Modeling Of Manufacturing Processes Research Articles

Handling uncertainties in modelling manufacturing processes with hybrid swarm intelligence

Seldom has research regarding manufacturing process modelling considered the two common types of uncertainties which are caused by randomness as in material properties and by fuzziness as in the inexact knowledge in manufacturing processes. Accuracies of process models can be downgraded if these uncertainties are ignored in the development of process models. In this paper, a hybrid swarm intelligence algorithm for developing process models which intends to achieve significant accuracies for manufacturing process modelling by addressing these two uncertainties is proposed. The hybrid swarm intelligence algorithm first applies the mechanism of particle swarm optimisation to generate structures of process models in polynomial forms, and then it applies the mechanism of fuzzy least square regression algorithm to determine fuzzy coefficients on polynomials so as to address the two uncertainties, fuzziness and randomness. Apart from addressing the two uncertainties, the common feature in manufacturing processes, nonlinearities between process parameters, which are not inevitable in manufacturing processes, can also be addressed. The effectiveness of the hybrid swarm algorithm is demonstrated by modelling of the solder paste dispensing process.

An integrated approach for dynamic flowsheet modeling and sensitivity analysis of a continuous tablet manufacturing process

Manufacturing of powder-based products is a focus of increasing research in the recent years. The main reason is the lack of predictive process models connecting process parameters and material properties to product quality attributes. Moreover, the trend towards continuous manufacturing for the production of multiple pharmaceutical products increases the need for model-based process and product design. This work aims to identify the challenges in flowsheet model development and simulation for solid-based pharmaceutical processes and show its application and advantages for the integrated simulation and sensitivity analysis of two tablet manufacturing case studies: direct compaction and dry granulation. The developed flowsheet system involves a combination of hybrid, population balance and data-based models. Results show that feeder refill fluctuations propagate downstream and cause fluctuations in the mixing uniformity of the blend as well as the tablet composition. However, this effect can be mitigated through recycling. Dynamic sensitivity analysis performed on the developed flowsheet, classifies the most significant sources of variability, which are material properties such as mean particle size and bulk density of powders.

Interoperability of manufacturing applications using the Core Manufacturing Simulation Data (CMSD) standard information model

The goal of this paper is to propose an approach to enhance interoperability between manufacturing applications using the Core Manufacturing Simulation Data Information Model (CMSDIM) in order to streamline design and manufacturing activities throughout the product life cycle. To this end, a system framework required to facilitate such interoperability is first presented. The proposed approach, architecture, and developed translators are then illustrated and demonstrated using two separate case studies. The first case study facilitates design for manufacturing and assembly improvements for the development of new products, allowing for part of a discrete event simulation model of a downstream manufacturing and assembly process to be automatically generated from corresponding product assembly information contained in the lean design engineering software. Conceptual design and development of this case study, which extracts outputs from Design Profit™ lean design software and generates a corresponding discrete event simulation model in ProModel™ for a Nikon® L-100 Camera, is then discussed. The second case study demonstrates interoperability of three applications (order and inventory system, Gantt chart scheduler, and discrete event simulation) for a generic job shop operation. Using the considered case studies, this paper also details and demonstrates the benefits of interoperability enhancement using the CMSDIM, which is an important consideration in any product life cycle. Finally, we discuss how future research opportunities integrating additional manufacturing applications can be used to address intellectual challenges present in our current approach.

Job-Shop Manufacturing Execution Process Monitoring Technology

According to the production characteristics of single piece and small batch production and the combination of production and research in aerospace workshops, this thesis has made an in-depth study of job-shop manufacturing execution process monitoring technology with strong adaptability. A model for visual manufacturing process monitoring based on process route was proposed, it realizes the job-shop production featured by entire staff participation, entire process management and omnibearing warranty.

Formalizing Manufacturing Process Modeling Using Composition Trees

Optimizing manufacturing process modeling is critical to improving the efficiency and quality of manufacturing. However, manufacturing processes in general can be very complicated. A manufacturing process may involve (a) transforming a material into a new form, (b) combining different materials to form a new object, and (c) retrieving a certain part from a material as a new entity. All those activities could be confusing because many intermediate objects that exist only for a short period during the manufacturing process may have no formal names. To solve this problem, this paper proposes that composition trees (CT) can be successfully used to model manufacturing processes. Composition trees are parts of the formal notation in behavior engineering (BE), which is an innovative approach to the development of large software systems. For a composition tree, its characteristics of being intuitive, expressive and formal give it many advantages over other formal and informal modeling languages in software engineering. This paper uses a cooking case study to demonstrate how to use a composition tree model manufacturing processes. It is argued that the method could be applied to process engineering to good effect.

