Cycle Time Estimation Research Articles

Job cycle time estimation in a wafer fabrication factory with a bi-directional classifying fuzzy-neural approach

Estimating the cycle time for every job in a factory is a critical task. It was recently reported that job classification noticeably enhanced the accuracy of job cycle time estimation. In pre-classifying approaches, whether the pre-classification approach combined with the subsequent estimation approach is suitable for the data is questionable. Conversely, the difficulty in classifying a job according to only the estimation error not the various attributes is a problem to post-classifying approaches. To tackle these problems, a bi-directional classifying fuzzy-neural approach is proposed in this study. In the proposed methodology, jobs are not only pre-classified but also post-classified. The results of pre-classification and post-classification are aggregated into a suitability index for each job. A job is then assigned to the category to which its suitability index is the highest. A radial basis function network is also constructed to predict the suitability index of a job according to the various attributes. To evaluate the effectiveness of the proposed methodology, a practical example was used in this study. According to experimental results, the estimation accuracy of the proposed methodology was significantly better than those of many existing approaches.

Simulation-based estimation of cycle time using quantile regression

Production cycle time is an important performance measure in manufacturing systems, and thus it is of interest to characterize distributional properties, such as quantiles, for informative decision making. This article proposes a non-linear quantile regression model for the relationship between stationary cycle time quantiles and corresponding throughput rates of a manufacturing system. The statistical properties of the estimated cycle time quantiles are investigated and the impact of dependent data from simulation output on parameter estimations is analyzed. Extensive numerical studies are presented to demonstrate the effectiveness of the proposed methods.

A compact microchip atomic clock based on all-optical interrogation of ultra-cold trapped Rb atoms

We propose a compact atomic clock that uses all-optical interrogation of ultra-cold Rb atoms that are magnetically trapped near the surface of an atom microchip. The interrogation scheme, which combines electromagnetically induced transparency with Ramsey’s method of separated oscillatory fields, can achieve an atomic shot-noise-level performance better than \(10^{-13}/\sqrt{\tau}\) for 106 atoms. A two-color Mach–Zehnder interferometer can detect a 100-pW probe beam at the optical shot-noise level using conventional photodetectors. This measurement scheme is nondestructive and therefore can be used to increase the operational duty cycle by reusing the trapped atoms for multiple clock cycles. Numerical calculations of the density matrix equations are used to identify realistic operating parameters at which AC Stark shifts are eliminated. By considering fluctuations in these parameters, we estimate that AC Stark shifts can be canceled to a level better than 2×10−14. An overview of the apparatus is presented with estimates of cycle time and power consumption.

A comparative study of truck cycle time prediction methods in open‐pit mining

PurposeThe purpose of this paper is to compare the predictive capability of three methods of truck cycle time estimation in open‐pit mining: computer simulation, artificial neural networks (NNs), and multiple regressions (MRs). The aim is to determine the best method. The most common method currently used is computer simulation.Design/methodology/approachTruck cycle times at a large open pit mine are estimated using computer simulation, artificial NNs, and MRs. The estimated cycle times by each method are in turn compared to the actual cycle times recorded by a computerized mine monitoring system at the same mine. The errors associated with each method relative to the actual cycle times are documented and form the basis for comparing the three methods.FindingsThe paper clearly indicates that computer simulation methods used in predicting truck cycle times in open‐pit mining underestimate and overestimate the results for short and long hauls, respectively. It appears that both NN and regression models are superior in their predictive abilities compared to computer simulations.Research limitations/implicationsThe cycle time prediction models developed apply to a specific mine site and one has to be careful not to directly apply these models to other operations.Practical implicationsThe paper describes the implementation of regression and NN modelling. An opportunity exists for mines to utilise the large volumes of data generated to predict truck haulage cycle times more accurately and hence, improve the quality of mine planning.Originality/valueThe paper addresses an area of need in the mining industry. Accurate prediction of cycle times is critical to mine planners as it impacts on production targets and hence, the budgets.

Estimation of product development cycle time resulting from early supplier involvement

Early supplier involvement (ESI) is widely applied to improve the product development process and has been proved to be able to shorten product development cycle time. The current study presents a systematic approach to support enterprises in selecting suitable tasks for implementing ESI. In order to estimate the influence of ESI on product development cycle time, the proposed method analyses complex product development processes to determine how to implement collaborative design appropriately via design structure matrix. This approach integrates an analysis of the binary design structure matrix, two-dimensional variables (sensitivity and variability), and the amount of overlapping of the tasks to discuss complex issues associated with product development cycle time. By applying the influence analysis to overlapping, the study identifies suitable tasks and the priority of each task to collaborative design. Finally, a simplified case study is discussed to demonstrate how enterprises can utilize the proposed method.

