DEVICE FOR OPTICAL INSPECTION OF PARISONS

Abstract

A device (1) for optical inspection of parisons (2) comprises: an illuminator (3) configured to emit a beam of light directed at a parison (2) located at an inspection position (10); a detector (4) configured to capture an image (10) of the parison (2) interposed between the illuminator (3) and the detector (4), where the illuminator (3) includes an emission-polarizing filter (32) configured to generate a polarized light beam, and where the detector (4) includes a receiving polarizing filter (41) configured to receive the polarized light beam.

Claims

1-35. (canceled)

36. A device for optical inspection of parisons, comprising: an illuminator including a light source configured to emit a beam of light directed towards a parison located at an inspection position; a detector including a camera configured to capture an image of the parison located at the inspection position, wherein the parison, at the inspection position, is operatively interposed between the illuminator and the detector, wherein the illuminator includes an emission-polarizing filter configured to intercept the beam of light emitted by the light source and to generate a polarized light beam, and in that the detector includes a receiving polarizing filter configured to receive the polarized light beam, so that the parison, at the inspection position, is operatively interposed between the emission-polarizing filter and the receiving polarizing filter.

37. The device according to claim 36, further comprising a processing unit including: a memory including reference data sets; a processor programmed to process the image captured by the detector based on the reference data sets, in order to derive a diagnostic indication regarding a defectiveness of the parison.

38. The device according to claim 37, wherein the processing unit is configured to: process the image captured by the detector based on the reference data sets, in order to derive from the image values of a plurality of image features; process the values of the plurality of image features to derive the diagnostic indication regarding the defectiveness of the parison.

39. The device according to claim 38, wherein the processing unit is configured to: generate an image reconstructed from the values of the plurality of image features and based on the reference data sets; deriving the diagnostic indication regarding the defectiveness of the parison as a function of comparing the image captured by the detector with the reconstructed image.

40. The device according to claim 39, comprising a self-learning system configured to: receive as input a plurality of images captured by the detector for a corresponding plurality of parisons; process each image of the plurality of images captured by the detector based on the reference data sets, in order to derive for each image corresponding values for the plurality of image features based on a predetermined criterion; generate for each image of the plurality of images a corresponding reconstructed image, based on the reference data sets, using the corresponding derived values for the plurality of image features; compare each image of the plurality of images captured by the detector with the corresponding reconstructed image in order to derive, for each image of the plurality of images, a corresponding similitude parameter representing a similarity between the image captured by the detector and the corresponding reconstructed image; for each image of the plurality of images, update the reference data sets as a function of the similitude parameter and of a predetermined threshold value for the similitude parameter.

41. The device according to claim 40, wherein the self-learning system is configured to update the plurality of image features as a function of the similitude parameter and of the predetermined threshold value.

42. The device according to claim 40, wherein the predetermined criterion includes a maximum number of image features for the plurality of image features.

43. The device according to claim 40, wherein the self-learning system includes convolutional neural networks.

44. The device according to claim 36, wherein the emission-polarizing filter is a linear filter, configured to polarize the light in a first polarizing direction.

45. The device according to claim 36, wherein the receiving-polarizing filter is a linear filter, configured to polarize the light in a second polarizing direction.

46. A line for making containers of thermoplastic material, comprising: either one of i) a moulding machine configured to make parisons, or ii) a blow-moulding machine configured to receive the parisons and to blow-mould them in moulds to make the containers, the line further comprising a device for optical inspection of parisons, the device including an illuminator including a light source configured to emit a beam of light directed towards a parison located at an inspection position; a detector including a camera configured to capture an image of the parison located at the inspection position, wherein the parison, at the inspection position, is operatively interposed between the illuminator and the detector, wherein the illuminator includes an emission-polarizing filter configured to intercept the beam of light emitted by the light source and to generate a polarized light beam, and in that the detector includes a receiving polarizing filter configured to receive the polarized light beam, so that the parison, at the inspection position, is operatively interposed between the emission-polarizing filter and the receiving polarizing filter, and wherein the optical inspection device is operatively located either i) downstream of the moulding machine, or ii) upstream of the blow-moulding machine.

