Search Results (2)

This paper describes the optimal path planning of a redundant finishing mechanism developed for joint space-based additive-finishing integrated manufacturing (AFM). The research motivation results from an inevitable one-sided layout of a finishing stage (FS) with regard to the additive stage (AS) in the AFM. These two stages share a 2-dof bed stage (BS), and the FS can lightly shave off the rough-surfaced 3D print on the bed. Since the FS located at the side of the AS cannot reach all the target points of the 3D print, the bed should be able to rotate the 3D print about the z-axis and translate it in the z-axis. As a result, the AS has 4-dof joints for 2P and 1P1R during the additive process with AS-BS, and FS has 4-dof and 2-dof integrated joints for 2P2R and 1P1R during the finishing process with FS-BS, respectively. For the kinematic modeling of the FS part and the BS, the virtual linkage connecting the bed frame origin and the FS’s joint frame for approaching the BS is considered to realize seamless kinematic redundancy. The minimum Euclidian norm of the joint velocity space is the objective function to find the optimal joint space solution for a given tool path. To confirm the feasibility of the developed joint path planning algorithm in the proposed FS-BS mechanism, layer-by-layer slicing of a given 3D print’s CAD model and tool path generation were performed. Then, the numerical simulations of the optimal joint path planning for some primitive 3D print geometries were conducted. As a result, we confirmed that the maximum and mean pose error in point-by-point only, with the developed optimal joint path planning algorithm, were less than 202 nm and 153 nm, respectively. Since precision and general machining accuracies in tool path generation are in the range of ±10 μm and 20 μm, the pose error in this study fully satisfies the industry requirements. Full article

In this study, we consider a production lot-sizing and scheduling problem found in the fruit juice production industry from an enhanced inventory management perspective. The problem can be classified as a P2SMM (two-stage multi-machine lot-scheduling) problem. We extended the classical P2SMM problem by incorporating an additional inventory management aspect of finished products to reflect a possible real-life case problem, specifically regarding the shelf-life concept and limited warehouse capacity, with a possibility of outsourcing the warehousing demand to a third-party logistics company. We developed the mixed integer linear programming (MILP) model to fully represent the considered problem (due to the NP-hard nature of the problem, only small-scale instances could be solved to optimality), and the hybrid variable neighborhood search with linear programming (VNS/LP) model to solve both small and real-life large-scale problem instances. The goal of the developed models is to minimize total costs that consist of the cost of backordering, the cost of planned minimum and maximum stock level violation, the cost of warehouse capacity overflow, the costs of production setup time and unused available production time. The main idea of the VNS/LP model is to solve the scheduling segment of P2SMM (the production sequence) via a VNS heuristic, and the lot-sizing segment of P2SMM via the linear programming (LP) model. Based on the results from five variants of the problem setup, a potential decision maker can have an overview of the impact of different important input parameters (production time costs, warehouse capacity and costs, inventory related costs and production demand) on the total cost of a production process and improve its efficiency in changing conditions. Full article