Modeling and Simulation of LED Manufacturing Process with ExtendSim

LED (Light Emitting Diode) has many excellent features as a new lighting device, and has been widely used by far. The structure and light-emitting principle are introduced. The LED manufacturing process is explained and dynamically shown with FLASH CS. The processing line is modeled and simulated through dynamics software ExtendSim. The simulation result is analyzed and the processing line is balanced. The throughput is improved with the simulation.

Integrated approach for rotor blade manufacturing cost estimate

PurposeThe purpose of this research is to develop an integrated model for composite rotor blade manufacturing cost estimates at the conceptual design stage. The integrated model seeks to provide a rapid and dynamic feedback based on evaluating the manufacturing cost estimate for a new product design at the conceptual design stage. This paper describes the automated estimating process for design to manufacturing cost of composite rotor blade.Design/methodology/approachAn integrated approach is implemented for evaluating the manufacturing cost estimates. The paper develops each module of the computer‐aided parametric model generation, time estimation models for composite manufacturing processes and decision support system. Finally, process flow data integration is done for all the modules. An example for a complicated geometric rotor blade is shown in this research paper. The results are compared in different design parameters and discussed.FindingsThe data integration for this approach was built by using ModelCenter® software. It is easier and more robust to apply than the other proposed methods. The selection of design, material and manufacturing parameters is achieved by integrated model within a short period of time.Practical implicationsThis paper provides an integrated concurrent approach for manufacturing cost evaluation of composite rotor blade. Manufacturing factors could be considered at the early stage of product development phase.Originality/valueThis paper suggests an effective and efficient way of evaluating the manufacturing cost at the conceptual stage of the design process. The concurrent engineering and integrated product process development approaches were addressed.

Modeling and Control of a Plastic Film Manufacturing Web Process

This paper is concerned with the modeling of a plastic film manufacturing process and the development and implementation of a model-based Cross-Directional (CD) controller. The model is derived from first-principles and some empirical relationships. The final validated nonlinear model could provide a useful offline platform for developing control and monitoring algorithms. A new controller is designed which has a similar structure to that of Internal Model Control (IMC) with the addition of an observer whose gain is designed to minimize process and model mismatch. The observer gain is obtained by solving a multiobjective optimization problem through the application of a genetic algorithm. The controller is applied to the nonlinear model and simulation results are presented demonstrating improvements that can be achieved by the proposed controller over two existing CD controllers.

The Control of Leontief Model on Industry Manufacturing Process

The classic Leontief model on industry manufacturing process is investigated. A kind of discrete-time singular dynamic input-output model of industry manufacturing process based on the classic Leontief Model is provided and the stability of this kind of model is researched. By the new mathematic method, the singular dynamic input-output system will not be converted into the general linear system. Finally, a sufficient stability condition under which the discrete-time singular Extended Leontief Model is admissible is proved.

Modeling of a Liquid Epoxy Molding Process Using a Particle Swarm Optimization-Based Fuzzy Regression Approach

Modeling of manufacturing processes is important because it enables manufacturers to understand the process behavior and determine the optimum operating conditions of the process for a high yield, low cost and robust operation. However, existing techniques in modeling manufacturing processes cannot address the whole common issues in developing models for manufacturing processes: a) manufacturing processes are usually nonlinear in nature; b) a small amount of experimental data is only available for developing manufacturing process models; c) outliers often exist in experimental data; d) explicit models in a polynomial form are often preferred by manufacturing process engineers; and e) models with satisfactory prediction accuracy are required. In this paper, a modeling algorithm, namely, the particle swarm optimization-based fuzzy regression (PSO-FR) approach, is proposed to generate fuzzy nonlinear regression models, which seek to address all of the common issues in developing models for manufacturing processes. The PSO-FR first employs the operations of particle swarm optimization to generate the structures of the process models in nonlinear polynomial form, and then it employs a fuzzy coefficient generator to identify outliers in the original experimental data. Fuzzy coefficients of the process models are determined by the fuzzy coefficient generator in which the experimental data excluding the outliers is used. The effectiveness of the PSO-FR approach is evaluated by modeling the manufacturing process liquid epoxy molding process which is a commonly used technology for microchip encapsulation in electronic packaging. Results were compared with those based on the commonly used modeling methods. It was found that PSO-FR can achieve better goodness-of-fitness than other methods. Also, the prediction accuracy of the model developed based on the PSO-FR is better than the other methods.