47. A method for optical inspection of parisons, comprising the following steps: emitting a beam of light directed towards a parison located at an inspection position, by means of an illuminator including a light source; capturing, with a detector including a camera, an image of the parison located at the inspection position, wherein the parison, at the inspection position, is operatively interposed between the illuminator and the detector; generating a polarized light beam by intercepting the beam of light emitted by the illuminator on an emission-polarizing filter interposed between the light source and the parison; receiving the beam of polarized light on a receiving polarizing filter, interposed between the parison and the camera; wherein, at the inspection position, the parison is operatively interposed between the emission-polarizing filter and the receiving polarizing filter.

48. The method according to claim 47, comprising a step of processing the image, wherein the step of processing includes the following sub-steps: processing the image captured by the detector based on the reference data sets, in order to derive from the image values of a plurality of image features; generating an image reconstructed from the values of the plurality of image features and based on the reference data sets; deriving a diagnostic indication regarding a defectiveness of the parison as a function of comparing the image captured by the detector with the reconstructed image.

49. The method according to claim 48, comprising a step of self-learning comprising the following sub-steps: capturing a plurality of images for a corresponding plurality of parisons; processing each image of the plurality of images based on the reference data sets, in order to derive from each image of the plurality of images corresponding values of a plurality of image features as a function of a predetermined criterion; generating for each image of the plurality of images a corresponding reconstructed image using the corresponding values of the plurality of image features and based on the reference data sets; comparing each image of the plurality of images with the corresponding reconstructed image and deriving a corresponding similitude parameter representing a similarity between the image captured by the detector and the corresponding reconstructed image; updating the reference data sets and the plurality of image features as a function of the similitude parameter and of a predetermined threshold value.

50. The method according to claim 49, wherein the images of the plurality of images captured by the camera during the step of self-learning are representative of a corresponding plurality of defect-free parisons.

51. The method according to claim 49, comprising a step of feeding the parisons of the plurality of parisons to the inspection position one at a time, and according to a predetermined orientation relative to the emission-polarizing filter and relative to the receiving polarizing filter.

52. The method according to claim 47, wherein the emission-polarizing filer is a linear polarizing filter, configured to polarize the light in a first polarizing direction.

53. The method according to claim 52, wherein the parison, at the inspection position, is oriented with a respective axis parallel to the first polarizing direction.

54. The method according to claim 52, wherein the receiving polarizing filter is a linear polarizing filter, configured to polarize the light in a second polarizing direction, different from the first polarizing direction.

55. A method for processing an image of a parison, captured by a detector, the method comprising the following steps: processing the image captured by the detector based on the reference data sets, in order to derive from the image values of a plurality of image features; generating an image reconstructed from the values of the plurality of image features and based on the reference data sets; deriving a diagnostic indication regarding the defectiveness of the parison as a function of comparing the image captured by the camera with the reconstructed image.

56. The method for processing an image of a parison according to claim 55, comprising a step of self-learning which comprises the following sub-steps: capturing a plurality of images for a corresponding plurality of parisons; processing each image of the plurality of images based on the reference data sets, in order to derive from each image of the plurality of images corresponding values of a plurality of image features on the basis of a predetermined criterion; generating for each image of the plurality of images a corresponding reconstructed image using the corresponding values of the plurality of image features and based on the reference data sets; comparing each image of the plurality of images with the corresponding reconstructed image and deriving a corresponding similitude parameter representing a similarity between the image captured by the detector and the corresponding reconstructed image; updating the reference data sets and the plurality of image features as a function of the similitude parameter and of a predetermined threshold value.

57. A method for processing an image of an object made of plastic material, the image being captured by a detector, the method comprising the following steps: processing the image captured by the detector based on reference data sets, to derive from the image values of a plurality of image features; generating an image reconstructed from the values of the plurality of image features and based on the reference data sets; deriving a diagnostic indication regarding a defectiveness of the object, as a function of comparing the image captured by the detector with the reconstructed image.

