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Wear and fracture of steel dies employed in hot forging were evaluated through metallographic study with the aim of qualifying a Finite Element Simulation of the productive process. Apart from providing useful insights into the causes of die damaging, the simulation was exploited in a Design of Experiments to prevent fracture and to counter different mechanisms of wear. The objective is the optimization of die life acting only on process parameters that are directly adjustable in the actual industrial process. In the examination of stress distribution on the dies and the estimation of die wear, the complete forging cycle has been taken into consideration. Despite the considerable variability of the process, the study demonstrates that it is possible to prevent fracture insurgence and to increase the life expectancy of the die by a careful tuning of standard process parameters. Possible stakeholders of the study are not only process designers but also production managers, as most process parameters are modifiable during production.

The advent of new technologies and their implementation in manufacturing is accelerating the progress of Industry 4.0 (I4.0). Among the enabling technologies of I4.0, collaborative robots (cobots) push factory reconfiguration and prompt for worker empowerment by exploiting the respective assets of both humans and robots. Indeed, human has superior dexterity, flexibility, problem-solving ability. Robot excels in strength, endurance, accuracy and is expendable for risky activities. Therefore, task assignment problem in a production line with coexisting humans and robots cannot limit to the workload balancing among workers but should make the most of everyone respective abilities. The outcomes should not be only an increased productivity, but also improved production quality, human safety and well-being. Task assignment strategy should rely on a comprehensive assessment of the tasks from the viewpoint of suitability to humans or robots. As there are several conflicting evaluation criteria, often qualitative, the study defines the set of criteria, their metrics and proposes a method for task classification relying on Fuzzy Inference System to map each task onto a set of collaboration classes. The outcome of the study is the formal description of a set of evaluation criteria with their metrics. Another outcome is a Fuzzy Classification procedure that support production managers to properly consider all the criteria in the assignment of the tasks. The proposed methodology was tested on a case study derived from a manual manufacturing process to demonstrate its application during the process planning.