of the load on the product (4) in 
the cup (15) begins as it reaches the bottom of the inverter wheel (11), 
as signalled by the optical encoder in the synchro motor driving the 
inverter wheel shaft (33) in FIG. 4. Note that the initial reading of the 
optical encoder in the stepper motor (25), may be used to measure the 
vertical dimension, i.e. the thickness of the product (4). In conjunction 
with the horizontal dimensions obtained by optical means at the reflected 
radiation inspection station (5) and the cup weighIng station (14) in FIG. 
1., this enables accurate computation of product volume and its specific 
gravity. In many products specific gravity is an indicator of internal 
quality, especially when it is determined by liquids to solids ratios. 
Using specific gravity as a classification feature for some fresh produce 
cultivars, may enable separation of freeze damaged fruit from sound fruit, 
thick skinned fruit from thin rind fruits, ripe and high juice content 
fruit from immature fruit, dehydrated from moist foods etc. 
For an eight inspection stations inverter wheel, such as (11) in FIG. 3, 
and a speed of 4 cups per second, the entire force application and removal 
cycle lasts about 1 second. For a given throughput speed, longer cycle 
times may be obtained with larger inverter wheels incorporating more than 
eight inspection stations on their periphery. 
When in addition to force-deflection, it is also desired to obtain 
information about the contact surface characteristics and pressure 
distribution thereon, a tactile sensor may be employed in place of the 
load cell. In this case the first contact point between the gripper pad 
and the product is detected by the tactile sensor, which can also measure 
the contact area shape, contour and pressure distribution over it. The 
shape and size of the contact surface between the product (4) and the 
tactile sensor (52) depends on the approach of the gripper arm (26), as 
well as on the size, shape and stiffness of the product (4). For a given 
gripper approach and product size and shape, the contact surface is 
proportional to its firmness or rigidity. Hence the tactile sensor may be 
viewed as a "mechanical thumb", as its operation is similar to pressing a 
thumb to the product and assessing its formness by the indented surface 
contact area of the fingers, while applying a given force or pressure. 
The flexible gripper (9) as depicted in configuration in FIGS. 3 and 4 is 
somewhat less accurate when measuring force-deflection, however it also 
affords measuring mechanical product properties by dynamic loading, e.g. 
measuring its vibration response characteristics simultaneously with force 
deflection. 
The flexible gripper bodies examplified in FIGS. 5, 7 and 9 comprise a base 
plate (37), bolted to a gripper arm (26) by stud bolts (39). A rubber 
bellows boot (38) is bonded to the metal base plate (37), forming an air 
tight seal. The other end of the boot (38) is also sealed airtight by a 
double layer rubber diaphragm (45), bonded to the rubber boot periphery, 
forming a flexible gripper pad (44). A metal retaining ring (47), bonded 
to the inside edge of the rubber boot, and boot ring (56), retain its 
basic shape and active dimensions, even when the gripper (9) is loaded 
excentrically due to irregularly shaped products. The initial degree of 
rigidity of the rubber boot (38) and the gripper pad (44) is controlled by 
a pre-charge air pressure introduced into the air tight boot (38) through 
the inlet pressure valve (35). Sometimes there may be no need for a 
precharge pressure, i.e. the initial pressure in the boot is equal to the 
ambient atmospheric pressure. In any case, a pressure transducer (31) 
constantly monitors the air pressure in the boot (38). This pressure 
varies as the boot (38) is compressed or released, however barring air 
leaks the pre-charge pressure, when the boot is unloaded externally 
remains fairly constant. It follows then that the air pressure in the boot 
(38) as measured by the pressure transducer (31) and applied to the 
gripper pad (44), is also equal to the pressure applied to the gripped 
product (4). 
As the gripper arm (26) and boot (38) approach the product (4) retained in 
the cup (15) the gripper pad (44) is kept approximately flat because the 
pre-charge pressure is only slightly higher than the ambient pressure. 