Statistical Approach for Cycle Time Estimation in Semiconductor Packaging Factories

In the semiconductor industry, to enhance customer satisfactions and ability of quick responses, the development of cycle time estimation model is very important. Cycle time estimation is an essential planning basis, which has many applications, especially on the analyses of performance indexes, capacity planning, and the assignments of due dates. In this paper, we provide a statistical approach for cycle time estimation in semiconductor plastic ball grid array (PBGA) packaging factories. Due to today's fierce competitive environments in the semiconductor industry, planners involved in PBGA packaging factories need an approach to obtain estimated cycle times with different confidence to ensure the due date assignments more accurately. Therefore, upper confidence bounds of estimated cycle times at various confidence coefficients are also presented in this paper. We demonstrate the applicability of the proposed cycle time estimation model incorporating the upper confidence bounds by presenting a real-world example taken from a PBGA packaging shop floor in a semiconductor packaging factory located in the Science-Based Industrial Park in Hsinchu, Taiwan.

A FUZZY-NEURAL FLUCTUATION SMOOTHING RULE FOR SCHEDULING JOBS WITH VARIOUS PRIORITIES IN A SEMICONDUCTOR MANUFACTURING FACTORY

A fuzzy-neural fluctuation smoothing rule is proposed in this study to improve the performance of scheduling jobs with various priorities in a semiconductor manufacturing factory. The fuzzy-neural fluctuation smoothing rule is modified from the well-known fluctuation smoothing rule by improving the accuracy of estimating the remaining cycle time of a job, which is done by applying Chen's fuzzy-neural approach with multiple buckets. To evaluate the effectiveness of the proposed methodology, production simulation is also applied in this study. According to experimental results, incorporating a more accurate remaining cycle time estimation mechanism did improve the scheduling performance especially in reducing the average cycle times. Besides, the fuzzy-neural fluctuation smoothing rule was also shown to be a Pareto optimal solution for scheduling jobs with various priorities in a semiconductor manufacturing factory.

Cycle Time Estimation of a Multiple Product Production Line: An Approach With Fuzzy and Chance-Constrained Programming

This paper presents an approach to estimate the cycle time of a multi-product production line composed of workstations that are subject to random failure and repair. We utilize chance constrained programming (CCP) to establish an upper bound for the stochastic variables and approximate them with interval variables. Fuzzy linear programming (FLP) is utilized to incorporate the interval variables into the optimization model. We discuss and utilize some of the existing methods of determining production line efficiency as the line efficiency is a component of our proposed model. Numerical examples are presented and the results obtained by the modeling approach are then compared and validated with simulation results.

Job remaining cycle time estimation with a post-classifying fuzzy-neural approach in a wafer fabrication plant: A simulation study

A post-classifying fuzzy-neural approach is proposed in this study for estimating the remaining cycle time of each job in a wafer fabrication plant, which has seldom been investigated in past studies but is a critical task for the wafer fabrication plant. In the methodology proposed, the fuzzy back-propagation network (FBPN) approach for job cycle time estimation is modified with the proportional adjustment approach to estimate the remaining cycle time instead. Besides, unlike existing cycle time estimation approaches, in the methodology proposed a job is not preclassified but rather post-classified after the estimation error has been generated. For this purpose, a back-propagation network is used as the post-classification algorithm. To evaluate the effectiveness of the methodology proposed, production simulation is used in this study to generate some test data. According to experimental results, the accuracy of estimating the remaining cycle time could be improved by up to 64 per cent with the proposed methodology.

Levels of Capacity and Material Handling System Modeling for Factory Integration Decision Making in Semiconductor Wafer Fabs

As the costs of building a new wafer fab increase, a detailed simulation model representing the production operations, the tools, the automated material handling systems (AMHS), and the tool-AMHS interactions is needed for accurately planning the capacity of these facilities. The problem is that it currently takes too long to build, experiment, and analyze a sufficiently detailed model of a fab. The key for building accurate and computationally efficient fab models is to decide on the right amount of model details, specifically those details representing the equipment capacity and the AMHS. This paper identifies a method for classifying a fab model by the level of capacity detail, the level of AMHS detail, or the level of capacity/AMHS detail. Within the capacity/ AMHS modeling level, our method further differentiates between detailed integrated capacity/AMHS models and abstract coupled capacity/AMHS models. The proposed classification method serves as the basis of a framework that helps users select the system components to be modeled within a desired level of detail. This research also provides a review of past-published literature summarizing the work done at each of the proposed fab modeling levels. A case study comparing the performance between an integrated capacity/AMHS model and a coupled capacity/AMHS model is presented. The study demonstrates that the coupled model generates cycle time estimates that are not statistically different than those generated by the integrated model. This paper also shows that the coupled model can improve CPU time by approximately 98% in relation to the integrated model.

