Classifications

• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
  • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
  • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
  • B07C5/04—Sorting according to size
  • B07C5/12—Sorting according to size characterised by the application to particular articles, not otherwise provided for
  • B07C5/14—Sorting timber or logs, e.g. tree trunks, beams, planks or the like
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
  • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
  • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
  • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
  • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
  • B07C5/34—Sorting according to other particular properties
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
  • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
  • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
  • B07C5/36—Sorting apparatus characterised by the means used for distribution
  • B07C5/38—Collecting or arranging articles in groups

Definitions

• the invention relates to sorting different kinds of piece goods, such as boards, logs and stone- ware, in an industrial production process. Specifically, the invention relates to sorting the goods into different quality classes according to various properties associated to each good.
• the division into quality classes can be first of all performed on a sensory basis.
• the specific determined individual properties are not used as the quality criteria so much as the general visual impression formed by the evaluator about the object.
• the sensory classification requires a sufficient number of human resources continuously tied to the process, which produces ex- traditionss.
• the outcome of the classification inevitably depends at least to some extent on the evaluator, which produces deviation in the final classification.
• the sensory evaluation may also be too slow a process. Sensory evaluation can be replaced with measurements. Modern, for example optical, measuring systems are able to quickly gather a great number, even dozens or hundreds, of units of measurement data about the properties of objects passing on the production line without stopping the object.
• each object By comparing the measurement data to a rule base precreated on the base of test measurements, each object can be quickly classified into a specific quality class, and the means for sorting the objects can be controlled accordingly. Measurement and classification of the objects and their sorting according to the classes can be arranged as a computer-controlled automatic process which, besides increasing the speed and accuracy, saves human resources.
• the rule base may comprise limit values which are specific for each measured property, and the result must fit within these limits. For example in the case of boards, presence in a specific class may require that the board is not allowed to have more than 3 healthy branches for the length of one metre, and that the entire board does not comprise any branches with a diameter of more than 20mm. Different properties may also be stressed with different coefficients, so that a larger deviation can be allowed for one property, if another and a more signifi- cant one is correspondingly of a specifically good quality.
• the higher quality classes may comprise some absolute quality criteria.
• a rule-based system is inflexible when making changes to the classifications.
• Creating an entirely new quality class typically requires that all classification rules are reset.
• a quick fine adjustment between classes would be desirable because of the starting material, conditions or market factors.
• expanding the best qual- ity class slightly so that a higher price could be set for an ever larger share of the products requires heavy measures in order to be able to reset the rules.
• the use of the known classification systems requires good knowledge of the numerical data included in the measurement results, and therefore often necessitates a lengthy education and know-how acquired by experience.
• the objective of the invention is to provide a method for sorting natural piece goods, wherein the classification principles can be created easily and quickly compared to the prior art, and the classification system is also flexible to changes in the classi- fication.
• the invention is characterized by what has been presented in claim 1.
• the method of the invention for sorting natural objects into different quality classes based on their properties comprises defining, for each object to be sorted, a parametre set comprising several pa- rametres which represent the properties of the object, and determining the quality class of the object on the base of the parametre set.
• the term "natural” signifies that the objects to be sorted are made from material which is acquired from the nature, such as for example wood or stone. These objects typically have a vast variety of properties which affect the quality of the end-product and which may exhibit a relatively large deviation. Determining the classification principles with the traditional methods is in this case extremely challenging.
• Said parametres can be defined using any measuring method, including for example different optical measurements conducted without contacting the object, and other remote measurements. Some of the parametres can also be defined on a sensory basis.
• the parametres may represent the appearance of the object and its internal and material properties, for example the dimensions, shape or density of the object.
• the advantages of the invention compared to the solutions of the prior art become evident when there are several parametres, for example at least three.