Initially there is just one contact point between the gripper pad (44) and 
the product (4). At this point the reading of the encoder in the stepper 
motor (25) may be used for measuring the vertical product dimension, 
similarly to the rigid gripper configuration described above. With further 
approach of the gripper arm (26) rotated by the stepper motor (25), using 
the pressure transducer signal (31) for closed loop feedback control, the 
gripper pad (44) begins to flex inwards as it conforms to the shape of the 
gripped product (4). A flexible strain gauge (46) embedded between and 
bonded to the two rubber layers of the gripper pad (44) generates an 
electrical signal, which is proportional to its flexure. The relatively 
weak signal of the strain gauge (46) in the flexible gripper pad (44) is 
transmitted by the leads (40) and the air tight lead connecting plug (36) 
to the conditioning and amplification electronics (54) FIGS. 3 and 4. The 
amplified signal is then transmitted to the slave microcomputer (32) on 
the inverter wheel (11) for further processing by appropriate product 
classification software. 
Instead of a flexible gripper pad (44), as in the flexible grippers of 
FIGS. 5, 7 and 9, most suitable for engaging convex objects, the 
semi-flexible gripper configuration in FIG. 10, employs a rigid gripper 
pad (44'). This pad is better suitable for inspecting plate shaped 
objects, such as printed circuit boards. Male of a non conducting 
material, it incorporates a set of bulges projecting from its surface, 
interspaced with a set of probe connectors, which engage selected sites on 
the printed circuit board (4). The projecting bulges retain the board (4) 
in the tray (15) by engaging blank sites on the board, while the probe 
connectors establish electrical contacts at selected junctions or leads of 
the circuit. Thus, a functionality check may be performed, in conjunction 
with visual inspection. Power supply to the board and the signals from it 
are transmitted via leads (41) and air tight connecting plug (36). These 
signals are sent to the slave microcomputer (32) on the inverter wheel 
(11) for processing by appropriate product classification software. Thus, 
the shape of the pad, probe connectors set and associated pattern of 
bulges, are unique for a specified circuit board. 
The four gripper connecting studs (39) protrude down into the rubber 
bellows boot (38). This limits the contraction of the boot (38) whenever 
the retaining ring (47) contacts the ends of the studs (39). This should 
not happen in normal operation due to the naturally increasing pressure 
within the boot as it contracts. However should a puncture occur, this 
safety feature prevents the boot from collapsing. 
In FIGS. 5, 7, 9 and 13, the product (4) is seen to be supported by the 
vibration actuator head (42), which may be incorporated when it is desired 
to measure the mechanical properties of the product by dynamic loading. 
The vibration actuators (10) may be dismantled if it is not desired to use 
product vibration response as a classification feature. In this case the 
products (4) would rest in the cups or trays (15) directly as shown in 
FIGS. 6, 8 and 10. 
When vibration response is included as a product classification feature, 
the vibration actuator head (42) automatically enters through the opening 
in the cup (15) rising the product (4) slightly off the supporting surface 
of the cup, while the flexible gripper pad (44), or leaf spring (44"), 
begins to apply pressure to it. In this configuration the first contact 
between the flexible gripper and the product is detected by the strain 
gauges (46) or (46'), as soon as the gripper pad (44) begins to deform. 
This triggers the approach speed reduction of the stepper motor (25) and 
beginning of pressure deflection data acquisition, similarly to the rigid 
gripper configuration described above. However since the gripper is not 
rigid, its deformation must be subtracted from the deformation measured by 
the optical encoder of the stepper motor (25). The flexure of the strain 
gauges (46) and (46') is proportional to the deformation of the gripper. 
Thus the signal it emits, while compressing the product, may be used in 
conjunction with the signal from the optical encoder, to quantify product 
deformation. With careful calibration the deformation measurement accuracy 
obtained by this method should be only slightly lower than in the rigid 
gripper configuration. Also rather than force deformation, the signals 
obtained here are mean pressure versus product deformation. For some 
products, e.g. fruits and vegetables, measuring pressure directly is more 
meaningful than force. Nevertheless if the contact area and the pressure 
distribution over it are known, both the mean pressure and the total 
gripping force may be computed, but this takes up additional computer 
time. 
Each of the vibrator assemblies examplified in FIGS. 5, 7 and 9 comprise 
two vibration actuators. The lower vibrator (10), is an electromagnetic 
vibration actuator for generating frequencies in the 10 Hz to 20 KHz 
range, while the upper vibrator (43) is a piezoelectric actuator for 
generating ultrasound frequencies in the range of 20 to 60 KHz. Either one 
of them may be driven separately or they can operate in tandem, while each 
excites a different component of the inspected product. 
