Improved population-based incremental learning algorithm for scheduling multi-bridge waterjet cutting processes

An improved population-based incremental learning (IPBIL) algorithm is proposed to plan collision-free cutting paths of multi-bridge water-jet cutting processes. Multi-bridge waterjet cutting machines (MWCM) are one of the preferred solutions for cutting large-size flat work pieces. The work areas of two adjacent bridges with a waterjet head are designed to overlap with each other in an MWCM to ensure no dead zones of cutting. It means that a pair of adjacent bridges may crash with each other over their overlapped area and result in damages of the machine. It is an interference problem that one must solve for MWCM. Due to a great number of curves to be cut in a large process area of MWCM, it needs to optimize the cutting routes of the bridges. This paper proposes an IPBIL-based integrated method for solving both the interference and routing problems. The validity of the presented method is confirmed with a case study.