A new perspective for production process analysis using additive manufacturing—complexity vs production volume

Towards Digital Twin Implementation for Assessing Production Line Performance and Balancing

“…When data acquisition ends, the procedure provides for the evaluation of mean experimental cycle time (∆t µ ), and its standard deviation (σ), in order to compare them with the estimated cycle time (∆t theor ), according to inequality (7). As triggers per each operation are introduced in the acquisition, the values ∆tμ and σ have not been evaluated by using Equations (5) and (6), as suggested by the framework of Figure 1. As it is possible to consider the operations s-independent, ∆tμ, and σ have been evaluated by the following equations: The parameter ε, in this case study, has been considered equal to the 15% of ∆ttheor, i.e., 3 s. As triggers per each operation are introduced in the acquisition, the values ∆t µ and σ have not been evaluated by using Equations (5) and (6), as suggested by the framework of Figure 1.…”

“…As triggers per each operation are introduced in the acquisition, the values ∆tμ and σ have not been evaluated by using Equations (5) and (6), as suggested by the framework of Figure 1. As it is possible to consider the operations s-independent, ∆tμ, and σ have been evaluated by the following equations: The parameter ε, in this case study, has been considered equal to the 15% of ∆ttheor, i.e., 3 s. As triggers per each operation are introduced in the acquisition, the values ∆t µ and σ have not been evaluated by using Equations (5) and (6), as suggested by the framework of Figure 1. As it is possible to consider the operations s-independent, ∆t µ , and σ have been evaluated by the following equations: The parameter ε, in this case study, has been considered equal to the 15% of ∆t theor , i.e., 3 s. It is possible to assess the first level screening by using inequality (7), considering ∆t µ = 21.60 s, σ = 3.08 s, and ∆t theor = 20 s.…”

“…Furthermore, these technologies allow avoiding complex mathematical problems, such as Non-determistic Polynomial-time hard (NP-hard), typical for production line scheduling and balancing models [3]. This paper is focused on the use of IoT and simulation tools that, by integrating sensors and simulators for investigating human working performance [4], allow in-line adjustments and process control, enhancing a near-real time management of the production lines based on different kinds of production technologies [5]. IoT, despite the different visions of the scientific communities [6], is considered as a pervasive presence around us of a variety of things or objects (sensors, actuators, mobile phones, etc.…”

Does Reduction of Material and Energy Consumption Affect to Innovation Efficiency? The Case of Manufacturing Industry in South Korea

“…Previous literature looked into the methods of managing energy consumption, such as "demand response" which is a tactic for managing electrical loads of users [50]. Moreover, material and energy consumption is also considered as a key performance indicator in the research field of production technology [51][52][53]. Future research on the efficiency of such research field which includes the issue of material and energy consumption is believed to suggest great implications as well.…”

Optimal Order Policies for Dual-Sourcing Supply Chains under Random Supply Disruption

“…Ta et al [40] examined the critical role of trustworthiness in the financial supply chain relationships in the event of a contract breach. Fera et al [41] presented a novel approach to classify and analyze the production of specific products to mitigate supply chain risks by using additive manufacturing or subtractive manufacturing.…”