58. The method according to claim 57, comprising a step of self-learning which comprises the following sub-steps: capturing a plurality of images for a corresponding plurality of objects; processing each image of the plurality of images based on the reference data sets, to derive from each image of the plurality of images corresponding values of a plurality of image features on the basis of a predetermined criterion; generating, for each image of the plurality of images, a corresponding reconstructed image using the corresponding values of the plurality of image features and based on the reference data sets; comparing each image of the plurality of images with the corresponding reconstructed image and deriving a corresponding similitude parameter representing a similarity between the image captured by the detector and the corresponding reconstructed image; updating the reference data sets and the plurality of image features as a function of the similitude parameter and of a predetermined threshold value.

59. The method according to claim 58, wherein, in the step of self-learning, also the plurality of image features are updated as a function of the similitude parameter and of the predetermined threshold value.

60. The method according to claim 58, wherein the predetermined criterion includes a maximum number of image features for the plurality of image features.

61. The method according to claim 58, wherein the self-learning step includes using convolutional neural networks.

62. The method according to claim 58, wherein the images of the plurality of images captured by the detector during the step of self-learning are representative of a corresponding plurality of defect-free objects.

63. The method according to claim 57, wherein the objects are preforms or parisons.

64. A device for optical inspection of objects made pf plastic material, comprising: an illuminator including a light source configured to emit a beam of light directed towards a parison located at an inspection position; a detector including a camera configured to capture an image of the object located at the inspection position, wherein the object, at the inspection position, is operatively interposed between the illuminator and the detector, a processing unit including a memory, containing reference data, and a processor, programmed to process the image captured by the detector, based on the reference data, to derive from the captured image values of a plurality of image features, and to process the values of the plurality of image features, to derive a diagnostic information regarding a defectiveness of the object, wherein the processing unit is configured for generating an image reconstructed, from the values of the plurality of image features and based on the reference data sets, and for deriving the diagnostic indication regarding the defectiveness of the object as a function of comparing the image captured by the detector with the reconstructed image.

65. The device according to claim 64, comprising a self-learning system configured to: receive as input a plurality of images captured by the detector for a corresponding plurality of objects; process each image of the plurality of images captured by the detector based on the reference data sets, in order to derive for each image corresponding values for the plurality of image features based on a predetermined criterion; generate for each image of the plurality of images a corresponding reconstructed image, based on the reference data sets, using the corresponding derived values for the plurality of image features; compare each image of the plurality of images captured by the detector with the corresponding reconstructed image in order to derive, for each image of the plurality of images, a corresponding similitude parameter representing a similarity between the image captured by the detector and the corresponding reconstructed image; for each image of the plurality of images, update the reference data sets as a function of the similitude parameter and of a predetermined threshold value for the similitude parameter.

66. The device according to claim 45, wherein the self-learning system includes convolutional neural networks.

Description

BRIEF DESCRIPTION OF DRAWINGS

[0091] These and other features will become more apparent from the following detailed description of a preferred embodiment, illustrated by way of non-limiting example in the accompanying drawings, in which:

[0092] FIG. 1 illustrates an optical inspection device according to this disclosure;

[0093] FIG. 2 represents a process an image is subjected to by the optical inspection device of FIG. 1;

[0094] FIG. 3 represents a self-learning process carried out on an image by the optical inspection device of FIG. 1;

[0095] FIGS. 4A, 4B and 4C show, for a defect-free parison, an image captured by a camera, a reconstructed image and a comparison between the captured image and the reconstructed image, respectively;

[0096] FIGS. 5A, 5B and 5C show, for a defective parison, an image captured by a camera, a reconstructed image and a comparison between the captured image and the reconstructed image, respectively;

[0097] FIG. 6 illustrates a line for making containers of thermoplastic material, comprising the device of FIG. 1.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS OF THE INVENTION

[0098] With reference to the accompanying drawings, the numeral 1 denotes an optical inspection device configured to inspect a parison 2.