The vibration input energy to the product (4) by the vibration actuator 
head (42) is monitored by the lower acceleration transducer (34') while 
the corresponding output acceleration is measured by the upper 
acceleration transducer (34). The signals from both transducers are fed 
through the leads (41) to the conditioning and amplification electronics 
package (53), and then to the relative displacement, power and energy 
dissipation measurement system (55) in FIG. 3. The operation of this 
system is summarized by the block diagram in FIG. 11, as described below. 
Note that the dual vibration actuator configuration is needed only in 
special cases, when the product must be excited by a very wide frequency 
range, e.g. 10 Hz to 60 KHz, which most inspections will not require. 
Thus, in most cases only the lower actuator (10), or the upper actuator 
(43) will be required, wherein the actuator head (42), is bolted directly 
to either one of said actuators, while using an appropriately thicker base 
plate (49). 
The flexible gripper and its sensors in conjunction with the vibration 
actuators, comprise a sophisticated system, whereby most versatile 
measurement of mechanical product properties can be accomplished "on the 
go". These may be carried out while the conveying system moves at a 
constant speed, or during stop intervals, when it intermittently moves and 
stops by a preprogrammed stop-and-go conveyor motion. The modular 
construction enables usage of only part of its features, or all of them 
simultaneously. It is particularly suitable for delicate handling and 
inspection of visco-elastic objects, e.g. most fruits and vegetables, food 
products and industrially manufactured appliances and components. 
The different possibilities of product inspection and classification by 
mechanical properties, utilizing either the rigid or the flexible gripper 
configurations and sensors, may be summarized as follows: 
a. Overall product stiffness via force or pressure versus deformation 
characteristics. Most suitable for measuring visco-elastic and 
visco-plastic properties of products, e.g. fruits, vegetables, food 
products, engine mounts, expanded polymer products or foams etc. In the 
rigid gripper, configurations as in FIGS. 6 and 8, the gripper force is 
measured directly by a load cell in the gripper body (51), while the 
deformation is recorded by an optical encoder in the stepper motor (25) in 
FIG. 4. In flexible gripper configurations in FIGS. 5, 7 and 9 the gripper 
pad pressure is measured directly by a pressure transducer (31), while 
deformation measurement is accomplished by subtracting the strain gauge 
signals from the said encoder signal. 
b. Product indentation characteristics, i.e. "Mechanical Thumb", via 
contact surface contour and its contact area, pressure distribution, 
center of gravity and direction of centroidal axis. Integration of the 
pressure over the contact surface are also permits computation of the 
total gripping force. 
Rigid gripper configurations may use a tactile sensor in place of a force 
load cell. Overall product stiffness may be obtained simultaneously as 
well. 
c. Ultrasound energy transmission for detecting voids, cracks or similar 
internal product discontinuities. May also be used for measuring water or 
moisture content in the product. 
d. Automated vibration inspection, enabling measurement of frequency 
response and internal damping characteristics via dynamic relative 
displacement, power and energy dissipation measurement system, as 
summarized by the block diagram in FIG. 11. These classification features 
are most suitable for inspecting large quantities of industrial, avionic 
or military systems incorporating mechanical and electronic components, 
which must comply with dynamic loading specifications such as ASTM 
standards or MIL-SPEC's. Vibration energy dissipation may also quantify 
mechanical properties of fruits, vegetables and processed foods. 
Once a preset gripper pressure is applied to the product, the vibration 
actuator (10) in FIGS. 3. and 4 may apply any desired vibration profile as 
the product (4) in the cup (15) moves around on the inverter wheel. The 
dwelling time of the vibration test may be very short, say a 0.5-0.75 sec. 
burst for high speed produce sorting, or it may last several minutes when 
a relatively slow frequency sweep is required for automatically generating 
and recording a Bode diagram test record for each product. 
Different vibration profiles may be chosen, via the settings of the 
function generator driving the electromagnetic vibration actuators (10). 
For generating constant input acceleration level frequency sweeps, such as 
needed for recording Bode diagrams, acceleration transducer (31) monitors 
the output, while transducer (31') serves for controlling the input by 
closed loop feedback to the function generator. Dwelling at selected 
narrow band random frequencies or at selected discrete frequencies, 
corresponding to product resonance bands may be similarly accomplished. 