Lot cycle time prediction in a ramping-up semiconductor manufacturing factory with a SOM–FBPN-ensemble approach with multiple buckets and partial normalization

“Ramp-up” is the process of increasing the production rate of a semiconductor manufacturing factory from the first lot to whole volume. Lot cycle time estimation and control during production ramp-up in a semiconductor factory is even more important. However, in addition to the large fluctuation, there are also trends and jumps (sudden increases) in the lot cycle time, which makes it a very challenging task. A SOM–FBPN-ensemble approach with multiple buckets and partial normalization is therefore proposed in this study for lot cycle time prediction in a ramping-up semiconductor manufacturing factory, which was seldom investigated in the past studies. The proposed methodology is composed of two parts. In the first part, the multiple-bucket approach is applied to consider the ramp-up plan of the semiconductor manufacturing factory. Subsequently, the SOM–FBPN-ensemble approach is applied to predict the cycle time of every lot in the ramping-up semiconductor manufacturing factory. The buckets obtained in the first part become additional inputs to the SOM–FBPN, and every parameter is normalized into a range narrower than [0, 1] to reflect the difference between the future and the past. To evaluate the effectiveness of the proposed methodology, a production simulation model was employed in this study. According to experimental results, the prediction accuracy of the proposed methodology was significantly better than those of many existing approaches.

A fuzzy-neural and multiple-bucket approach for estimating lot cycle time in a wafer fab with dynamic product mix

A fuzzy-neural and multiple-bucket approach is proposed in this study for lot cycle time estimation in a wafer fab with dynamic product mix, which was seldom thoroughly investigated in the past studies. The proposed methodology is composed of two parts. In the first part, the multiple-bucket approach is applied to consider the future release plan of the wafer fab. Subsequently, the FCM-FBPN approach is applied to estimate the cycle time of every lot in the wafer fab. The buckets obtained in the first part become additional inputs to the FBPN. In this way, the fluctuation in the product mix since the release of a wafer lot can be considered in estimating the cycle time of the wafer lot. According to experimental results, the estimation accuracy of the proposed methodology was significantly better than those of many existing approaches. Other findings include that a large number of buckets were beneficial to the estimation accuracy, and might not worsen the efficiency of the proposed methodology.

Evaluation and modeling of productivity and dynamic capability in high-speed machining centers

The current standards for machine tool performance evaluation do not consider productivity and dynamic capability at high feed rates. Therefore, industry does not have a basis for machine tool comparison. Testing was conducted with the Heidenhain KGM 181 grid encoder to record the actual path of the machine at high feed rates (up to 16 m/min). This paper shows the significance of linear vs. circular interpolation, acceleration and rate of change in acceleration (jerk) on the productivity and dynamic capability of this type of machine. Under high feed rate interpolations, the actual cycle time can differ significantly from the ideal cycle time (calculated with the traveled distance and programmed feed rate). A modeling approach with simple testing protocols is presented in order to predict the cycle time during machining with high feed rates. The proposed modeling approach provides less than 6% error in estimating cycle time.

Process-based Cycle Time Estimation for the Thermoplastic Tape Placement

In order to meet manufacturing process decisions, it is necessary to know the expected production costs. Unlike the thermoset tape placement, the thermoplastic tape placement is not yet available for industrial applications other than the aircraft sector. Therefore, there is insufficient production data available for the thermoplastic tape placement concerning the cycle time in relation to part geometry, weight, etc. Compared to other composite manufacturing techniques the process time plays a big role due to the exorbitant placement time. As a result, the previously developed cost estimation approaches cannot fulfill this demand because these models are not suitable for qualitative analysis of the elements and shape complexity. Therefore, the determination of the process time, which depends on component complexity, is only available as an inconsistent function and gives relatively fuzzy statements about the manufacturing costs. The method of the production cost estimation introduced here is a steady function based on the process-physics of the thermoplastic tape placement. The result is that, the complexity of the component and manufacturing system is transferred into the cycle time which is solely based on the measurable process parameters. Furthermore, the verification procedure of the theoretical attempt is introduced and discussed.

