Fatigue evaluation in maintenance and assembly operations by digital human simulation

Virtual human techniques have been used a lot in industrial design in order to consider human factors and ergonomics as early as possible. The physical status (the physical capacity of virtual human) has been mostly treated as invariable in the current available human simulation tools, while indeed the physical capacity varies along time in an operation and the change of the physical capacity depends on the history of the work as well. Virtual Human Status is proposed in this paper in order to assess the difficulty of manual handling operations, especially from the physical perspective. The decrease of the physical capacity before and after an operation is used as an index to indicate the work difficulty. The reduction of physical strength is simulated in a theoretical approach on the basis of a fatigue model in which fatigue resistances of different muscle groups were regressed from 24 existing maximum endurance time (MET) models. A framework based on digital human modeling technique is established to realize the comparison of physical status. An assembly case in airplane assembly is simulated and analyzed under the framework. The endurance time and the decrease of the joint moment strengths are simulated. The experimental result in simulated operations under laboratory conditions confirms the feasibility of the theoretical approach

Multi-step time series prediction intervals using neuroevolution

Multi-step time series forecasting (TSF) is a crucial element to support tactical decisions (e.g., designing production or marketing plans several months in advance). While most TSF research addresses only single-point prediction, prediction intervals (PIs) are useful to reduce uncertainty related to important decision making variables. In this paper, we explore a large set of neural network methods for multi-step TSF and that directly optimize PIs. This includes multi-step adaptations of recently proposed PI methods, such as lower--upper bound estimation (LUBET), its ensemble extension (LUBEXT), a multi-objective evolutionary algorithm LUBE (MLUBET) and a two-phase learning multi-objective evolutionary algorithm (M2LUBET). We also explore two new ensemble variants for the evolutionary approaches based on two PI coverage--width split methods (radial slices and clustering), leading to the MLUBEXT, M2LUBEXT, MLUBEXT2 and M2LUBEXT2 methods. A robust comparison was held by considering the rolling window procedure, nine time series from several real-world domains and with different characteristics, two PI quality measures (coverage error and width) and the Wilcoxon statistic. Overall, the best results were achieved by the M2LUBET neuroevolution method, which requires a reasonable computational effort for time series with a few hundreds of observations.This article is a result of the project NORTE-01- 0247-FEDER-017497, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We would also like to thank the anonymous reviewers for their helpful suggestionsinfo:eu-repo/semantics/publishedVersio