For high speed automatic vibration inspection, e.g. fruits and vegetables 
or processed food products etc. a white or pink noise driving signal may 
be used. Short vibration bursts followed by high speed FFT processing, 
affords identification of response peaks and low frequency response bands 
corresponding to distinct product properties, which may serve as 
classification features. 
Regardless of the vibration profile employed, additional information may be 
extracted from the acceleration transducer signals (34) and (34'), by the 
relative displacement power and energy dissipation measurement system 
summarized by the block diagram in FIG. 11. After suitable amplification 
and low pass filtering each signal is passed through a dynamic 
compensation network, which matches the gain and phase characteristics of 
the two acceleration transducers, and extends their usable frequency band. 
This affords precise derivation of the relative acceleration between the 
top and the bottom of the product. The dynamic compensation network is 
necessary since no two acceleration transducers of the same make are 
exactly alike. Without compensation, part of the relative acceleration 
signal may be due to the difference between the transducers rather than 
between the input and output accelerations. After the compensation, two 
real time consecutive integrations of the relative acceleration yield the 
relative velocity and displacement respectively. Then the product of the 
output acceleration by the relative velocity and its integral give the 
power and energy dissipated in the product, due to the input vibration 
burst. This energy is a direct measure of the internal damping properties 
of the product, which may be used as a product classification feature. 
Apart from the energy dissipation, the three additional signals thus 
created, i.e. the relative acceleration, velocity and displacement are 
more effective for identifying tell tale low and high response 
frequencies, than the usual output to input acceleration ratio employed in 
conventional vibration testing. Firstly because the relative acceleration 
derived from the compensation network is by far more precise in 
quantifying the frequency response of the product. Secondly a greater 
dynamic range is afforded for a given frequency band. To see this observe 
that at the low frequency end, relatively large amplitudes may be present 
in the spectrum while the associated accelerations may be very low. 
Conversely at the high frequency end of the spectrum large acceleration 
peaks may correspond to negligibly small amplitudes. Similarly in the 
mid-range of the spectrum, velocity peaks dominate both the corresponding 
accelerations and displacement amplitudes. Since this classification 
feature hinges on spectral differences of product categories, it is more 
useful to use the relative displacement signal for FFT processing, when 
the differences between the spectra are most predominant in the low 
frequency range. Similarly the differential velocity or acceleration 
should be used when the spectral differences are predominantly in the mid 
and high end of the spectrum respectively. 
In order to avoid the need for switching between the different spectra, a 
composite signal is derived by summing the three signals together prior to 
FFT processing, as depicted on the right side of FIG. 11. The 
significantly larger dynamic range of the composite spectrum thus derived, 
in comparison the spectra of the three components, affords superior 
product classifications by spectral differentials in the entire frequency 
band. 
It should be noted that the modular structure of the mechanical properties 
inspection stations as described above, permit simultaneous classification 
by product stiffness, ultrasound energy transmission, vibration response 
spectral differentials and differences in energy dissipation i.e. 
vibration damping characteristics. Any one of these may be disregarded or 
switched off whenever inappropriate to a particular product classification 
task. 
3. Computing Hardware Sensors and Actuators 
The computing hardware, sensors and actuators and associated software, are 
summarized by the block diagram in FIG. 12. The flow of information 
between the different units is shown by the arrows. This hardware 
comprises three interconnected microcomputer systems and associated 
peripherals, marked in the block diagram of FIG. 12 by one, two and three 
asterisks respectively. 
The master microcomputer (*) serves three main purposes. Firstly it 
comprises the operator's interface for machine parameter settings, long 
term statistical data acquisition and storage on disc, as well as book 
keeping and printing hard copy reports if required. Its second main task 
is receiving data samples from stationary slave microcomputer (**), and 
sending back updated lookup tables of optimal product classification 
feature scales. These periodically obtained data samples represent the 
most recent "raw material composition" e.g. mean grade proportions in the 
inflowing stream of objects, and the associated most recent classification 
decision profile. The purpose of this process is to minimize product 
classification errors, under variable raw material composition, while 
executing a given product classification policy, as may be implemented by 
a special algorithm developed by the inventor but not described herein. 
