Multiobjective Optimization for Integrated Tolerance Allocation and Fixture Layout Design in Multistation Assembly

Author(s):  
Zhijun Li ◽  
Michael Kokkolaras ◽  
Luis E. Izquierdo ◽  
S. Jack Hu ◽  
Panos Y. Papalambros

Cost and product quality are significant attributes in manufacturing processes, such as multistation assembly. We use multiobjective optimization for integrated tolerance allocation and fixture layout design to address their interaction and to quantify tradeoffs among cost, product quality, and assembly process robustness. Design decisions relate to product tolerances, assembly process tolerances, and fixture locating positions. A nested optimization strategy is adopted, and the proposed methodology is demonstrated using a vehicle side frame assembly example. The obtained results provide evidence for the existence of tradeoffs, based on which we can identify critical quality and budget requirements.

Author(s):  
Z. Li ◽  
L. E. Izquierdo ◽  
M. Kokkolaras ◽  
S. J. Hu ◽  
P. Y. Papalambros

Cost and dimensional variation of products are significant attributes in multistation assembly processes. These attributes depend on product∕process tolerances and fixture layouts. Typically, tolerance allocation and fixture layout design are conducted separately without considering potential interrelations. In this work, we use multiobjective optimization for integrated tolerance allocation and fixture layout design to address interactions and to quantify tradeoffs among cost, product variation, and assembly process sensitivity. A nested optimization strategy is applied to a vehicle side frame assembly. Results demonstrate the presence and quantification of tradeoffs, based on which we introduce the concept of critical variation and critical budget requirements.


Author(s):  
L. Eduardo Izquierdo ◽  
S. Jack Hu ◽  
Hao Du ◽  
Ran Jin ◽  
Haeseong Jee ◽  
...  

Reconfigurable assembly systems enable a family of products to be assembled in a single system by adjusting and reconfiguring fixtures according to each product. The sharing of fixtures among different products impacts their robustness to fixture variation due to trade offs in fixture design (to allow the accommodation of the family in the single system) and to frequent reconfigurations. This paper proposes a methodology to achieve robustness of the fixture layout design through an optimal distribution of the locators in a multistation assembly system for a product family. This objective is accomplished by (1) the use of a multistation assembly process model for the product family, and (2) minimizing the combined sensitivity of the products to fixture variation. The optimization considers the feasibility of the locator layout by taking into account the constraints imposed by the different products and the processes (assembly sequence, data scheme, and reconfigurable tools’ workspace). A case study where three products are assembled in four stations is presented. The sensitivity of the optimal layout was benchmarked against the ones obtained using dedicated assembly lines for each product. This comparison demonstrates that the proposed approach does not significantly sacrifice robustness while allowing the assembly of all products in a single reconfigurable line.


2012 ◽  
Vol 38 ◽  
pp. 1693-1703 ◽  
Author(s):  
M. Vasundara ◽  
K.P. Padmanaban ◽  
M. Sabareeswaran ◽  
M. RajGanesh

2018 ◽  
Vol 6 (5) ◽  
Author(s):  
Frederick Ray Gomez

The technical paper discusses the reduction of high leakage current failures of semiconductor IC (integrated circuit) packages by eliminating the ESD (electrostatic discharge) events during assembly process and ensuring the appropriate machine grounding and ESD controls.  It is imperative to reduce or ideally eliminate the leakage current failures of the device to ensure the product quality, especially as the market becomes more challenging and demanding.  After implementation of the corrective and improvement actions, high leakage current occurrence was reduced from baseline of 5784 ppm to 1567 ppm, better than the six sigma goal of 4715 ppm.


2021 ◽  
Vol 11 (18) ◽  
pp. 8379
Author(s):  
Seongmin Kim

A recent innovation in the trusted execution environment (TEE) technologies enables the delegation of privacy-preserving computation to the cloud system. In particular, Intel SGX, an extension of x86 instruction set architecture (ISA), accelerates this trend by offering hardware-protected isolation with near-native performance. However, SGX inherently suffers from performance degradation depending on the workload characteristics due to the hardware restriction and design decisions that primarily concern the security guarantee. The system-level optimizations on SGX runtime and kernel module have been proposed to resolve this, but they cannot effectively reflect application-specific characteristics that largely impact the performance of legacy SGX applications. This work presents an optimization strategy to achieve application-level optimization by utilizing asynchronous switchless calls to reduce enclave transition, one of the dominant overheads of using SGX. Based on the systematic analysis, our methodology examines the performance benefit for each enclave transition wrapper and selectively applies switchless calls without modifying the legacy codebases. The evaluation shows that our optimization strategy successfully improves the end-to-end performance of our showcasing application, an SGX-enabled network middlebox.


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Qing An ◽  
Jun Zhang ◽  
Xin Li ◽  
Xiaobing Mao ◽  
Yulong Feng ◽  
...  

The economical/environmental scheduling problem (EESP) of the ship integrated energy system (SIES) has high computational complexity, which includes more than one optimization objective, various types of constraints, and frequently fluctuated load demand. Therefore, the intelligent scheduling strategies cannot be applied to the ship energy management system (SEMS) online, which has limited computing power and storage space. Aiming at realizing green computing on SEMS, in this paper a typical SIES-EESP optimization model is built, considering the form of decision vectors, the economical/environmental optimization objectives, and various types of real-world constraints of the SIES. Based on the complexity of SIES-EESPs, a two-stage offline-to-online multiobjective optimization strategy for SIES-EESP is proposed, which transfers part of the energy dispatch online computing task to the offline high-performance computer systems. The specific constraints handling methods are designed to reduce both continuous and discrete constraints violations of SIES-EESPs. Then, an establishment method of energy scheduling scheme-base is proposed. By using the big data offline, the economical/environmental scheduling solutions of a typical year can be obtained and stored with more computing resources and operation time on land. Thereafter, a short-term multiobjective offline-to-online optimization approach by SEMS is considered, with the application of multiobjective evolutionary algorithm (MOEA) and typical schemes corresponding to the actual SIES-EESPs. Simulation results show that the proposed strategy can obtain enough feasible Pareto solutions in a shorter time and get well-distributed Pareto sets with better convergence performance, which can well adapt to the features of real-world SIES-EESPs and save plenty of operation time and storage space for the SEMS.


Author(s):  
Zhenyu Kong ◽  
Dariusz Ceglarek ◽  
Wenzhen Huang

Dimensional control has a significant impact on overall product quality and performance of large and complex multistation assembly systems. To date, the identification of process-related faults that cause large variations of key product characteristics (KPCs) remains one of the most critical research topics in dimensional control. This paper proposes a new approach for multiple fault diagnosis in a multistation assembly process by integrating multivariate statistical analysis with engineering models. The proposed method is based on the following steps: (i) modeling of fault patterns obtained using state space representation of process and product information that explicitly represents the relationship between process-related error sources denoted by key control characteristics (KCCs) and KPCs, and (ii) orthogonal diagonalization of measurement data using principal component analysis (PCA) to project measurement data onto the axes of an affine space formed by the predetermined fault patterns. Orthogonal diagonalization allows estimating the statistical significance of the root cause of the identified fault. A case study of fault diagnosis for a multistation assembly process illustrates and validates the proposed methodology.


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