Transportation flexibility in manufacturing factories can be improved by introducing a function that allows the operator to physically interact with autonomous mobile robots (AMRs.) In our previous study, a manual operation mode was implemented with a sensorless power-assist scheme according to impedance control based on the touching force of the operator on a stationary AMR. In this paper, we propose a mode-switching algorithm for an AMR that switches from autonomous navigation to manual operation. Control-mode switching is achieved by detecting the body sway caused by the touching force of the operator. The experimental results demonstrate that the control mode can be successfully switched adaptively based on the intention of the operator, without any additional sensors.
Process harmonics often need to be eliminated for accurate regenerative chatter diagnosis. To eliminate the harmonics with many band-stop characteristics, this paper uses a periodic/aperiodic separation filter (PASF) and proposes a chatter detection algorithm based on the PASF. The PASF can design the cutoff frequency and time constant, directly, and the high-order PASF improves the harmonics elimination performance without deteriorating chatter signal extraction performance. The experiments validated the proposed algorithm under several conditions of spindle speeds, cutting types, thresholds, and cutoff frequencies. Their results showed the robustness of the proposed chatter detection algorithm against the threshold selection and change of cutting conditions.
Recently, many wearing simulation systems, which do not need samples of clothes or actual trying on them, have been developed to improve the productivity of clothes. However, in the case of knitted clothes, conventional systems only offer a looking not based on mechanical consideration at the stitch level because such consideration leads to a significant increase in computational cost. In this paper, we propose a shape prediction method for knitted stitches using machine learning. First, a yarn is modeled as a structure with straight springs, rotating springs, and torsion springs. By minimizing the potential energy of yarns, the stable shape of a stitch can be derived. Next, using such shapes as training data, machine learning was performed with nonlinear neural networks. Then, various shapes of the stitch can be predicted without time-consuming optimization. Our proposed method will be useful for precise wearing simulation of knitted clothes.
In recent years, electric power consumption and costs have become important evaluation indicators as well as productivity in the manufacturing industry due to efforts for SDGs and an increase in electricity charges. In addition, the globalization of the manufacturing industry requires faster decision-making and faster implementation of measures to gain customers. Along with this, production schedules that simultaneously consider indicators related to productivity and electricity are required, and it is necessary to develop a scheduling method that can quickly reflect the multiple requirements. In this paper, we propose the scheduling method using the satisficing trade-off method that satisfies the requests of the schedule planners for productivity and electric power costs quickly, and evaluate the effectiveness of the proposed method by computer experiments.