Ballistic transport and reaction modeling of atomic layer deposition manufacturing processes

AbstractIn this paper we develop a model describing the ballistic transport of chemical precursor species for an atomic layer deposition process (ALD). Because of the large Knudsen number corresponding to ALD over nanoporous materials, the gas-phase transport inside the nanostructures takes place in a purely ballistic manner. Precursor transmission probability functions describing the fluxes between the pore surface features are developed and then are coupled to ALD surface reaction models, spatially discretized, and integrated over each precursor exposure period to determine the pore spatial surface reaction extent profile. Predictions from our dynamic model then are compared to a previously published study of ALD in nanopores to validate our simulator. The utility of physically based models of the type we develop can be exploited to determine optimal precursor exposure levels for ALD based nanomanufacturing operations.

Uncertainty in Machining: Workshop Summary and Contributions

A National Science Foundation-sponsored workshop was held from Feb. 24–26, 2010 in Arlington, VA. The purpose of this “Uncertainty in machining” workshop was to address uncertainty and risk in machining and related manufacturing operations. The application of decision theory, which defines how rational decision makers should make decisions in the presence of uncertainty, was discussed. A summary of the meeting outcomes is presented. To aid in the application of decision theory to manufacturing process modeling, an example of Bayesian inference for the well-known mechanistic turning force model is provided. The discrete grid method is presented, and updating is performed using force data from the Assessment of Machining Models study completed by the National Institute of Standards and Technology.

Modelling of size effects on strengthening of multiphase Al based composites

A new multilevel mechanical model for multiphase metal matrix composite is proposed, accounting for size distribution effects. The matrix is considered as a micropolar elastic plastic Cosserat material and the hardening phases – as pure elastic ones. A two-steps homogenization procedure is applied to obtain the overall properties of the composite. A variational approach is used to evaluate the equivalent stress on macro level at the transition from micro to macro scale. The model is developed using information provided by microstructural investigations and EDX analysis. The elastic–plastic behaviour of rapidly solidified Al based Fe Si enriched alloys is considered. Due to fast cooling the material can be regarded as “natural” (in situ) composite, containing intermetallic and non-intermetallic compounds of different shapes, sizes, mechanical properties and volume fractions. The multistage modelling of bulk material manufacturing process is simulated using the FEM. The model is implemented as user defined subroutines into the FE code MARC. The influence of the microstructural size parameters on the hardening behaviour of the overall material is discussed.

Using past manufacturing experience to assist building the yield forecast model for new manufacturing processes

Polarizers are one of the key parts of Thin-Film Transistor Liquid-Crystal Displays (TFT-LCD), and their production requires high material costs. How to reduce manufacturing costs is thus a key task in this highly competitive global market. The precise yield forecast model considering learning effects that is proposed in this work is believed to be an effective approach to reduce both the raw material input-cost and inventory cost of overproduction. Support vector regression (SVR) model is one of the commonly used approaches to forecast the yield trend. However, in the early manufacturing stages for a new product, an SVR model is usually sensitive and unstable because of the use of insufficient data. Faced with this problem, this research aims at enhancing the SVR model by using past manufacturing experience and virtual samples to estimate the yield trend model for pilot products. This paper proposes a novel Quadratic-Curve Diffusion (QCD) method, wherein we derive a quadratic yield function (QYF) of the new manufacturing process for each key manufacturing variable by utilizing past manufacturing experience; and then use the QYF to generate virtual samples to assist building the overall yield forecast model of the new manufacturing process. The results show that the proposed method is superior to the performance of other forecast and virtual sample generation models.