[0099] The parison 2 includes a body 200 that is substantially cylindrical in shape.

[0100] The parison 2 (or the body 200) defines an axis of symmetry A. The body 200 is thus cylindrically symmetric about the axis of symmetry A. The parison 2 includes a closed bottom 201. The parison 2 includes a neck 202 defining an opening. The parison 2 includes a ring 203.

[0101] The device 1 is configured to receive a parison 2 at an inspection position 10. In an embodiment, the inspection position is defined by an inspection pocket. In an embodiment, the inspection pocket includes a supporting element 11 configured to hold the parison 2 (preferably by supporting the ring 203).

[0102] The device 1 includes an illuminator 3. The illuminator 3 includes a light source 31. The light source 31 is configured to emit a beam of light directed at a parison 2 (that is, at an inspection position 10). The illuminator 3 includes an emission-polarizing filter 32. In an embodiment, the emission-polarizing filter 32 is connected to the light source 31. The emission-polarizing filter 32 is configured to intercept the light beam emitted by the light source 31 and to polarize it. Thus, the parison 2 receives a polarized light beam from the emission-polarizing filter 32 and refracts it.

[0103] The device 1 includes a detector 4. The detector 4 includes a camera 41. The detector includes a receiving polarizing filter 42. In an embodiment, the receiving polarizing filter 42 is connected to the camera 41. The receiving polarizing filter 42 is configured to receive the light beam refracted by the parison 2 and to polarize it. Thus, the camera 41 receives the beam of light polarized by the emission-polarizing filter 32, refracted by the parison and further polarized by the receiving polarizing filter 42. The camera 41 is configured to capture (or acquire) an image 20 of the parison 2.

[0104] The illuminator 3 laterally illuminates the parison 2 on a first side 200A of the body 200. The detector 4 captures a lateral image of the parison 2 on a second side 200B of the body 200, opposite to the first side 200A.

[0105] The device 1 includes a memory 5. The memory 5 contains reference data. More specifically, the memory 5 contains at least a first reference data set 51 and a second reference data set 52; in an embodiment, the first reference data set 51 and the second reference data set 52 are distinct from each other.

[0106] The device 1 includes a processor 6. The processor 6 is connected to the memory 5. The processor 6 is programmed to process the image 20 captured by the camera 41 based on the reference data sets 51, 52, in order to derive the diagnostic indication 23 regarding the defectiveness of the parison 2. More specifically, the processor 6 is programmed to perform a step 61 of encoding the image 20 as a function of the first reference data set 51 in order to derive values of a plurality of image features 21. The processor 6 is also configured to perform a step 62 of decoding the image features 21, thus generating a reconstructed image 22, based on the second reference data set 52.

[0107] The processor 6 is then configured to perform a step 63 of comparing the reconstructed image 20 with the captured image 22 to derive a diagnostic indication 23 regarding the defectiveness of the parison 2.

[0108] In an embodiment, the diagnostic indication includes an error map 25 given by a difference between the captured image 20 and the reconstructed image 22 (or vice versa). In an embodiment illustrated in the drawings, the error map 25 presents uniform shading if the parison is good or patched shading if the parison is defective.

[0109] In an embodiment, the diagnostic indication 23 includes a similitude parameter 24 whose value is correlated with a degree of similarity between the captured image 20 and the reconstructed image 22. In an embodiment, the processor 6 is programmed to derive the similitude parameter 24 on the basis of the error map 25. In an embodiment, the diagnostic indication 23 includes a binary parameter value indicating whether the parison is good or defective (calculated, for example, by comparing the similitude parameter 24 with a predetermined threshold value).

[0110] In an embodiment, the device 1 (or preferably the processing system) comprises a self-learning system 7. The self-learning system 7 is preferably integrated in the processor 6. The self-learning system 7 is connected to the memory 5.

