Optimizing the Tolerance for the Products with Multi-Dimensional Chains via Simulated Annealing

The assembly is the last process of controlling the product quality during manufacturing. The installation guidance should provide the appropriate assembly information, e.g., to specify the components in each product. The installation guidance with low quality results in rework or the resource waste from the failure products. This article extends the dimensional chain assembly problem proposed by Tsung et al. to consider the multiple dimensional chains in the product. Since there are multiple dimensional chains in a product, the installation guidance should consider inseparability and acceptability as computing the installation guidance. The inseparability means that the qualities of all dimensional chains in the part should be evaluated together without separation, while the acceptability stands for that the size of each product should be satisfied with the specification. The simulated annealing (SA) algorithm is applied to design the assembly guidance optimizer named as AGOMDC to compute the assembly guidance in the dimensional chain assembly problem with multiple dimensional chains. Since SA has high performance in searching neighbor solutions, the proposed approach could converge rapidly. Thus, proposed AGOMDC could be applied in real-world application for the implementation consideration. The simulations consist of two parts: the feasibility evaluation and the algorithm configuration discussion. The first part is to verify the inseparability and acceptability that are the hard constraints of the assembly problem for the proposed AGOMDC, and the second one is to analyze the algorithm configurations to calculate the assembly guidance with 80% quality. The simulation results show that the inseparability and acceptability are achieved, while the proposed AGOMDC only requires more than two seconds to derive the results. Moreover, the recommended algorithm configurations are derived for evaluate the required running time and product quality. The configurations with product quality 80% are that the temperature descent rate is 0.9, the initial temperature is larger than 1000, and the iteration recommended function is derived based on the problem scale. The proposed AGOMDC not only helps the company to save the time of rework and prevent the resource waste of the failure products, but is also valuable for the automatic assembly in scheduling the assembly processes.

Assembly Wirebond Process Solution for Mitigating Leadframe Bouncing on Multi-Die QFN Device

Modification and improvement of an existing tooling design in semiconductor packaging industry has been a usual practice, to enhance the current setup and to provide a solution to a specific assembly problem. This paper discusses the solution in eliminating the smashed ball defect occurrence observed after wirebond process. Smashed ball is usually encountered if the unit is unstable and creates a bouncing effect during wirebond process. It is therefore important to mitigate this micro-bouncing effect by analyzing the package design and the window clamp and top plate (WCTP). The objective is to increase the stability of the unit during wirebonding, especially for quad-flat no-leads (QFN) package with no tape. To achieve this, the solution is to alter the vacuum hole design of the top plate from single hole per unit to multiple holes of varied sizes per unit. Ultimately, after changing the design of the top plate, the micro-bouncing encountered during wirebond process was significantly reduced. This in turn created a consistent ball formation in all bonded wires. The comparative data presented in this paper confirmed the effectivity of the redesigned WCTP. For future works and studies, the improvement and learnings could be used on devices with comparable configuration.

DEVELOPMENT OF 3D PIN-BY-PIN CORE SOLVER TORTIN AND COUPLING WITH THERMAL-HYDRAULICS

Currently, safety analyses mostly rely on codes which solve both the neutronics and the thermal-hydraulics with assembly-wise nodes resolution as multiphysics heterogeneous transport solvers are still too time and memory expensive. The pin-by-pin homogenized codes can be seen as a bridge between the heterogeneous codes and the traditional nodal assembly-wise calculations. In this work, the pin-by-pin simplified transport solver Tortin has been coupled with a sub-channel code COBRA-TF. The verification of the 3D solver of Tortin is presented at first, showing very good agreement in terms of axial and radial power profile with the Monte Carlo code SERPENT for a small minicore and with the state-of-the-art nodal code SIMULATE5 for a quarter core without feedback. Then the results of Tortin+COBRA-TF are compared with SIMULATE5 for one assembly problem with feedback. The axial profiles of power and moderator temperature show good agreement, while the fuel temperature differ by up to 40 K. This is caused mainly by different gap and fuel conductance parameters used in COBRA-TF and in SIMULATE5.

A Two-Stage Three-Machine Flow Shop Assembly Problem Mixed with a Controllable Number and Sum-of-Processing Times-Based Learning Effect by Simulated Annealing Algorithms

The two-stage assembly scheduling problem is widely used in industrial and service industries. This study focuses on the two-stage three-machine flow shop assembly problem mixed with a controllable number and sum-of-processing times-based learning effect, in which the job processing time is considered to be a function of the control of the truncation parameters and learning based on the sum of the processing time. However, the truncation function is very limited in the two-stage flow shop assembly scheduling settings. Thus, this study explores a two-stage three-machine flow shop assembly problem with truncated learning to minimize the makespan criterion. To solve the proposed model, we derive several dominance rules, lemmas, and lower bounds applied in the branch-and-bound method. On the other hand, three simulated annealing algorithms are proposed for finding approximate solutions. In both the small and large size number of job situations, the SA algorithm is better than the JS algorithm in this study. All the experimental results of the proposed algorithm are presented on small and large job sizes, respectively.