• the maximum number of the parametres that may be included in a parametre set is not limited in the invention; instead, there may be any desired number of them, for example several dozens.
• the method comprises dividing the parametre sets of a number of objects into groups of similar parametre sets, selecting at least one group to each of the quality classes, and then assigning groups that have not yet been classified to the quality classes based on their similarity to the groups that have already been classified, until all groups are classified, so that the quality class of an individual object is determined according to the quality class of the group which represents the parametre set of that object.
• mathematical distances for example Euclidean distances, between the multi-dimensional parametre sets are used in the method as the measure of similarity between the parametre sets and the groups .
• the method of the invention provides valuable advantages in creating a classifica- tion system. Processing the objects in groups of similar objects makes creating the classification simpler more effective. When at least one group has been, on some basis, assigned to a specific class, the groups that are the most similar to the first group can be assigned to the same class up to the desired quantity, or until another class that has already been formed becomes more suitable. In other words, the entire sorting process merely requires that only one group is selected for each quality class, so that the classifi- cation can then be completed solely according to the similarities between the groups, without taking any standpoint as to the values of the parametres representing individual objects.
• the method in accordance with the invention provides a classification based on the similarity between the objects, specifically evaluated as a whole, which is quite a challenging objective for example in the sensory evaluation, and becomes all the more difficult as the number of parametres taken into account in the sorting process increases.
• each class comprises several groups, they have precise boundaries.
• a quality classification based on the groups and similarities is extremely flexible when making changes in it.
• the division of the groups into quality classes is changed, if necessary, by moving the groups which define the boundaries of the quality classes to adjacent quality classes. In this manner, assigning the mutually similar groups into the same quality class can be continued as a continuous control, per- formed when necessary, even after the initial classification.
• a new quality class is added to the classification whenever needed by selecting a group to the new quality class and assigning groups that are similar to the first group to the new quality class. To establish a class, it is therefore sufficient to first select one group that represents the new class, and the class can then be expanded as desired by adding groups that are close to the first group .
• the parametre sets are arranged for grouping and as- signing to quality classes into a one-, two- or multidimensional map, on which the distance between two parametre sets depends on the original mathematical distance of the parametre sets.
• the map is so formed that it retains the original distance re- lations of the parametre sets as accurately as possible. Retaining the distance relations in a completely accurate manner is naturally not possible when moving into a space of smaller dimensions.
• grouping and quality classification can be performed manually based on the distance relations on the map.
• Conversion into a two-dimensional presentation may be performed for example using the principle of a self-organising map, known per se.
• one map unit may in this case represent the parametre set of an individual object, or a group of similar parametre sets.
• Adjacent map units on the map are close to each other also in the original multi-dimensional space in which the parametre sets reside, i.e. they relate to mutually similar objects as a whole.
• Grouping can also be based for example on the so-called K-means method or on some other known grouping method.
• grouping and assigning the groups into quality classes can also be performed automatically by a computer, so that the visualization into a map is not required.
• the groups are divided into quality classes according to the desired share of each quality class in the production. In this manner, it is easy to provide the specific proportions of the quality classes in the entire production.
• the selection of a group to a quality class is preferably based on the sensory evaluation of a sample object which represents this group.
• the sensory evaluation it is possible to select such exemplary object to a specific class which bears the desired general impression.
• Assigning the objects which are the most similar in their general impression to the same class can then be easily performed by assign- ing to this specific class groups that are close to each other in their mathematical distance.
• a preferred embodiment of the invention comprises sorting of timber, for example boards.
• Evalua- tion of the quality of timber, for example sawn timber in particular is typically based on evaluating a vast number of properties. Some of these properties, such as the objects' dimensions, derive from operations performed on timber. On the other hand, these types of natural goods have a vast variety of different properties which derive from the growth conditions and typically exhibit a large deviation. As a result, the final number of the quality classes is typically extensive. In such case of multi-dimensional parametre sets, the advantages of the invention in terms of the simplicity of creating the classification and adjusting it become evident.