In addition to the above two main tasks, the master microcomputer may also 
be used for running various off line programs. One example of these may be 
a management decision aid program, comprising an expert system for guiding 
the operator in choosing optimal product classification policies, in a 
given market environment. Another such program may be run in conjunction 
with the classified products sampling stations. Using data keyed in by 
expert inspectors, as they reclassify the sample products, this program 
enables precise sensor calibrations and checkups of actual classification 
efficiency of the machine. 
The stationary slave microcomputer (**) controls the cup drop triggers, 
sending the classified products onto the appropriate side delivery 
conveyors (17) in FIGS. 1 and 2, while running the main product 
classification program, which is unique for each inspection task. It is 
also responsible for data acquisition and processing, from the sensors of 
the stationary product inspection stations, (5), (6), (7), (14) and (8) in 
FIG. 1. Additional data is received from block (***), i.e. from the 
revolving slave microcomputer (32), via the slip ring assembly (27) in 
FIG. 4. This data is derived from the sensors (31), (34), (34'), (46), 
(52), 44' as examplified in FIGS. 6 and 10 and optical encoders in the 
stepper motors (25) in FIG. 4. 
After processing the sensor data, the product classification program 
compares it to the classification scale lookup tables, whereby a decision 
is made as to the category of the inspected product. As the products are 
inspected and classified in turn, raw material composition and decision 
profile data may be continuously accumulated and periodically sent to the 
master microcomputer, while newly computed updated lookup tables are 
received from it. 
Each inverter wheel (11) in FIG. 3 incorporates one revolving slave 
microcomputer (32), marked by block (***) in FIG. 12. Thus a four lane 
machine, as depicted in FIG. 2 requires one master microcomputer (*), one 
stationary slave microcomputer (**) and four revolving slave 
microcomputers (***) on the inverter wheels. This configuration allows 
parallel processing wherein the slave microcomputers are dedicated for 
data acquisition and real time product inspection and classification only, 
while the master microcomputer (*) is saddled with most of the number 
crunching tasks off line. 
In addition to data acquisition from the revolving inspection stations 
sensors, the said slave microcomputers (32), also control the gripper 
stepper motors, i.e. the motion of the gripper arms (26) in FIG. 4. They 
are also responsible for activating and deactivating the vibration 
actuators (10) and (43) in FIGS. 5, 7 and 9. If an electrical 
functionality check is implemented on the inspected products, they must 
also run an appropriate diagnostics program. 
The slip ring assemblies (27) in FIG. 4, comprise the communication links 
between the revolving slave microcomputers (***) and the stationary slave 
microcomputer (**), which in turn communicates with the master 
microcomputer. The said slip ring assemblies (27) are also used for 
transferring power to the revolving slave microcomputers (32), the signal 
conditioning and amplification electronics (53) and (54), the relative 
displacement power and energy dissipation measurement system (55) in FIG. 
3 and the vibration actuators (43) and (10) in FIGS. 5, 7 and 9. This 
affords cool operation and compact light weight construction of the 
revolving microcomputers and electronics, since heat generating and heavy 
transformers or bulky AC to DC converters are not carried on the inverter 
wheels. Also, only one function generator and power amplifier is required 
for driving all the vibration actuators (43) and (10) in FIGS. 5, 7 and 9 
while switching control logic is software implemented by the autonomous 
slave microcomputers (32) mounted on each inverter wheel, as shown in 
FIGS. 3 and 4. 
All computing hardware and peripherals may be implemented by standard stock 
items, readily available from different vendors at competitive prices. 
According to the computation functions summarized in the block diagram in 
FIG. 12, the following guidelines are given as regards computer 
suitability: 
The functions of the master microcomputer (*) may be implemented by a low 
cost personal microcomputer, incorporating a mouse user interface, 
multi-tasking processing, fast color graphics and standard digital I/O. 
For a two lane, relatively slow speed inspection machine the functions of 
the stationary slave microcomputer (**) may be performed by a high end 
personal microcomputer, via multi-channel digital I/O, A/D, D/A, frame 
grabbing boards, extra memory buffers and accompanying software. To 
perform these functions in a four lane or larger machine, operating at 
high speed, a more powerful industrial microcomputer would be required. 
Currently available machines are capable of high speed 512.times.512 or 
1024.times.1024 pixels image acquisition at 8 bit resolution and 
processing in essentially real time. 
