• Journal of the Korea Safety Management & Science
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• v.3 no.3
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• pp.221-230
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• 2001

The assembly tolerance design methods have applied linear or nonlinear programming methods and used simulation method and search algorithms to optimize the tolerance allocation of each part in an assembly. However, those methods are only considered to the relationship between tolerance and manufacturing cost, which do not consider a quality loss cost for each part tolerance. In this paper, the integrated simulation model used genetic algorithm and the Monte-Carlo simulation method was developed for the allocation of the optimal tolerance considering the manufacturing cost and quality loss cost.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.49
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• pp.33-41
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• 1999

The original tolerances, which are assigned by designers on the basis of handbooks and experience, cannot always be expected to be optimal or feasible, because they may yield an unacceptable manufacturing costs. So the systematic tolerance design considering manufacturing costs should be done. Therefore, this research analyzes the tolerance within the tolerance design using Monte-Carlo simulation method and sensitivity analysis and using genetic algorithm by tolerance allocation method. The genetic algorithm was developed for allocation of the optimal tolerance under the manufacturing limitation cost.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.2
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• pp.175-183
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• 1998

When a product is designed, tolerances must be given to the product so that required functions are guaranteed and production costs are minimized. In this research, a model is suggested which allocates tolerances to components optimally according to the STA(Statistical Tolerance Allocation) method. Taking into account the concept that dimensional errors have characteristics of statistical distributions, this model presents the discrete pseudo-boolean approach for the tolerance optimization by minimizing the tolerance cost and the quality loss cost. In this approach, two methods are proposed for the reduction of the problem scale; 1) a method for converting the minimization model for casts into the maximization model for cost savings, and 2) procedures to reduce the number of constraints and variables.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.224.1-224.1
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• 2005

• Korean Journal of Optics and Photonics
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• v.22 no.4
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• pp.161-169
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• 2011

In this paper, a tolerance allocation method using Monte-Carlo simulation with measured reflective eccentricity for high-sensitive IR optics is proposed. During optics fabrication and alignment, reflective eccentricity was measured using an optical centration measurement instrument. A Monte-Carlo simulation was performed using measured eccentricity data, and it gives statistical estimated performance of the optics after fabrication. The validity of the proposed tolerance allocation method was verified comparing the estimated MTF result with the measured MTF result of the fabricated optics.

• IE interfaces
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• v.16 no.spc
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• pp.39-44
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• 2003

As one of many design variables, the role of dimension tolerances is to restrict the amount of size variation in a manufactured feature while ensuring functionality. In this study, a nonlinear integer model has been modeled to allocate the optimal tolerance to each individual feature at a minimum manufacturing cost. While a normal distribution determines statistically worst tolerances with its symmetrical property in many previous tolerance allocation studies, a asymmetrical distribution is more realistic because its mean is not always coincident with a process center. A nonlinear integer model is modeled to allocate the optimal tolerance to a feature based on a beta distribution at a minimum total cost. The total cost as a function of tolerances is defined by machining cost and quality loss. After the convexity of manufacturing cost is checked by the Hessian matrix, the model is solved by the Complex Method. Finally, a numerical example is presented demonstrating successful model implementation for a nonlinear design case.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.901-905
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• 1992

The manual procedure of drafting engineering design is fast becoming obsolete. One of the important areas in CAPP is to automate tolerance assignment which is encountered in mechanical part design. This paper presents an approach for automating tolerance assignment using CAD database and tolerancing database in CAD drawing. The system is consisted of four major functions feasture extraction, feaure inferencing, rule-based tolerance allocation, and automatic upating. Auto CAD R.II is employed as CAD system and a computer progran is developed by using Auto LISP on PC-386.

• Korean Journal of Computational Design and Engineering
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• v.16 no.6
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• pp.397-406
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• 2011

Since tolerance allocation in a mobile phone camera manufacturing process greatly affects production cost and reliability of optical performance, a systematic design methodology for allocating optimal tolerances is required. In this study, we proposed the tolerance optimization procedure for determining tolerances that minimize production cost while satisfying the reliability constraints on important optical performance indices. We employed Latin hypercube sampling for evaluating the reliabilities of optical performance and a function-based sequential approximate optimization technique that can reduce computational burden and well handle numerical noise in the tolerance optimization process. Using the suggested tolerance optimization approach, the optimal production cost was decreased by 30.3 % compared to the initial cost while satisfying the two constraints on the reliabilities of optical performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1258-1275
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• 2023

Cloud computing is a new technology that has adapted to the traditional way of service providing. Service providers are responsible for managing the allocation of resources. Selecting suitable containers and bandwidth for job scheduling has been a challenging task for the service providers. There are several existing systems that have introduced many algorithms for resource allocation. To overcome these challenges, the proposed system introduces an Optimized Task Scheduling Algorithm with Deep Learning (OTS-DL). When a job is assigned to a Cloud Service Provider (CSP), the containers are allocated automatically. The article segregates the containers as' Long-Term Container (LTC)' and 'Short-Term Container (STC)' for resource allocation. The system leverages an 'Optimized Task Scheduling Algorithm' to maximize the resource utilisation that initially inquires for micro-task and macro-task dependencies. The bottleneck task is chosen and acted upon accordingly. Further, the system initializes a 'Deep Learning' (DL) for implementing all the progressive steps of job scheduling in the cloud. Further, to overcome container attacks and errors, the system formulates a Container Convergence (Fault Tolerance) theory with high-level security. The results demonstrate that the used optimization algorithm is more effective for implementing a complete resource allocation and solving the large-scale optimization problem of resource allocation and security issues.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2240-2254
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• 2021

We investigate the channel allocation problem in an ultra-dense device-to-device (D2D) enabled cellular network in underlaying mode where multiple D2D users are forced to share the same channel. Two kinds of low complexity solutions, which just require partial channel state information (CSI) exchange, are devised to resolve the combinatorial optimization problem with the quality of service (QoS) guaranteeing. We begin by sorting the cellular users equipment (CUEs) links in sequence in a matric of interference tolerance for ensuring the SINR requirement. Moreover, the interference quota of CUEs is regarded as one kind of communication resource. Multiple D2D candidates compete for the interference quota to establish spectrum sharing links. Then base station calculates the occupation of interference quota by D2D users with partial CSI such as the interference channel gain of D2D users and the channel gain of D2D themselves, and carries out the channel allocation by setting different access priorities distribution. In this paper, we proposed two novel fast matching algorithms utilize partial information rather than global CSI exchanging, which reduce the computation complexity. Numerical results reveal that, our proposed algorithms achieve outstanding performance than the contrast algorithms including Hungarian algorithm in terms of throughput, fairness and access rate. Specifically, the performance of our proposed channel allocation algorithm is more superior in ultra-dense D2D scenarios.