Need to Know<\/p>\n
PVI Essk\u00e5 cut cycle times in half and increased annual production capacity by approximately 500 hours by upgrading controls and automation while retaining its existing hydraulic presses.
\nManufacturers may be able to increase the productivity of existing equipment by addressing controls, automation, integration and production bottlenecks rather than replacing entire production lines.
\nAP&T’s new automation control system uses machine learning to optimize individual production cycles, targeting micro-stops while balancing cycle time, equipment wear and downtime.<\/p>\n
ULRICEHAMN, Sweden \u2014 Swedish automotive supplier PVI Essk\u00e5 cut cycle times in half and increased annual production capacity by approximately 500 hours by upgrading the controls and automation surrounding its existing hydraulic presses rather than replacing the presses themselves.<\/p>\n
The project was completed by AP&T and illustrates how manufacturers may be able to increase the productivity of existing equipment by addressing controls, automation and integration rather than investing in entirely new production lines.<\/p>\n
\u201cWe see it again and again: customers who have become blind to what their existing lines are capable of,\u201d said J\u00f6rgen Theander, product manager for automation at AP&T. \u201cBut with the right automation and integration, we can often double their output using the same presses they already have in place.\u201d<\/p>\n
AP&T integrates presses from multiple manufacturers with its automation equipment and develops simulations and cycle-time calculations before equipment is installed. The company also examines production flow, work-in-process inventory and bottlenecks when evaluating existing lines.<\/p>\n
\u201cIf they have a lot of semi-finished material waiting between stations, that is a sign that production is not fully connected,\u201d Theander said. \u201cThen we can help find a solution that reduces that kind of tied-up capital and gets throughput moving.\u201d<\/p>\n
AP&T is also introducing a new control system for its automation equipment that uses machine learning to optimize production cycles.<\/p>\n
The system evaluates each cycle and adjusts equipment movement so machines operate faster where possible and slower where necessary. The goal is to optimize cycle time while reducing equipment wear, downtime and small production interruptions.<\/p>\n