Optimizing Sheet Metal Bending: A Comparative Study on Springback Characteristics of AISI 4130 and ASTM A653M

Jack S. E. — Tue, 07 Jul 2026 00:00:00 +0000

Sheet metal is a wide variety of materials that are used in a variety of different industries to make components that are not only durable but are also lightweight and can be easily bent, stamped and cut into the shape that is required. It is usually a slim metal sheet in different alloys, customised for applications in automotive and aerospace building, consumer goods, and so on. The study is based on the application of Finite Element Analysis in sheet metal bending for the purpose of calculating spring-back for materials AISI 4130 steel (0.6mm) and ASTM A653M (1.0mm). The k-factor was 0.4625 for AISI 4130 and 0.5375 for ASTM A653M. The analysis showed that AISI 4130 has a higher spring-back value, especially at a 110° angle, and ASTM A653M has a lower spring-back value. The value of von Mises stress for AISI 4130 material was 8.464 × GPa and for ASTM A653M material was 6.971 × GPa. To sum up, more springback compensation is suggested for AISI 4130 at mid-range angles, while ASTM A653M offers more predictable and stable forming results. The results indicate that the bending behaviour of the two materials is different in terms of the k factor, showing the uniqueness of the bending behaviour of each material. The evaluation of the springback bending behaviour also showed that the bending behaviour is strongly affected by elastic recovery, which has to be taken into consideration in manufacturing processes to ensure accuracy, particularly for AISI4130. The variables’ effect on springback is used as a basis to improve finite element models for both materials, to increase failure prediction accuracy, and to increase manufacturing efficiency and reliability.

An Optimization Model for the Dynamic Production Scheduling Problem with Machine Failures and Energy Constraint

Ogagavwodia Ejovi Okuma, Briggs Otekenari Tonye — Thu, 20 Aug 2026 00:00:00 +0000

Under uncertain machine-failure disruptions, Flexible Manufacturing Systems (FMSs) must be supported by a strong scheduling optimization model that is able to guarantee satisfaction of due-date requirements by relying on threshold scenarios. This paper proposes a complete robust optimization approach to dynamic production scheduling in the presence of uncertainty in machine failures, minimization of energy consumption, and compliance with production deadlines. The challenge of dynamic scheduling in a flexible manufacturing workshop with machine failure disturbances is to update the production plans quickly, with multiple objectives to be achieved — such as completion time, energy consumption, and schedule deviation — that are often competing, thus greatly complicating the computation. A Mixed-Integer Linear Programming (MILP) model was developed that explicitly considers the degradation in machine reliability, varying energy consumption in machine operational states, and real-time rescheduling triggers. The changing machine failure rate is added to the integrated optimization of job shop production scheduling and predictive maintenance, and the machine state is predicted based on the processing time of the current job. Flexible flow-shop systems scheduling with uncertainties in processing time is optimized using robust optimization and series-parallel production system configurations are taken into consideration in the context of preventive maintenance policies. The proposed framework is based on scenario-based robust optimization, using hybrid dynamic rescheduling triggers based on events and periods. To address the issues of rescheduling problems, namely how to reschedule a job and when to reschedule, a hybrid dynamic rescheduling trigger strategy (with four judgment mechanisms) has been developed. Based on computational experiments on the benchmark instances, the results confirm that the model outperforms the deterministic and traditional robust methods in terms of computational performance. The model reduces the make span by 12%–15% on average and reduces the energy consumption under disruption scenarios by 8%–14%. The findings provide a realistic approach for manufacturing systems to meet production efficiency, equipment reliability, and sustainability goals while operating under uncertainty.

International Journal of Machine Design and Technology

Optimizing Sheet Metal Bending: A Comparative Study on Springback Characteristics of AISI 4130 and ASTM A653M

An Optimization Model for the Dynamic Production Scheduling Problem with Machine Failures and Energy Constraint