EP2352867B1 - A method for optimizing a manufacturing process in a textile plant - Google Patents

A method for optimizing a manufacturing process in a textile plant Download PDF

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Publication number
EP2352867B1
EP2352867B1 EP09767925.2A EP09767925A EP2352867B1 EP 2352867 B1 EP2352867 B1 EP 2352867B1 EP 09767925 A EP09767925 A EP 09767925A EP 2352867 B1 EP2352867 B1 EP 2352867B1
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EP
European Patent Office
Prior art keywords
data
parameters
manufacturing process
parameter
database
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Revoked
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EP09767925.2A
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German (de)
English (en)
French (fr)
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EP2352867A1 (en
Inventor
Flavio Carraro
Chandran Prabakaran
Krishnan Muraliganesh
Hansruedi Wampfler
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Uster Technologies AG
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Uster Technologies AG
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Publication of EP2352867A1 publication Critical patent/EP2352867A1/en
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    • D—TEXTILES; PAPER
    • D01—NATURAL OR MAN-MADE THREADS OR FIBRES; SPINNING
    • D01G—PRELIMINARY TREATMENT OF FIBRES, e.g. FOR SPINNING
    • D01G21/00—Combinations of machines, apparatus, or processes, e.g. for continuous processing
    • D—TEXTILES; PAPER
    • D01—NATURAL OR MAN-MADE THREADS OR FIBRES; SPINNING
    • D01H—SPINNING OR TWISTING
    • D01H13/00—Other common constructional features, details or accessories
    • D01H13/32—Counting, measuring, recording or registering devices