Joint allocation of measurement points and controllable tooling machines in multistage manufacturing processes

Stream of variations (SoV) modeling of multistage manufacturing process has been studied for the past 15 years and has been used for identification of root causes of manufacturing errors, characterization and optimal allocation of measurements, process-oriented tolerance allocation, fixture design, and operation sequence optimization. Most recently, it was used for optimal in-process adjustments of programmable, controllable tooling (controllable fixtures, CNC machines) in order to enable autonomous minimization of errors in dimensional product quality. However, due to the time and resources needed to take the measurements and the high cost of controllable tooling, it is plausible to strategically position such measurements and controllable devices across a manufacturing system in a way that the ability to mitigate quality problems is maximized. In this article, a distributed stochastic feed-forward control method is devised to optimally (in the least square sense) reduce the variations in dimensional workpiece quality with a limited number of controllable tooling components and measurements distributed across a multistage manufacturing process. Based on this, a reactive tabu search algorithm is proposed to enable joint optimal allocation of measurement points a controllable tooling devices. Theoretical results are evaluated and demonstrate using the SoV model of an actual industrial process for automotive cylinder head machining.

Modeling of orders in autonomously controlled logistic systems

The paper extends the Autonomous Logistics Engineering Methodology (ALEM) by a deeper understanding of immaterial logistic objects to trigger manufacturing processes. Further, a hierarchical modeling concept is introduced to split customer orders logically into partial orders, which run directly at the shop floor level. Each partial order consists of certain manufacturing steps. The amendments enable adequate modeling of autonomous manufacturing processes. The research is a further step to integrate autonomously controlled processes in logistics.

Reducing overfitting in manufacturing process modeling using a backward elimination based genetic programming

Genetic programming (GP) has demonstrated as an effective approach in polynomial modeling of manufacturing processes. However, polynomial models with redundant terms generated by GP may depict overfitting, while the developed models have good accuracy on trained data sets but relatively poor accuracy on testing data sets. In the literature, approaches of avoiding overfitting in GP are handled by limiting the number of terms in polynomial models. However, those approaches cannot guarantee terms in polynomial models produced by GP are statistically significant to manufacturing processes. In this paper, a statistical method, backward elimination (BE), is proposed to incorporate with GP, in order to eliminate insignificant terms in polynomial models. The performance of the proposed GP has been evaluated by modeling three real-world manufacturing processes, epoxy dispenser for electronic packaging, solder paste dispenser for electronic manufacturing, and punch press system for leadframe downset in IC packaging. Empirical results show that insignificant terms in the polynomial models can be eliminated by the proposed GP and also the polynomial models generated by the proposed GP can achieve results with better predictions than the other commonly used existent methods, which are commonly used in GP for avoiding overfitting in polynomial modeling.

Distributed Modeling of Manufacturing Activities Using Integrative Manufacturing Process Model

Distributed Modeling of Manufacturing Activities Using Integrative Manufacturing Process Model

Research on Dynamic Reconfigurable Manufacturing Process Framework Based on MDA

With the characteristic analysis of manufacturing process in various manufacture enterprises, Model Driven Architecture methodology is applied to construct dynamic reconfigurable manufacturing process model, which can solve the problems of portability, information integration, and interoperability effectively. A dynamic reconfigurable modeling process framework based on model driven Architecture is introduced for manufacture enterprises in this paper. It separates the manufacturing system functionality specification from its implementation on any specific technology platform, and makes the system reconfigurate the manufacturing progress through the transformation of models and transmission of information. At last, the integrated system was applied to a discreet manufacturing process and the application result proves that the system is practical and effective.

Analytical model for geometrical characteristics control of laser sintered surfaces

Selective laser sintering (SLS) is an additive rapid manufacturing technique where a high-power laser is used to fuse micro- and nano-particles into a specified three-dimensional geometry. The goal of this work was to develop an analytical model for the SLS manufacturing process in order to control the geometrical characteristics of the sintered areas when iron/copper (Fe/Cu) powder alloy is used on a flat substrate. Powder particles are subject to melting by the laser energy and form a liquid globule that solidifies as the laser beam spot moves along the substrate. The model is developed by considering lumped mass and energy balances and fluid dynamic equilibrium of the sintered material. It is assumed that the process is two-dimensional and axisymmetric. It is further assumed that the energy delivered by the laser is used to sinter the material rising its temperature up the melting point, while the energy lost due to conduction in the metal substrate is very small. The sintered area geometry is parameterised and the parameters are obtained by solving a system of nonlinear algebraic equations.