Self-optimization of the Weaving Process
Weaving is one of the oldest processes for the fabrication of textiles. The process is characterized by orthogonal interlacing of warp- and weft-threads. To enable the interlacing, the warp-threads have to produce the so-called shed, which is created by raising or lowering of the weft-threads. After the shed-formation, the weft yarn is inserted into the open shed. The first looms are dated at approximately 3000 BC. To produce the required tension for weaving, stones were used as weights on the warpthreads of the early looms. In the course of industrialization however, the first mechanically powered looms have been created. In the 20th century the development of looms was characterized by an increasing automation of the weaving process sequence, with an aim of higher efficiency. Due to the cumulative computerization of the weaving process, weaving became the most productive process for producing textile fabrics. On modern looms, different materials can be processed and a wide variety of woven goods can be produced such as clothing fabric like denim or suit cloth made of cotton or cashmere wool to technical goods like parachutes made of polyamides or lightweight constructions made of carbon fibres.
Practical Issues
The modification of the weaving-loom to apply various materials requires complex adjustments be made to the machine. For example, yarn guiding elements, weft-inserting-systems or process parameters must all be aligned to
the particular material. Another important characteristic for the judgement of the weaving process is warp tension (WT). If the warp tension is too high, the warp threads will break and the weaving process will stop. If the warp tension is too low, the warp threads will obstruct the weft insertion and the fabric cannot be produced. The adjustment of the warp tension is based on the experience of the weaver. Furthermore, the quality surveillance of the fabric does not take place while the fabric is on the loom. The quality surveillance of a fabric takes place in the so-called fabric inspection by trained employees, at special inspection tables after the fabric is removed from the
loom. Therefore, it is not possible to respond to failures which occur during the weaving process.
Approach
The aim of this project is to design the setup-process of the loom to such an extent that the loom itself will define the necessary process parameters for a desired product quality. For this purpose a meta-model will be generated and integrated into the loom achieving a premium worsted fabric with a shortened setup time (30 min) at a cost reduction of 61.10 € to 60.50 € per running meter. Furthermore, there was a reduction in waste material associated with the setup resulting in the saving of the expensive materials. At first, the process has to be modelled with regard to
the warp tension. In addition, a self-optimizing-routine which optimizes the weaving parameters of the loom will have to be realized. Another aspect will be the identification of suitable sensors for monitoring the product-quality using the ITA “9-Step-Tool” method and their integration in the weaving process.
Technical Challenge
With help of the “9-Step-Tool” an X-ray sensor of the BST ProControl GmbH, Freudenberg has been chosen to monitor the fabric grammage. Following the integration of the sensor within the weaving process, a control loop monitoring the fabric grammage was realized with help of a smith-predictor. For additional system monitoring
of the fabric-quality, an optical camera system has been developed and integrated into the loom.
With the use of a thread sensor which has been integrated into the loom, the warp tension could be measured
online. The obtained data has been used for modelbuilding by quadratic regression. By the dint of the Gauss-Markov theorem and additional quality criteria, an optimized set of parameters could be calculated. In this manner a self-optimizing-routine was generated, which was implemented with the help of products (Soft SPS) by
the company iba AG, Fürth into the weaving loom. Trials in the technical centre of ITA show that using this
routine can reduce the warp thread tension up to 13 %. The optimizing-routine has been field tested at the weaving plant Weyermann Technical Textiles GmbH & Co. KG, Wegberg. An air jet loom of the company Picanolnv, Ieper, Belgium was connected to the routine. Due to the new settings after completing the optimizing-routine, the loom runs 100 rpm faster. Furthermore, the loom produced two shifts without process-related stops and the fabric quality fulfilled the requirements. On-going work will aim to embed further parameters as e.g. the energy consumption, or the position of the loom’s components into the optimization-routine. And for that purpose, both appropriate sensors and additional actuators were needed to be integrated into the loom. One solution to monitor the air consumption was the installation of a flow sensor between the compressed air supply, and the loom. Present-day measuring techniques enable the wireless transfer of measured data at sufficient speed. By integration of stepper motors e.g. the position of the warp stop motion can also be adjusted automatically. The position of the warp stop motion therefore has a strong
impact on the warp thread tension. There is still however, the question whether a regression model can provide the
optimum results for the described additional input- and output-parameters. Hence alternative models have to be analyzed. Likewise the control of the self-optimization by so-called Smart Devices like Tablets or Mobiles will be of
interest as well as the storage and forwarding of process data using RFID technology.