Definitions

  • the invention relates to a method for optimizing with regard to quality, productivity and/or profitability a manufacturing process in a textile plant such as a spinning mill, weaving mill or embroidery plant, according to the preamble of the first claim.
  • Various raw materials are processed in spinning mills in several processing steps via intermediate products into yarns as end products.
  • the raw materials pass through various work stations such as blowing, opening, cleaning, mixing, carding, combing, drafting, roving, fine spinning and finally spooling and winding.
  • Machines are used for most steps which are equipped with sensors.
  • the sensor signals are used for controlling the processing and/or for monitoring the quality of the produced intermediate and end products.
  • a method and a system for quality monitoring in a spinning mill are known from DE-41' 13'384 C2 , in which the data collected for a lot or batch in a single instance are linked with this lot of the processed material and are stored centrally, so that the passage of material on the basis of a lot is completely traceable and interventions can be made in the production process of an end product in the case of quality deficiencies.
  • This procedure aims at tracing more rapidly the errors in a processing chain having several stations.
  • the detected data are also saved, so that correlations of problems influencing the quality especially over various batches can be determined.
  • the goal of DE-41'13'384 C2 is quality assurance by monitoring the machines used in the processing chain.
  • EP-0'365'901 A2 relates to a system for monitoring a plurality of textile machine workstations, e.g., winding positions of an automatic winding machine.
  • the system contains measurement elements associated with the workstations, and means for evaluating the signals supplied by the measurement elements. Characteristic parameters are obtained during the evaluation for the individual workstations and analyzed for significant deviations from the corresponding desired values. The desired values are formed from the behavior of a statistically comparable collective.
  • generalized start values are used for the individual desired values, which are converted during the course of the monitoring into more accurate, absolute values. These are updated continuously and form the core data for an automatic inference process. Consequently, the evaluation of the measurement results becomes independent of the interpretation of the operating personnel.
  • EP-0'410'429 A1 discloses a method and an apparatus for operating a spinning mill comprising various regions such as blow room, spinning preparation and spinning.
  • Correction values are formed from at least some of the quality features measured in the spinning preparation and spinning regions and are used to influence the operation of a textile machine of an earlier region.
  • US-2005/0159835 A1 describes a quality assurance model in which process status data and product check data are determined during a production process.
  • the process status data concern data which are obtained continuously from the process steps during production, and the product check data are associated with the semi-finished goods or end products produced after a process. A characteristic quantity of these data is taken for each product or product group.
  • Such characteristic data quantities and the process status data are then placed in correlation, and the quality assurance model is thus generated in such a way that an analysis is performed by data mining, such that the data placed in correlation are processed, so that a relationship is obtained between the data to the characteristic data quantities and the process status data.
  • this specification discloses a datamining method for optimizing the process steps during the production process, which is disclosed in the sequence of different process steps in semiconductor production. Based on this technology, the same end products are assumed and the difficulties lie in managing the process and the boundary conditions there. This is a different initial situation than in the textile industry.
  • a spinning mill In addition to quality assurance, a spinning mill also has an interest to know the quality, profitability and productivity to be achieved on the basis of purchased batches and lots. This knowledge is essential for the spinning mill because the measurable properties of the yarns, which can also be designated as yarn quality, is the relevant quantity for the target agreements with the industry that further processes the yarns or with the merchants purchasing the yarns.
  • Such target agreements can be found in the delivery agreements and the underlying physical or chemical properties are checked upon delivery of the merchandise.
  • the tabular values of "USTER ® STATISTICS” of Uster Technologies AG, Uster, Switzerland, are frequently used for such agreements.
  • the "USTER ® STATISTICS” concern quality reference data relating to worldwide textile production. They can be retrieved from the website www.uster.com or ordered from Uster Technologies AG, 8610 Uster, Switzerland.
  • a plurality of textile quality parameters of the participants on the market are measured over a relevant past period of time and their statistical distributions are published. They are used as a benchmark tool for determining and agreeing upon quality properties of textile structures such as yarns on the basis of their respectively measured parameters.
  • the process parameters of the employed machines are set conservatively, so that the productivity, i.e., the production of yarn per unit of time, will decrease.
  • a method shall be provided in which the employed qualities of a lot are as close as possible to the quality of a lot which can also be designated as necessary quality of a lot in order to offer the yarn quality provided for a delivery agreement. It is a further object of the invention to also enable such an evaluation when such measured data are combined with each other only subsequently because the individual production stations are operated in an isolated manner with respect to the transducers and data handover. It is a still further object of the invention to alert a user in case of abnormal events or states.
  • the invention is based on the finding that the data collected with the individual sensors in the various steps of the processing chain will allow drawing conclusions on the resulting yarn qualities and the process productivity and profitability.
  • the measured data of different completed processing steps with their defined material flows are linked together, such that measured parameters are acquired in at least two different processing steps, and such values are stored in a database and linked with each other in an index file. Thus, gaps between different processing steps are bridged. Mutual influences and dependencies are represented in a model of the manufacturing process.
  • all data available from the whole manufacturing process shall be linked with each other in the index file, in order to completely represent the manufacturing process.
