Automatic sequencing for panel bender

Day by day expectations from the software automation is growing, making it important to gain needed position at the competitive markets. Demand is that programming of the products for the machines much be faster, easier and more automated. Many manufacturers are running production where each part is unique due to high customization of the products. Small batch sizes and One-of-a-Kind-Production (OKP) are getting more popular. If programming of such production system cannot be made fast or/and automated, the manufacturer needs to hire multiple programmers and buy multiple licenses for the software to ensure that the production keeps running without unnecessary interruptions. For software automation there is also other aspects than just manufacturing itself. One of these is cost estimation. Due to high competition at the markets, it is necessary to be able to estimate production costs fast and with enough accuracy. Customer’s expectations have grown to get al most instant quotes, which is setting requirement for cost estimation high. To be able to estimate the cost of the product, it is first required to know how it will be manufactured. Even at its best, the cost estimation can be only as precise as the extracted production information of the product is. Extracting all this information manually without proper tools is a lot of work and is prone to human errors. Results of this study are concept and prototype implementation of the automatic sequencing for the panel bender machine. This concept and prototype consists from two main components which are feasibility study and bend sequence planner. Feasibility study analyses products to identify how feasible they are for the given machine. Based on feasibility study results it is possible to tell if product cannot be manufactured with the given machine or if it can be only partially manufactured with the given machine, leaving some features no the be done. Bend sequence planner search for valid and optimal bend sequence for the given product and machine. The bend sequence planner considers results of the feasibility study to be able to eliminate items that are not feasible for the given machine. Bend sequence planner is implemented using population-based algorithm, which measures goodness of the individuals by identifying if they are in valid state, evaluating how optimal they are, and how good they match to configurable user preferences