• the invention is naturally suitable for other sawn and/or planed sawn timber and also for logs that have not yet been processed.
• stoneware for example slabs of natural stone.
• stoneware also comprises properties which are characteristic of the raw material itself, and sorting according to these properties would be inconvenient and time- consuming with the traditional methods .
• FIG. 1 represents an arrangement used in one embodiment of the method in accordance with the invention
• Fig. 2 illustrates one embodiment of the method in accordance with the invention
• Fig. 3 represents one embodiment of the method in accordance with the invention as a flow chart.
• Fig. 1 presents an object 1 to be sorted which may be for example timber or stoneware.
• the fig- ure also presents part of a measuring system comprising a computer 2 and measuring devices 3a, 3b connected to the computer.
• the measuring devices are used for defining, for example optically, by means of ultrasound or by some other method which preferably does not involve contact with the object, different properties p of the object on the production line, and for combining these into a parametre set s (p a ,Pbr—rPi) which represents the properties of said object and is then used for the quality classification of the ob- ject.
• FIG. 2 presents a two-dimensional map 4 which comprises map units 5.
• Each map unit represents one group g (s ⁇ r s 2 ,. ⁇ , S k ) of parametre sets of similar objects.
• the two-dimensional map is created so that the original mutual mathematical distances between the pa- rametre sets and the respective groups are retained. In other words, proximity on the map 4 equals proximity also in the original i-dimensional parametre space.
• groups g f have been divided into four different quality classes C. Over the centre area of the map there are some groups that have not yet been classified. These can be moved into the desired quality classes by applying the proximity, i.e. similarity principle, so that at the end, each group will belong to some quality class. Boundaries of the quality classes can also be shifted later by changing the classes of the groups that reside on the border areas of the classes. The operation of creating the classification and changing it is illustrated in Fig. 2 by arrows between the groups and between the classes. Presence of a specific group in a specific class can be based for example on selecting the quality class by evaluating on the sensory basis an object which represents that specific group. Other similar groups can then be classified based on the proximity data of the map. Fig.
• Map 4 of Fig. 2 may be based on the entire set of objects that are to be sorted, comprising, for example, a specific production lot.
• all objects to be sorted can be first measured, then the measured parametre sets can be grouped and divided into quality classes, and finally the objects can be physically separated from each other according to their groups and the classes of these groups.
• a more preferred alternative in many embodiments is to first sort the objects into physically separate groups of similar objects on the base of the measurements, and to then assign each of the groups to a quality class.
• the quality classification of the groups can naturally be later changed, over and over if so required.
• This mode of implementation necessi- tates only one step of physical separation of the objects, which makes the sorting process extremely efficient and flexible.
• the sorting can also be based, in accordance with Fig. 3, on a specific, smaller sample lot which represents the entire set of objects that are to be sorted.
• the flow chart of Fig. 3 is an example of an embodiment of the invention in the timber industry.
• the desired parameter sets are first measured from the boards of the sample lot and grouped into groups based on their similarity. At least one representative for each quality class is then selected from the boards of the sample lot, for example by means of sensory evaluation, and the group of the pa- rametre set of that representative is assigned to the respective quality class.
• the quality classes of the groups that are yet to be classified are determined based on the proximity of the groups.
• the classification of the sample lot is complete, it is used for sorting the boards that were initially intended for sorting. In other words, for each board to be sorted, a parametre set which represents its properties is measured, and the location of this set is compared to the groups of the parametre sets of the sample lot.
• the quality class of the board is selected according to the quality class of that group in the sample lot which represents the board in the closest manner.
• Classification that has once been created based on the sample lot is later adjusted, where necessary, by moving groups from one class into another or by establishing new classes.
• the need for change may be caused for example by a reclamation issued by a customer concerning boards in a high-rated quality class.
• the method of the invention is not limited to the examples referred to above; instead, many variations are possible within the scope of the claims.
• the piece goods may be any natural, more or less reprocessed goods comprising various factors which contribute to the quality classification.

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Citations (2)

• 2007
  • 2007-02-01 FI FI20070085A patent/FI20070085L/fi not_active Application Discontinuation
• 2008
  • 2008-01-31 WO PCT/FI2008/050037 patent/WO2008093001A1/en active Application Filing

Patent Citations (2)

Non-Patent Citations (11)

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