The revolving slave microcomputers (***) mounted on the inverter wheels, 
are essentially single board microcomputers incorporating digital and 
analog I/O. 
In FIGS. 3 and 4 they are schematically depicted as STD BUS cages (32), 
including both the microcomputer and I/O boards. Such low cost compact 
units are manufactured by many different companies, targeted for various 
industrial control applications. Recent developments in "On a chip" 
microcomputers may enable implementation of these functions in yet a more 
compact and very low cost package. 
4. Optimal Product Classification Software 
It is tacitly assumed that most of the features X, of the products to be 
inspected and used for classification by the present invention, can be 
measured by different sensors and quantified by suitable feature scales 
YX, where Y expresses the degree or "strength" of each feature X. Examples 
of such scales may be product weight, dimensions, firmness, color, 
internal and external defects and blemishes etc. Or in case of 
functionality checks, a binary 0 or 1 index may be used, to quantify 
numerically the result of the inspection. The scales of some features, 
such as weight or dimensions are self evident, while the scales of other 
features must be predetermined by an expert inspector, to correlate the 
sensor reading to the feature strength, as provided for by the automatic 
product sampling means i.e. items (17) and (19) through (24) in FIG. 2. 
Interpretation of digitized images acquired form the reflected and or 
transmitted radiation sensors, may be performed by well known digital 
image processing techniques. To this end, software packages for "Pattern 
recognition" and for "Image understanding" are commercially available. 
In some cases the sensor readings uniquely classify the feature with 
absolute certainty, especially when the feature scale distribution may be 
considered to be binary, i.e. the feature is either detected or not. 
Inspection of printed circuits for lead continuity is a good example of 
such a feature. In this case, once a lead break is detected the decision 
making process for product classification is trivial. 
In most cases the feature scale within a product category follows a 
continuous distribution, and can usually be approximated by the normal 
(Gaussian) distribution. Bayesian type algorithms may be implemented to 
address this more difficult classification problem, i.e. when the sensor 
readings and associated interpretation software, can provide only a 
classification probability, rather than uniquely classify the product. 
Consider for example the classification process of sorting lemons into a 
yellow and green category. Here the amount of chlorophyl in the rind 
indicates the degree of "greenness" while its absence determines the 
degree of "yellowness". Using a pair of sensors for measuring this feature 
scale, i.e. yellow to green light reflectance ratio, will leave some 
"slightly yellow" in the green category, while "slightly green" lemons 
will be classified as yellow category. A classification policy must be 
adopted whereby a "scale separation line" divides the two lemon 
categories, according to some industry standard or policy based on 
marketing considerations. Clearly in this case the feature scale is 
continuously distributed within the product categories. In statistical 
terms we may say that there is an overlap between the probability density 
curves of the feature scales of these two categories, quantifying the 
probability of misclassifications about a predetermined separation line. 
It may be shown that the extent of this overlap depends on the average 
yellow/green ratio of lemons in the raw material as well as on the feature 
scale probability density curves within the categories. If, as in this 
example, the composition of the raw material is not constant, an adaptive 
decision algorithm is required to minimize product misclassifications at 
all times. To this end, a software package, which is not a part of the 
present invention, may be obtained from the inventor. This software 
package, comprises a general machine learning, optimal product 
classification algorithm which is readily adaptable, for various 
computerized inspection tasks, that can be implemented by the machine 
disclosed herein. In conjunction with the hardware, this algorithm 
provides a means of continuous statistical sampling of the raw material as 
well as each classification scale distribution. Information from on line 
statistical analysis of these samples, is used for automatic readjustment 
of classification scales for minimal probability of product 
misclassifications. Basically this means that the machine constantly 
checks the composition of the raw material inflow, while analyzing its 
previous decisions pattern in terms of classification errors it made, 
wherewith it automatically readjusts its sorting strategy to improve 
classification accuracy. The data derived in the sampling process may also 
be used for computing a set of weighted mean classification efficiency 
indexes, for each product classification feature. These indexes quantify 
the accuracy of the machine, i.e. they provide assessment of the machine's 
performance with respect to the said optimal product classification 
policy, or prevailing industry standards. 
As the structure of these programs will vary from product to product, and 
since they are not an integral part of the present invention, their 
structure will not be described here in detail.