  • the data include material data, processing data and data related to time. In practice, however, there may be cases in which a complete data collection and linking is not possible. Some data may not be available, for instance if some machines are not equipped with sensors or if some sensors are not working. Therefore, the evaluation of the data should be tolerant with respect to missing input information.
  • raw material is processed in a manufacturing process in several processing steps into intermediate products and an end product is produced.
  • Parameters of the raw material, the intermediate products and/or the end product are measured and stored in a database.
  • the parameters are measured in at least two different processing steps, stored in the database and linked in an index file.
  • the data stored in the database are statistically evaluated, at least one parameter of the raw material is predetermined and at least one parameter of the end product is determined by comparison of the data stored in the database with the at least one predetermined parameter of the raw material, depending on a chosen manufacturing process.
  • the data stored in the database are statistically evaluated, at least one parameter of the end product to be produced is predetermined and at least one parameter of the raw material is determined by comparison of the data stored in the database with the at least one predetermined parameter of the end product, depending on a chosen manufacturing processes.
  • the at least two different processing steps are performed on at least two different work stations or machines.
  • At least one parameter can be measured for the raw material used in the processing steps and/or at least one parameter can be measured for the end product produced in the manufacturing process.
  • parameters of the manufacturing process can additionally be stored in the database and linked in the index file.
  • Such parameters of the manufacturing process comprise, e.g., a setting of a machine, a characteristic of a machine and/or a temporal workflow.
  • the statistical evaluation of the data stored in the database is preferably performed in an evaluation module.
  • the statistical evaluation may comprise data mining.
  • the parameters of the raw material in a first step are displayed in a display and input module, so that an index page for the index file is entered in order to manually link the data associated with the said manufacturing process in the index file.
  • the displaying may comprise the displaying of the parameters from predetermined time window, which are associated in respect of time to a manufacturing process.
  • An alarm can be triggered when an abnormal event or state is detected based on the data stored in the database.
  • the threshold parameter values for triggering the alarm can be set by a user or, by a manufacturer or automatically calculated from other data.
  • the computer program product according to the invention comprises a program code stored on a machine-readable carrier for executing the method according to the invention as described above when the program product runs on a computer.
  • the evaluation module provides proposals for setting machines in the production chain or changes these settings in the machine directly, if the evaluation module is connected with the machines.
  • Figure 1 shows a diagram with a possible target agreement in the form of a predetermined quality curve 103 for a textile structure such as a yarn.
  • a specific imperfection parameter such as the size (e.g., diameter, or a product of diameter and length) of the imperfection is entered along the horizontal axis 101 and a frequency of the respective imperfection parameter such as the number of imperfections is entered along the vertical axis 102.
  • Individual yarns are represented by measuring points.
  • a first yarn 104 which is illustrated by its measuring point arranged above the curve 103, would be regarded as non-satisfactory with reference to this imperfection parameter.
  • the quality of the yarn can be improved to quality 114.
  • the parameterization of the device is relevant, as is shown by arrow 124, which therefore cuts out a higher number of imperfections. This is at the expense of the productivity (speed of the yarn passage) however.
  • the invention now allows the selection of the parameter settings in the individual processing stations on the one hand and also the selection of the suitable raw product batches, e.g., the cotton bales, on the other hand, which occurs in such a way that the measured values 115, 116, 117 are much closer to the target curve 103, leading to cost benefits for the spinning mill and ensuring at the same time the fulfillment of the target agreements.
  • the suitable raw product batches e.g., the cotton bales
  • a quality vector which characterizes a yarn and is the result of the plurality of measured values determining the quality properties is relevant in quality evaluation.
  • Figure 2 shows a material flow diagram, with a selection of manufacturing processes being shown.
  • the input side shows raw materials 201 such as textile fibers for example which can be characterized by different provenances, and in the case of the same provenance by individual batches with different properties. Fiber mixtures can also be used. That is why a combination of two different raw materials 201 is shown by the two arrows 211 which then pass through a predetermined manufacturing step 202.
  • the different manufacturing processes depend on the textile businesses and can comprise for example the processes of opening, cleaning, mixing, carding, combing, drafting, spinning and finally winding on a body such as a bobbin for further use, especially for delivery to a purchaser.
  • Figure 3 schematically shows a database 300 which comprises data entries 301, 302, 303, 304, ..., 311, 312, 313, ..., 391, 392,393, ... and an evaluation module 3100 which carries out several functions which are described below.
  • the data 301 to 304 shown as pages are for instance data of raw materials 201 (see Figure 2 ), which are entered prior to or upon arrival at the spinning mill. This includes for instance data of cotton bales, their provenance, supplier and further data characterizing the same like degree of ripeness, etc.
  • An arrow T indicates that these data are entered in a continuous way over time and a large number of comparative data of the raw materials 201 are present after a certain time.
  • Individual measurment data and/or setting data 311, 312, 313 of running intermediate processes 202, 203 are entered and stored at other places of the database 300.
  • One example for such an intermediate process is the carding of fibers into a sliver on a carding machine.
  • Each of these data pages 301, 302, 303, 304..., 311, 312, 313, ... 391, 392, 393... comprises one or several measured values and thus a data vector.
  • the statistical evaluation occurs by the evaluation module 3100.
  • the module 3100 knows which starting materials 201, which are characterized here by data 301 and 303, have passed through which manufacturing process (here: 312) in order to obtain which end product 204, which is characterized here by the data 393.
