As an important branch of machine learning, Monte Carlo learning has been successfully applied to engineering design optimization and product predictive analysis, such as design optimization of heavy machinery. However, the accuracy of the classical Monte Carlo algorithm is not high enough, and the existing improved Monte Carlo algorithm has a complex calculation process and difficult parameter control. In this paper, the Monte Carlo method based on boundary point densification is proposed to calculate workspace. This paper takes the calculation of 2000T offshore crane workspace as an example to verify the effectiveness and practicability of the algorithm. The D-H method is used to establish the workspace model of the offshore crane. The calculation method of crane workspace based on the Monte Carlo learning method with increased boundary point density is discussed in detail, and the correctness of crane workspace is verified. The steps of the algorithm include generate the basic space, extract and draw the boundary, increase the density of boundary points, and cyclic. The rationality of the method is proved by comparing the simulation results with the design experience and calculated values.
With the increasingly serious global climate problem, the low-carbon design of products aiming at reducing the carbon emission in the life cycle has gradually become an important direction of sustainable manufacturing. The optimization design of product structure is one of the important means to realize the low-carbon operation of product structure. Therefore, how to carry out the low-carbon optimization design of product is the focus of this paper. The dynamics analysis of product digital model can provide effective help for product structure optimization. It provides simulation results of the whole life cycle of products by establishing digital virtual prototyping model of complex system based on multi-body dynamics theory. In this paper, a new framework of low carbon manufacturing is constructed, a digital model of 2000 tons offshore crane is constructed by using virtual prototype technology and dynamic load analysis is carried out. Based on the dynamic simulation and load analysis of the offshore platform crane, the low-carbon optimization design of the offshore platform pile leg is carried out, and the carbon emission of the whole life cycle is optimized, and the feasibility of the scheme is verified.
In this work, zwitterionic polyacrylonitrile (PAN)-based membranes were synthesized via surface grafting strategy for improving the antifouling properties. The copolymer membrane consisting of PAN and poly(hydroxyethyl methacrylate) segments, was cast via nonsolvent induced phase separation, and then treated with acryloyl chloride to tether with carbon-carbon double bonds. Zwitterionic poly(sulfobetaine methacrylate) (PSBMA) layers were grafted onto membrane surface via concerted reactions of radical grafting copolymerization and quaternization with 2-(dimethylamino)ethyl methacrylate) and 1, 3-propanesultone (1, 3-PS) as the monomers. The grafting degree (GD) of PSBMA layers increases with the incremental content of monomers, leading to the enhancement in membranes surface hydrophilicity. The permeation experiments show that the flux of the zwitterionic membrane increases and then decreases with the increasing GD value, because of the surface coverage of PSBMA layers. The zwitterionic membrane has excellent separation efficiency for oil-in-water emulsion, with the rejection of a higher value than 99%. The irreversible membrane fouling caused by oil adsorption has been suppressed, as proved by the cycle-filtration tests. These outcomes confirm that oil-fouling resistances of membranes are improved obviously by the surface grafting of zwitterionic PSBMA layers.
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