Techniques are directed to a method and a device for monitoring a yarn tension of a running yarn in a yarn treatment process. To this end, the yarn tension of the yarn is continuously measured and the measurement signals for the yarn tension are compared with a threshold value of an admissible yarn tension. In the event of an inadmissible tolerance deviation of the measurement signals, a short-term signal path of the yarn tension is detected as a fault graph. In order to enable a fault diagnosis, the fault graph of the yarn tension is analyzed using a machine learning program. The fault graph is then allocated to one of the existing fault categories or to a new fault category. A device for this purpose may include a diagnosis unit, which cooperates accordingly with the yarn tension evaluation unit.

An instrument for identifying at least one of fiber blend composition and fiber blend ratio in an input material moved by a third set of fiber movements. A second spectral radiation source directs radiation toward the input material. A second spectral sensor receives portions of the second radiation that pass through the input material. A third spectral sensor receives portions of the second radiation that reflect off of the input material. A controller processes the signals from at least one of the second spectral sensor and the third spectral sensor to determine at least one of the fiber blend composition and the fiber blend ratio in the input material. The controller also sends control signals to the second electromagnetic radiation source and the third set of fiber movements.

The technology relates to modeling cross-sections of yarn. For instance, modeling cross-sections of yarn may include receiving yarn simulation input comprising a descriptive model of a general curvature followed by the yarn, providing a plurality of fibers distributed raidally from the center of a ply, setting a base position based on parameters, applying a strain model to simulate the effect of stretch forces applied to the yarn, and outputting a yarn model indicating position and directionality of fibers in the yarn. The technology also relates to real-time modeling of a garment comprising a fabric. For instance, real-time modeling of a garment may include providing an input associated with one or more parameters of the fabric, receiving frames of a computer simulated garment, the computer simulated garment including a simulation of the fabric, the fabric simulation including yarns simulated based on a yarn model.

Techniques are directed to a method and a device for monitoring a yarn tension of a running yarn in a yarn treatment process. To this end, the yarn tension of the yarn is continuously measured and the measurement signals for the yarn tension are compared with a threshold value of an admissible yarn tension. In the event of an inadmissible tolerance deviation of the measurement signals, a short-term signal path of the yarn tension is detected as a fault graph. In order to enable a fault diagnosis, the fault graph of the yarn tension is analyzed using a machine learning program. The fault graph is then allocated to one of the existing fault categories or to a new fault category. A device for this purpose may include a diagnosis unit, which cooperates accordingly with the yarn tension evaluation unit.

An instrument for identifying at least one of fiber blend composition and fiber blend ratio in an input material moved by a third set of fiber movements. A second spectral radiation source directs radiation toward the input material. A second spectral sensor receives portions of the second radiation that pass through the input material. A third spectral sensor receives portions of the second radiation that reflect off of the input material. A controller processes the signals from at least one of the second spectral sensor and the third spectral sensor to determine at least one of the fiber blend composition and the fiber blend ratio in the input material. The controller also sends control signals to the second electromagnetic radiation source and the third set of fiber movements.

Techniques are directed to a method and a device for monitoring a yarn tension of a running yarn in a yarn treatment process. To this end, the yarn tension of the yarn is continuously measured and the measurement signals for the yarn tension are compared with a threshold value of an admissible yarn tension. In the event of an inadmissible tolerance deviation of the measurement signals, a short-term signal path of the yarn tension is detected as a fault graph. In order to enable a fault diagnosis, the fault graph of the yarn tension is analyzed using a machine learning program. The fault graph is then allocated to one of the existing fault categories or to a new fault category. A device for this purpose may include a diagnosis unit, which cooperates accordingly with the yarn tension evaluation unit.

Techniques involve monitoring a texturing process for producing crimped threads. A thread tension is measured continuously on the textured thread and the measured signals of the thread tension are detected and analyzed continuously, at least in one time interval. For the early diagnosis of one of multiple sources of faults, a sequence of the measured signals occurring in the time interval is analyzed by means of a machine learning program. To this end, a device for monitoring has a diagnostic unit, which interacts with the thread tension measuring device in such a way that the measured signals of the thread tension can be analyzed by means of a machine learning program in order to identify one of multiple sources of faults.

In order to optically sense a yarn moved in the longitudinal direction of the yarn, a yarn sensor has a light source, a detector and a light guiding element. The yarn sensor is based on the effect of frustrated total internal reflection (FTIR). Because of the FTIR effect, scattered light exiting the light guiding element in the contact region between the yarn and an outer surface of the light guiding element is detected by means of the detector, in which case sensing of the yarn lying against the outer surface is enabled. Alternatively, the reduced intensity in the totally internally reflected beam is then sensed by the detector. The intensity in the totally internally reflected beam is reduced mainly by the scattered light coupled out of the light guiding element.

Fiber emitters, such as carbon nanotube (CNT) yarns, are used to create infrared (IR) transmitters that can operate at high data rates, can shift spectral response, and can emit polarized light, for example by alignment of the fiber emitters in close proximity and in parallel directions. These fiber emitters can, for example, be used in patches that can be bonded to fabric or to an object, or can be woven into fabric during fabrication of a textile. The fiber emitters can be used in a variety of methods, including for friend or foe identification, communications, and identification of objects.

Techniques involve a machine system for producing or treating synthetic threads, comprising a plurality of machine components, having actuators and/or sensors, that are associated with a plurality of control components. The control components are connected by a machine network to a central machine control station. In order to remedy process disruptions as quickly as possible and to ensure uniform product quality, the control components are coupled in parallel to a central analysis station by an analysis network.