  • These data pages are each shown in Figure 3 in a hatched way. They are associated with a specific manufacturing process or flow of materials, as is indicated in Figure 2 with the arrow 213.
  • the indexing of the concatenated elements of a manufacturing process lead to an index file with the entries 3001, 3002, 3003, ..., with the entry 3002 comprising the reference to the elements 301, 303, 312 and 393.
  • This entry 3002 is thus the visualization of all parameters of the manufacturing process 213 of Figure 2 .
  • Producing this indexed connection may possibly need to be made manually because many spinning mills run individual machines that are not networked, so that the data need to be loaded into the database 300.
  • the individual processing steps of an individually regarded manufacturing process characterized by process arrow 213 in Figure 2 run in a temporally successive manner, but not necessarily sequentially on the individual machines in a spinning mill, so that a direct temporal allocation is not necessarily correct.
  • several days may lie between the start of processing and the entry of a first data page 301 on the one hand and the finishing of the product 204 on the other hand, which is then followed by preparing a data page 393.
  • the respective data pages will advantageously automatically be linked to the index pages 3001, 3002, 3003.
  • the evaluation module 3100 also has the function of showing the textile experts in a spinning mill in a display and input module 3200 a suitable time window of the output data and measured data of the individual flows of material, so that the correct index page (which in this case is 3002) is produced by manual choice.
  • the time factor of the display is important, so that temporally older data pages of prior to 1 to 3 days before the current point in time are displayed in the processed bales (the starting materials 201) and later points in time of between 2 days to 8 days before the current point in time are displayed for the processing steps of the flow of material and 16 hours to the current point in time are displayed for the end products for example.
  • the index entry is produced by making manual selections from the displayed data pages that are not yet allocated and the allocated data pages are removed from the display. It is common practice to provide process codes instead of information on the parameters.
  • Figure 4 shows two spaces: A first space 401 which is characterized by parameters x 1 , x 2 of raw materials, as are designated in Figure 2 with reference numerals 201, and a second space 402 which is characterized by parameters y 1 , y 2 of end products, as are designated in Figure 2 with reference numeral 204.
  • Points 411, 412 are entered in the two spaces 401, 402 schematically, which points correspond to certain measured raw materials 201 and end products 204, respectively.
  • the parameters y 1 , y 2 of end products 204 can be combined if required into a single quality parameter.
  • An arrow 403 in Figure 4 which allocates a raw material 201 to an end product 204 represents a specific manufacturing process P. This fact can be expressed as follows in a symbolic way: P : x 1 , x 2 ⁇ y 1 x 1 x 2 , y 2 x 1 x 2 .
  • parameters y 1 , y 2 of the end products 204 can be approximated numerically if parameters y 1 , y 2 of the end products 204 are known for the respective process P, which correspond to such raw materials 201 whose parameters x 1 , x 2 are as close as possible to the respective point 411.
  • the two spaces 401, 402 which are shown in Figure 4 in two dimensions for reasons of simplicity, can have any desired number of dimensions.
  • methods known from multi-variant statistics can be employed, e.g., principal component analysis (PCA).
  • the allocation 403 does not have to be unique, as is illustrated with the exemplary process 213 of Figure 2 . If this is the case and/or if the data in the end product space 402 lie far apart, known interpolation or extrapolation methods can be applied, see for example J. Gleue, "Triangulierung und Interpolation von im R 2 unregel Spotify gown ” (Triangulation and Interpolation of Data Distributed Irregulary in R 2 ), Hahn-Meitner-Institut für Kernforschung Berlin GmbH, Report No. HMI-B 357, July 1981. Further known possibilities for easier handling of the data in a computational respect are the following: data reduction, data combination, elimination of outliers, resampling and/or smoothing.
  • Figure 5 shows a graphical display of two values 501 and 502 of a parameter of the property of a yarn as an end product and the allocation of products 511, 512 according to the invention and a product 513 according to the state of the art. They concern threshold values which are to be undercut by the respective values from the data pages 391, 392, 393.
  • a vertical axis 500 which is marked with %, shows a random parameter for example from the large number of the property values for yarns as predetermined by the benchmark tool USTER ® STATISTICS.
  • the 25 % value can be stated for the same parameter for example, wherein in this case only 25 % of all tested materials have this value or better.
  • an area 513 can be covered with a certain yarn thickness according to the state of the art.
  • this variance of the parameter inspected here can be narrowed down. Variance can be reduced for example by a factor 2, so that by choosing a certain flow of material which also includes the choice of certain raw materials and optionally settings of machines of the flow of material, a yarn can be produced which in respect of this parameter has a higher quality and remains below the better benchmark 502.
  • the product can be achieved with the specifications of value 501, but with a lower variance, so that higher value creation is possible by combination of the values because the agreed default parameters are not undercut too excessively.
  • the order is relevant which defines a predetermined number of parameters of the product to be delivered. These parameters are reflected directly or approximately by the data pages 391, 392, ... (cf. Figure 3 ).
  • the statistical methods of data mining in the evaluation module 3100 it is thus possible with the statistical methods of data mining in the evaluation module 3100 to provide an output of a possible flow of material (not shown in the drawings, but corresponding to Figure 3 ), which output undercuts the defaults of the product to be supplied only by measurement precision and the variance of all predetermined parameters.
  • This technical decision of the choice of a computable set of data on a flow of material can also comprise a compilation of individual data which are not yet saved as a data record in this form and form a new data record 3008 after the completion of the production lot. In this case, it is not the order parameters but the end product defined by the new material flow that is characterized on the basis of its parameterized properties.
  • measured data that cannot be influenced directly in the evaluation such as quality data of the starting materials
  • the quality parameters of the end products come as close as possible to and are slightly better than the agreed delivery data, which allows an especially temporal improvement of the production flows in the setting data of the existing machines.