Real-time monitoring of an industrial batch process

This paper describes the development of a real-time monitoring system for a batch process operated by Aroma and Fine Chemicals Limited. The process shares many similarities with other batch processes in that cycle times can vary considerably, instrumentation is limited and inefficient laboratory assays are required to determine the end-point of each batch. The aim of the work conducted in this study was to develop a data driven system to accurately identify the end-point of the batch. This information can then be used to reduce the overall cycle time of the process. Novel approaches based upon multivariate statistical techniques are shown to provide a soft sensor that can estimate the product quality throughout the batch and provide a long-term estimate of the likely cycle time. This system has been implemented on-line and initial results indicate that it offers potential to significantly reduce operating costs.

Factory Cycle-Time Prediction With a Data-Mining Approach

An estimate of cycle time for a product in a factory is critical to semiconductor manufacturers (and in other industries) to assess customer due dates, schedule resources and actions for anticipated job completions, and to monitor the operation. Historical data can be used to learn a predictive model for cycle time based on measured and calculated process metrics (such as work-in-progress at specific operations, lot priority, product type, and so forth). Such a method is relatively easy to develop and maintain. Modern data mining algorithms are used to develop nonlinear predictors applicable to the majority of process lots, and three methods are compared here. They are compared with respect to performance in actual manufacturing data (to predict times for both final and intermediate steps) and for the feasibility to maintain and rebuild the model.

THE DESIGN OF MASTER PRODUCTION SCHEDULING SYSTEM FOR BALL GRID ARRAY PACKAGING PROCESS

ABSTRACT The wafer bumping process in Ball Grid Array (BGA) packaging has three characteristics: long machine setup time, dynamical arrival of orders, and re-entrance of operations. These three properties make a factory with BGA packaging difficult to maximize its capacity utilization while having all orders meeting their due dates. Therefore, we design a master production scheduling (MPS) system for wafer bumping process which aims for having high due date achievement as well as doing customer order promising. The MPS system first estimates the cycle time roughly. Then a mixed integer programming (MIP) model is applied to optimize the setup schedule. With estimated cycle times and setup schedules, the master production scheduling system can plan master production schedules wich are capable to meet the due date requirement and can handle the task of customer order promising. The case studies show that this MPS system generates feasible master production schedules with high due date achievement.

Modelling performance and retarder chart of off-highway trucks by cubic splines for cycle time estimation

Cycle time is a crucial parameter for determining the number of trucks in the transportation system of an open pit mine. During the development of surface mining projects, the haulage roads are planned and the road conditions are predicted. Accordingly, the time that the trucks may spend on that haulage road is estimated. The performance and retarder charts installed within the cabs of the trucks are widely used to calculate the speeds and the time spent for particular road conditions. In this study, a computer-aided system is developed to estimate the truck speed, cycle time and truck number. Speed data for different resistances are read from truck performance and retarder charts and used to build a database. The database with total resistance and speed pairs is modelled by the cubic spline interpolation method. After modelling, the developed system could estimate the speed for any given total resistance. As the road distance is known, the time that any truck would spend travelling on this haul road may be calculated. The approach is coded in C++ and applied to a real overburden stripping mine panel. The results obtained show that the system is convenient for chart modelling and cycle time calculation. Also, the developed system can be used as a part of truck dispatching and simulation system.

Describing gait as a sequence of states

Traditionally, gait analysis has been based on normalizing the stride time to a percentage and then averaging several strides measured under the same conditions. This procedure relies on the questionable assumptions that gait is a cyclic movement with superimposed noise and that there is no variability in the timing of activation or in the angles within the stride so no rescaling occurs during the percentage conversion. However, there is a fluctuation in the timings at which the peak values occur. A typical hallmark of this time-rescaling is the increase of the joint angle standard deviation when the angular velocity increases. The goal of this paper is to present a description of gait to avoid averaging without distorting the original curves. In addition, it allows the analysis of the fluctuation between consecutive strides. In this method, it is assumed that gait is quasi-periodic. The key point is the representation of gait by a state vector that evolves in time. This state vector can be used to calculate the instantaneous period and provides a measure of the time fluctuations between strides. The sequence of states method describes a quasi-periodic movement like gait with a continuous estimate of cycle time and provides measure of the deviations between cycles.

New cycle time and space estimation models for automated storage and retrieval system conceptualization

New analytical models supporting the design conceptualization of automated storage and retrieval systems are proposed. They include an analytical cycle time model for automated storage and retrieval systems using dedicated storage and computationally efficient procedures for estimating space requirements with systems using randomized or class-based storage. These models fill two gaps in the existing literature focused on analysing space and cycle time tradeoffs for alternative system configurations defined by the number of storage aisles, rows and bays. They are illustrated through a sample problem.