[0111] The self-learning system 7 is configured to receive a plurality of captured images 20 for a corresponding plurality of parisons 2. The self-learning system 7 is preferably configured to perform the following steps for each image 20 it receives: 61 encoding the image 20 on the basis of the first reference data set 51, in order to derive a plurality of image features 21; 62 decoding the image features 21, on the basis of the second reference data set 51, to generate a reconstructed image 22; 63 comparing the reconstructed image 22 with the captured image 20 to derive a similitude parameter 24 representing a similarity between the captured image 20 and the reconstructed image 22. 70 evaluating the similitude parameter 24 with respect to a predetermined threshold value 72 for that similitude parameter; updating (iteratively) the first reference data set 51, the second reference data set 52 and the image features 21 until the similitude parameter 24 is above (or below) the threshold parameter 72.

[0112] Thus, the self-learning system 7 solves a problem of optimizing the encoding operations 61 and decoding operations 62, where the variables are defined by the first reference data set 51 and by the second reference data set 52 (and, if necessary, by the set of image features 21), in order to minimize the similitude parameter 24, that is, bring it below a certain threshold, (or maximize it, that is, bring it above a certain threshold). Preferably, therefore, the first reference data set 51 and the second reference data set 52 are updated in combination.

[0113] Since the self-learning system 7 optimizes the encoding and decoding operations 61 and 62 with images 20 of good parisons 2, the reference data sets 51, 52 (and, if necessary, the set of image features 21) determined as a result of optimization are such that, for good parisons 2, the difference between the captured image 20 and the reconstructed image 22 is minimal. on the other hand, since these operations are not optimized for defective parisons 2, the reconstructed image 22 for a defective parison is significantly different from the captured image 20 and the processor 6 (acknowledging that difference) generates a diagnostic indication 23 signifying that the parison is defective.

[0114] Preferably, the steps 61 of encoding, 62 decoding, 63 comparing, 70 evaluating and updating the reference data sets 51, 52 (and, if necessary, the image features 21) are performed iteratively by the self-learning system 7 for each image 20 in succession (that is, all the iterations necessary for minimizing or maximizing the similitude parameter 24 are first performed for a first parison 2, then for a second parison 2 and so on). In an embodiment, the self-learning system might also perform a first iteration in which it performs the steps 61 of encoding, 62 decoding, 63 comparing and 70 evaluating for all the images 20; then, starting from the similitude parameters 24 obtained for all the parisons 2, it updates the reference data sets 51, 52 (and, if necessary, the image features 21) and continues with a second iteration in which it again performs the steps 61 of encoding, 62 decoding, 63 comparing and 70 evaluating for all the images 20, and so on.

[0115] This disclosure also relates to a line 100 for making containers of thermoplastic material—for example, bottles.

[0116] The line 100 comprises a moulding machine 101 configured to make (that is, to mould) parisons 2. In an embodiment, the moulding machine 101 is a rotary machine. The line 100 also comprises a heating oven 102 configured to receive the moulded parisons 2 and to heat them. The line 100 comprises a blow-moulding machine 103 configured to blow-mould the parisons 2 so as to make the containers. In an embodiment, the blow-moulding machine 103 is a rotary machine.

[0117] Preferably, the line 100 includes a first transfer carousel 106 configured to transfer the parisons 2 from the moulding machine 101 to the heating oven 102. Preferably, the line 100 includes a second transfer carousel 107 configured to transfer the parisons 2 from the heating oven 102 to the blow-moulding machine 103. In an embodiment, the line 100 includes a storage unit 104 for storing the moulded parisons 2 before they are blow-moulded. In an embodiment, the line 100 includes a parison orienting device 105 configured to orient the parisons 2 leaving and/or entering the storage unit 104. In an embodiment, the line 100 includes a conveyor 108 configured to convey the parisons 2 into and/or out of the storage unit 104. The conveyor 108 feeds the parisons 2 from the storage unit 104 to the heating oven 102.