Papers by Author: Liang Gao

Abstract: Line heating is the main method for forming compound curved shells of hull. The accuracy of final deformation and the productivity depend on the experience of the workers. To predict the plate deformation, the explicit mathematical model for deformation and the main influencing factors by FEA and GEP is established in this paper. The main influencing factors in line heating process were analyzed firstly. Then, 16 group deformation results of steel plate under the five main influencing factors were obtained by FEA. At last, the explicit mathematical model for deformation and the main influencing factors was established.

Abstract: Line heating is a complex thermal-mechanical process as many factors affect the final shape of a processed plate. Generally, the temperature field of the processed plate determines the stress field and strain field. To predict the deformation of the processed plate, this paper investigates the effects of different factors on the temperature field during line heating by using design of experiment (DOE). Firstly, a three dimensional thermal elasto-plastic finite element method (FEM) is developed to calculate the temperature field induced by the single-pass oxygen-acetylene line heating. Secondly, the temperature field is analyzed by using fractional factorial design, in which the maximum temperature is selected as the response, and a fishbone diagram is used to overview all influencing factors. After performing a series of numerical experiments selected by using an ortho-gonal array, three main influencing factors are screened out: plate thickness, flow of acetylene and velocity of heat source. Next, the main effects of these factors are discussed. Finally, analytical re-sults indicate that there exist interaction effects among the three main influencing factors. This in-vestigation demonstrates that DOE is an efficient method for study of the temperature field during line heating.

Abstract: Process planning is an essential component of computer aided process planning (CAPP), which involves operations selection from design features and operations sequencing of these selected operations. It makes process planning a complex combinatorial optimization problem to conduct of these two steps simultaneously. In this paper, we propose a cooperative simulated annealing (CoSA) approach for the process planning problem to minimize total manufacturing cost. The proposed CoSA algorithm employed a novel optimization strategy different from all the existing approaches in the literature. Simulated annealing was utilized to optimize the four components of a process plan individually and sequentially. The approach is tested on two parts from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.

Abstract: Ship design is a complex engineering effort required excellent coordination between the various disciplines and essentially applies iteration to satisfy the relevant requirements, such as stability, power, weight, and strengths. Through, all-in-one Multidisciplinary Design Optimization (MDO) approach is proposed to get the optimum performance of the ship considering three disciplines, power of propulsion, ship loads and structure. In this research a Latin Hypercube Sampling (LHS) is employed to improve the space filling property of the design and explore it to sample data for covering the design space. To avoid the problem of huge calculation time and saving the develop time, a quadratic Response Surface Method (RSM) is adopted as an approximation model to study the relation between a set of design variables and the system output for solving the system design problems. A genetic algorithm (GA) is adopted as search technique used in computing to find exact or approximate solutions to optimize and search problems and appropriate design result in MDO in ship design. Finally, the validity of the proposed approach is proven by a case study of a bulk carrier.

Abstract: To realize the integration of process planning and scheduling (IPPS) in the manufacturing system, a particle swarm optimization (PSO) algorithm is utilized. Based on the general PSO (GPSO) model, one GPSO algorithm is projected to solve IPPS. In GPSO, crossover and mutation operations of genetic algorithm are respectively used for particles to exchange information and search randomly, and tabu search (TS) is used for particles’ local search. And time varying crossover probability and time varying maximum step size of tabu search are introduced. Experimental results show that IPPS can be solved by GPSO effectively. The feasibility of the proposed GPSO model and the significance of the research on IPPS are also demonstrated.

Abstract: This paper concentrates on the computational challenge in multidisciplinary design optimization (MDO) and a comprehensive strategy combining enhanced collaborative optimization (ECO) and kriging approximation models is introduced. In this strategy, the computational and organizational advantages of original collaborative optimization (CO) are inherited by ECO, which can satisfy the strengthened consistency requirements. Kriging approximation models are constructed to replace high-fidelity simulation models in individual disciplines and reduce the expensive computational cost in practical MDO problems. The proposed methodology is demonstrated by solving the classical speed reducer design problem. The better results indicate that ECO using kriging approximation models can achieve a considerable reduction of computational expense while guaranteeing the accuracy of optimal solutions with efficient convergence.

Abstract: In this paper we address a rectangular packing problem (RPP), which is one of the most difficult NP-complete problems. Borrowing from the respective advantages of the two algorithms, a hybrid of genetic algorithm (GA) and simulated annealing (SA) is developed to solve the RPP. Firstly, we adopt and improve Burke’s best-fit (BF) placement strategy, which is not restricted to the first shape but may search the list for better candidate shapes for placement. Secondly, we propose a new crossover operator, named Improved Precedence Operation Crossover (IPOX), which can preserve the valuable characteristics of the previous generation. At last, using a new temperature and iterations strategy and Boltzmann-type operator, we propose SA to re-intensify search from the promising solutions. The computational results validate the quality and the effectiveness of hybrid algorithm.

Abstract: Flexible job shop scheduling problem (FJSP) is an important extension of the classical job shop scheduling problem, where the same operation could be processed on more than one machine. It is quite difficult to achieve optimal or near-optimal solutions with single traditional optimization approach because the multi objective FJSP has the high computational complexity. An novel hybrid algorithm combined variable neighborhood search algorithm with genetic algorithm is proposed to solve the multi objective FJSP in this paper. An external memory is adopted to save and update the non-dominated solutions during the optimization process. To evaluate the performance of the proposed hybrid algorithm, benchmark problems are solved. Computational results show that the proposed algorithm is efficient and effective approach for the multi objective FJSP.

Abstract: Rapid prototyping (RP) is an innovative manufacturing technology. In recent years, the research to fabricate multi-material products by RP is becoming active. In this paper, we update the recent development of process planning for multi-material RP.

Abstract: Flexible job shop scheduling problem (FJSP) is an extended traditional job shop scheduling problem, which more approximates to real scheduling problems. This paper presents a multi-objective genetic algorithm (GA) based on immune and entropy principle to solve the multi-objective FJSP. In this improved multi-objective GA, the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Advanced crossover and mutation operators are proposed to adapt to this special chromosome structure. The proposed algorithm is evaluated on three representative instances and the computational results and comparison with some other approaches show that the proposed multi-objective algorithm is effective and potential.