Winding condition generating apparatus, winding apparatus, winding defect level prediction value generating apparatus, winding condition calculating method, winding method, and winding defect level prediction value generating method

A winding defect level prediction value generating apparatus comprising at least one processor configured to:

• calculate a winding defect level prediction value from a winding parameter and a winding condition of a new wound web, using a learning model created by machine learning with a combination of a winding parameter, a winding condition and a winding defect level value in producing a wound web as training data;
• and
• output the winding defect level prediction value,
• wherein the winding parameter includes at least one of a web width, a web transport velocity, a web winding length, a diameter of a winding core around which the web is wound, a name of a line in which the wound web is produced, a web thickness, a difference between a maximum thickness and a minimum thickness in a web width direction, or a modulus of elasticity of the web,
• wherein the winding condition includes at least one of a tension of the web at the start of winding, a tension of the web at the end of winding, a knurling height, a pressure of an air press for pressing the web, or a pressing force of a touch roller for pressing the web,
• wherein the winding defect level prediction value includes a web winding misalignment value and a web damage defect level, and
• wherein in a case where with respect to a set of the winding conditions obtained in producing the web, which is the training data, a value of each winding condition item is denoted by Cni, an allowable quality range value set to the value Cni of the winding condition item is denoted by Tin and the number of items to which the allowable quality range value Tni is set is denoted by N, a range obtained by the following Equation for each item is added to the winding conditions, and 3N−1 pieces of the training data or a part of the training data is assigned as additional training data,
• Ck=Cni±0.5×Tni.

A winding condition calculating method at least comprising:

• a step of creating a learning model by performing machine learning using a combination of a winding parameter and a winding condition in producing a wound web that satisfies a target winding quality as training data;
• a step of inputting a winding parameter of a new wound web;
• and
• a step of calculating a winding condition of the new wound web using the learning model from the winding parameter,
• wherein the winding parameter includes a web width, a web transport velocity, and a web winding length, and
• wherein the winding condition includes a tension of the web at the start of winding and a tension of the web at the end of winding;
• further comprising:
• a step of adding, in a case where with respect to a set of the winding conditions obtained in producing the web, which is the training data, a value of each winding condition item is denoted by Cni, an allowable quality range value set to the value Cni of the winding condition item is denoted by Tni and the number of items to which the allowable quality range value Tni is set is denoted by N, a range obtained by the following Equation for each item, to the winding conditions, and assigning 3N−1 pieces of the training data or a part of the training data, as additional training data,
• Ck=Cni±0.5×Tni.

A winding defect level prediction value generating method at least comprising:

• a step of creating a learning model that machine-learns training data of a combination having a winding parameter and a winding condition in producing a winding roll as an input and having a winding defect level value as an output;
• a step of inputting a winding parameter and a winding condition of a new wound web;
• and
• a step of calculating a winding defect level prediction value of the new wound web from the winding parameter and the winding condition, using the learning model,
• wherein the winding parameter includes at least one of a web width, a web transport velocity, a web winding length, a diameter of a winding core around which the web is wound, a name of a line in which the wound web is produced, a web thickness, a difference between a maximum thickness and a minimum thickness in a web width direction, or a modulus of elasticity of the web,
• wherein the winding condition includes at least one of a tension of the web at the start of winding, a tension of the web at the end of winding, a knurling height, a pressure of an air press for pressing the web, or a pressing force of a touch roller for pressing the web, and
• wherein the winding defect level prediction value includes a web winding misalignment value and a web damage defect level,
• further comprising:
• a step of adding, in a case where with respect to a set of the winding conditions obtained in producing the web, which is the training data, a value of each winding condition item is denoted by Cni, an allowable quality range value set to the value Cni of the winding condition item is denoted by Tni and the number of items to which the allowable quality range value Tni is set is denoted by N, a range obtained by the following Equation for each item, to the winding conditions, and assigning 3N−1 pieces of the training data or a part of the training data, as additional training data,
• Ck=Cni±0.5×Tni.