| In a printed-circuit board assembly(PCBA)line,the pick-and-place(PAP)process of the surface mounter consumes the greatest amount of time and is considered as the bottleneck.Optimizing the PAP process of a single surface mounter and even multiple surface mounters in a PCBA line simplifies the PCBA tasks,and shortens the PCBA time.The high-speed multi-functional surface mounter is a kind of indispensable surface mounter specially designed for the high-mix and low-volume PCBA tasks.Optimizing all the objectives of the surface mounting optimization in a multi-functional placer remains a formidable challenge till now.As one of the most important intelligent manufacturing process management technology in the PCBA industry,the surface mounting optimization aims at properly sequencing and scheduling the PAP process of the surface mounting system so as to maximize the production efficiency.Under the same production conditions,a superior surface mounting optimization method can accomplish the same PCBA task in a much shorter period of time,improving the average PCBA time by several and even dozens of percent.This thesis is concerned with building an optimal PAP planning strategy for the multi-functional placer,which includes the following main contents:Firstly,this study detailedly illustrates the PAP process of the high-speed multifunctional surface mounter.Based on this,the surface mounting optimization objectives are introduced together with their related PAP operations.After analyzing the current decomposition methods,it is pointed out that the existing studies fail to simultaneously optimizing all the objectives since that the subproblems obtained by the current decomposition methods are still seriously coupled.The interrelationships between “promoting more simultaneous pickups”,one of the main objectives,and all the subproblems are especially complicated.This makes that the promotion of simultaneous pickups can only be realized in an indirect and passive manner.Following the “objective-driven decoupling”idea,the decomposition method based on division and reconfiguration is proposed.This helps to divide the original surface mounting optimization problem into the head task assignment problem(HTAP)and the PAP location routing problem(PAPLRP).The hierarchical decoupling between the main objectives and the secondary objectives is achieved for the first time.Specifically,the head task assignment problem covers all the main objectives.The concept of “component allocation in a single surface mounter” and “feeder set” are proposed,which can promote the simultaneous pickups in an active manner.This lays the foundation for the simultaneous optimization of all the objectives.A hierarchical multiobjective heuristic(HMOH)for the surface mounting optimization in a high-speed multi-functional surface mounter is proposed.The further decomposition strategy is applied to make the subproblems solvable by heuristics.Specifically,HTAP consists of the nozzle assignment and component allocation,while PAPLRP comprises place allocation,feeder set assignment,and place sequencing problems.Adhering strictly to the lexicographic method,the HMOH solves these subproblems in a descending order of importance of their involved objectives.Exploiting the expert knowledge,each subproblem is solved by an elaborately designed heuristic.The PAP location-routing result of the HMOH is further improved by the tabu search.Finally,the proposed HMOH realizes the complete and optimal PCBA decision making in real time.Using industrial PCB datasets,the superiority of HMOH is elucidated through comparison with the builtin optimizer of a widely used surface mounter in the market.The coordinated encoding genetic algorithm is then proposed to directly solve the HTAP and PAPLRP.The HTAP is directly treated as the component allocation problem,which decides the component type handled by each head per PAP cycle.The component allocation problem is a quadratic three-dimensional assignment problem(Q3AP)and effectively combines the optimization of all the main objectives.It is possible that one head stays idle,so the assigning two-dimensional locations are uncertain.We propose the cell division genetic algorithm(CDGA)to solve such a complex Q3 AP.Based on the clustering idea,the CDGA allocates a component cell as the basic unit.A cell chromosome decoding heuristic is designed to determine the next assigning head.By doing so,the problem dimension is reduced,so the GA can be used for searching the optimal component allocation formed by the current-generation cells.The cell division mechanism is proposed.By dividing each cell into two new cells at the right time and apply the new cells for the following optimization,further refined allocation can be obtained.The multi-dimensional chromosomes adapted for the surface mounting optimization in a highspeed multi-functional surface mounter are proposed for the first time.The multichromosome genetic algorithm(MCGA)is employed for solving the PAPLRP.In simulations using the industrial samples,the proposed algorithm significantly reduces the PCBA time compared to two recent studies and the built-in optimizer of a widely-used high-speed multi-functional surface mounter,which demonstrates its effectiveness and superiority.Since the component carrier is capacitated,the PAPLRP is a capacitated location routing problem.Accordingly,the place allocation and feeder set assignment subproblems are both optimization problems with multiple tours.With heuristics designed for optimization within a single tour,these two subproblems are transformed into the heuristic sequencing optimization problems that optimize the solving sequence of the tours.The typical HNN for the travelling salesman problem is improved,which results in the heuristic sequencing HNN(HSHNN).The energy function is adapted for reflecting the activation states of the energy matrix about the index-sequence pairs.Through exploration into the objective function value of each index-sequence pair,the energy matrix is calculated by a specially designed method combining with a normalization technique.To achieve optimal performance of the HNN-based methods,a multi-start mechanism is employed.The HSHNN exhibits superior optimization performance in PAP location routing compared with the HMOH and the MCGA.Finally,practical surface mounting experiments are carried out using a widely-used high-speed multi-functional surface mounter in the market.Under the same production conditions and accomplishing the same PCBA task,the most convincing comparison of PAP optimization performance is realized by directly comparing the ultimate goal,namely the PCBA time.The compared surface mounting optimization methods are HMOH,tabuimproved HMOH,the CEGA and the built-in PAP optimizer of the experimental platform surface mounter.The PAP experiments include the average optimization performance experiments assembling five relatively complex PCBs and the extreme optimization performance experiment assembling an extremely complex PCB.The comparing results show that the surface mounting optimization methods proposed in this thesis can be directly used for real production,which can generate obviously better PAP plans than the industrial benchmark.As a meta-heuristic,the CEGA has the best global searching ability,but is also accompanied by the longest computing time and the solution uncertainties.The HMOH can generate stable sub-optimal solutions in almost real time.Improved by the tabu search,the HMOH can largely achieve the similar performance of CEGA,and thus can be seen as a compromise between the HMOH and CEGA.The propose surface mounting optimization algorithms have superiority from different aspects and can be selected for use according to the need of production in a multi-functional surface mounter. |