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  • Engineering & Computer Science (AREA)
  • Textile Engineering (AREA)
  • Mechanical Engineering (AREA)
  • General Factory Administration (AREA)
  • Spinning Or Twisting Of Yarns (AREA)
  • Preliminary Treatment Of Fibers (AREA)
EP09767925.2A 2008-11-14 2009-11-13 A method for optimizing a manufacturing process in a textile plant Revoked EP2352867B1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
IN2802CH2008 2008-11-14
PCT/CH2009/000363 WO2010054497A1 (en) 2008-11-14 2009-11-13 A method for monitoring a manufacturing process in a textile plant

Publications (2)

Publication Number Publication Date
EP2352867A1 EP2352867A1 (en) 2011-08-10
EP2352867B1 true EP2352867B1 (en) 2014-04-16

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EP09767925.2A Revoked EP2352867B1 (en) 2008-11-14 2009-11-13 A method for optimizing a manufacturing process in a textile plant

Country Status (4)

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EP (1) EP2352867B1 (zh)
JP (1) JP5382475B2 (zh)
CN (1) CN102216503B (zh)
WO (1) WO2010054497A1 (zh)

Cited By (3)

* Cited by examiner, † Cited by third party
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EP4101957A1 (en) * 2021-06-11 2022-12-14 Maschinenfabrik Rieter AG Device and method for determining a classification of a current performance of one or more parts of a spinning mill
WO2022259108A1 (en) * 2021-06-11 2022-12-15 Maschinenfabrik Rieter Ag Device and method for determining a classification of a current production output of at least one or more parts of a spinning mill
EP4375405A1 (en) * 2022-11-22 2024-05-29 Maschinenfabrik Rieter AG Device and method for determining a cause of a malfunction in a plurality of textile machines

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CN103161006B (zh) * 2013-03-27 2014-05-07 吴江市金平华纺织有限公司 一种循环式织机维护通道
EP2881818A1 (en) * 2013-12-03 2015-06-10 Airbus Operations S.L. Method for managing a manufacturing plant for the production of carbon fiber pieces
DE102015012214A1 (de) * 2015-09-17 2017-03-23 Saurer Germany Gmbh & Co. Kg Verfahren zum Anpassen einer eine Garnpartieherstellung betreffenden werksseitigen Auslegung einer Offenend-Rotorspinnmaschine
EP3320495A1 (de) 2015-11-10 2018-05-16 Uster Technologies AG Lokales netzwerk für textile qualitätskontrolle
WO2017190259A1 (en) * 2016-05-04 2017-11-09 Uster Technologies Ag Monitoring contamination in a stream of fiber flocks
EP3654114A1 (en) 2018-11-16 2020-05-20 Maschinenfabrik Rieter AG Parameter manager, central device and method of adapting operational parameters in a textile machine
ES2917375T3 (es) 2020-04-30 2022-07-08 Rieter Ag Maschf Dispositivo y método para detectar una falla en una hiladora y para estimar una o más fuentes de la falla
CN115142187A (zh) * 2022-07-06 2022-10-04 圣东尼（上海）针织机器有限公司 针织大圆机织造质量优化控制系统

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP4101957A1 (en) * 2021-06-11 2022-12-14 Maschinenfabrik Rieter AG Device and method for determining a classification of a current performance of one or more parts of a spinning mill
WO2022259108A1 (en) * 2021-06-11 2022-12-15 Maschinenfabrik Rieter Ag Device and method for determining a classification of a current production output of at least one or more parts of a spinning mill
EP4375405A1 (en) * 2022-11-22 2024-05-29 Maschinenfabrik Rieter AG Device and method for determining a cause of a malfunction in a plurality of textile machines
WO2024110351A1 (en) 2022-11-22 2024-05-30 Maschinenfabrik Rieter Ag Device and method for determining a cause of a malfunction in a plurality of textile machines

Also Published As

Publication number Publication date
CN102216503B (zh) 2014-02-19
CN102216503A (zh) 2011-10-12
JP2012508837A (ja) 2012-04-12
WO2010054497A1 (en) 2010-05-20
JP5382475B2 (ja) 2014-01-08
EP2352867A1 (en) 2011-08-10

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