Automation Cuts Cycle Times in Half Without Replacing Existing Presses

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PVI Esskå 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.
Manufacturers may be able to increase the productivity of existing equipment by addressing controls, automation, integration and production bottlenecks rather than replacing entire production lines.
AP&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.

ULRICEHAMN, Sweden — Swedish automotive supplier PVI Esskå 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.

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.

“We see it again and again: customers who have become blind to what their existing lines are capable of,” said Jörgen Theander, product manager for automation at AP&T. “But with the right automation and integration, we can often double their output using the same presses they already have in place.”

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.

“If they have a lot of semi-finished material waiting between stations, that is a sign that production is not fully connected,” Theander said. “Then we can help find a solution that reduces that kind of tied-up capital and gets throughput moving.”

AP&T is also introducing a new control system for its automation equipment that uses machine learning to optimize production cycles.

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.

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Those micro-stops can be particularly difficult for manufacturers to identify because an individual interruption may last only a few minutes. Repeated across hundreds or thousands of production cycles, however, they can consume significant production capacity.

“For our customers, it is about eliminating costs and reducing tied-up capital,” Theander said. “With the economy the way it is, production needs to become smarter — not just faster.”

The control platform is also designed to provide operators with greater visibility into production-line performance and help them identify where adjustments can improve output.

AP&T says the approach could be particularly useful for manufacturers operating older manual or semiautomated equipment. The company sees opportunities in the United States, including the home appliance industry, where existing presses and production equipment could potentially be upgraded with new controls and automation rather than replaced.