Optimal Processing Sequence Research Articles

Enhancing Manufacturing Process Education via Computer Simulation and Visualization

Industrially significant metal manufacturing processes such as melting, casting, rolling, forging, machining, and forming are multi-stage, complex processes that are labor, time, and capital intensive. Academic research develops mathematical modeling of these processes that provide a theoretical framework for understanding the process variables and their effects on productivity and quality. However it is usually difficult to provide the students with hands-on experience of experimentation with process parameters which leads to disconnect between engineering education and industrial needs. In order to solve this problem, interdisciplinary student projects were undertaken at author’s institution to develop computer simulation tools that would facilitate process visualization, experimentation, exploration, design and optimization. The hypothesis is that these new computer-based tools would enhance educational experience for the manufacturing engineering students as assessed by the ABET-derived educational outcomes and also based on Bloom’s cognitive outcomes modified for STEM disciplines. The first system described in this paper is the visualization of metal ingot production schedule in an industrial setting that provides a basis for interactive decisions. The graphical user interface is created to visualize the schedule according to the specific characteristics of the machines. Another example of process simulation presented in this paper is the design and analysis of flexible rolling technology in industrial processing of low carbon steels. Process simulation tools designed in both cases allow new process sequences to be generated by breaking down existing process routes into key elements and then by recombining them to generate novel alternative and more efficient hot processing sequences. This enables the identification of an optimal process sequence for specified steel compositions that also satisfies simultaneous design criteria such as process feasibility and property maximization. It is proposed that incorporation of such computer simulation tools in the pedagogy would be highly effective to enhancing and enriching undergraduate manufacturing education.

Optimal Policies for Perishable Products When Transportation to Export Market Is Disrupted

We consider a problem where a firm produces a variety of fresh products to supply two markets: an export market and a local market. A public transportation service is utilized to deliver the products to the export market, which is cheap, but its schedule is often disrupted severely. Each time this happens, the firm faces the following questions. (i) For a product that has been finished and is waiting for delivery to the export market, should it continue to wait, at an increasing risk of decay, and when should the waiting be terminated and the product be put to the local market? (ii) For a product that has not been finished, should its processing be postponed, so as to reduce the loss from decay after its completion? (iii) What is the best sequence to process the remaining products, according to the information available? We develop, in this study, a model to address these and other related questions. We find optimal policies that minimize the total expected loss in both the make‐to‐order and make‐to‐stock production systems, respectively. For each finished product, we reveal relationships among the desirable waiting time, the price at the local market, and the decaying cost. For unfinished products, we find the optimal start times and processing sequence. Numerical experiments are also conducted to evaluate the optimal policies.

Model-based Optimisation of Integrated Algae Biorefinery

With conventional energy reserves diminishing and atmospheric pollution increasing due to excessive usages of conventional fuel products, we need to switch to cleaner replacements of energy urgently. In this respect, microalgae have many advantages over other bio-energy options. Microalgae can yield very high lipid content, which can be processed to biodiesel. However, the major concern for large scale application of microalgae as an energy-resource is the relatively high cost associated with the entire process. The presence of several batch processes, which are inherently dynamic in nature, provides a significant challenge. Thus, optimisation study needs to be carried out to ensure efficient and effective application of diverse potential of microalgae in an economical way. Model-based optimisation provides the necessary tools to achieve this goal. In this work, we have studied various alternatives for algae growth and harvesting steps. An economic model has been formulated with cost data of various alternatives. The model was applied to a case of fixed daily biodiesel demand of 230550 kg, with the objective of net annualised life cycle cost minimisation. The optimisation model developed using the software of General Algebraic Modeling System (GAMS) problem was solved using the CPLEX solver. The optimal process sequence obtained was growth in 1095 numbers of ponds of 100000 m 2 for 20 days with chicken waste as nutrient, followed by settling in a series of two settling tanks without the aid of any flocculant. 19 primary tanks of 25000 m 3 capacity each and one secondary tank of 24000 m 3 capacity were selected by the model. The subsequent centrifugation of the resulting solution was carried out by 43 centrifuges of capacity 0.01009 m 3 /sec. (160 gallons per minute) each. The computed net cost of growth and harvesting steps of biodiesel production was 1.446 $/kg (1.258 $/l).

Detoxification of a lignocellulosic biomass slurry by soluble polyelectrolyte adsorption for improved fermentation efficiency

This study investigated the detoxification of a dilute acid pretreated Ponderosa pine slurry using the polyelectrolyte polyethyleneimine (PEI). The addition of polyelectrolyte to remove enzymatic and/or fermentation inhibitory compounds, that is, acetic acid, furfural, and 5-hydroxymethylfurfural (HMF), was performed either before or after enzymatic hydrolysis to determine the optimal process sequence. Negligible acetic acid, glucose, and xylose were removed regardless of where in the process the polymer addition was made. Maximum furfural and HMF separation was achieved with the addition of PEI to a clarified pre-enzymatic hydrolysis liquor, which showed that 88.3% of furfural and 66.4% of HMF could be removed. On the other hand, only 23.1% and 13.4% of furfural and HMF, respectively, were removed from a post-enzymatic hydrolysis sample; thus, the effects of enzymes, glucose, and wood solids on inhibitor removal were also investigated. The presence of solid particles >0.2 µm and unknown soluble components <10 kDa reduced inhibitory compound removal, but the presence of elevated glucose levels and enzymes (cellulases) did not affect the separation. The fermentability of detoxified versus undetoxified hydrolysate was also investigated. An ethanol yield of 92.6% of theoretical was achieved with Saccharomyces cerevisiae fermenting the detoxified hydrolyzate, while no significant ethanol was produced in the undetoxified hydrolyzate. These results indicate that PEI may provide a practical alternative for furan removal and detoxification of lignocellolosic hydrolysates, and that application before enzymatic hydrolysis minimizes separation interferences.

Selection of optimal set of cutting tools for machining of polygonal pockets with islands

Selection of the optimal set of cutting tools is one of the most important steps in process planning for 2.5-D pocket machining. Conventional CAM software requires considerable input from the user in terms of selection of tool sizes and machining strategy. This trial-and-error procedure to determine the optimal process sequence tends to generate conservative and suboptimal results. This paper presents a methodology for optimal selection of a sequence of tools to minimize the total time required to end mill a non-convex polygonal pocket with or without islands using the staircase milling strategy. The algorithm decomposes the pocket geometry into convex regions and mills each region independently by selecting a sequence of tools based on the accessibility of various tools to the region. Strategies have been developed for machining the main pass and the subsequent leftover areas in order to obtain the final pocket geometry. Subsequently, the machining times for each decomposed area are aggregated while accounting for the need to use multiple passes, non-cutting time, and the tool change time. A dynamic programming approach is used to determine the optimal set of tools which minimizes the total processing time. The effect of varying the non-cutting speed and tool change time on the tool path length and number of tool selection is studied.

Thermomechanical Process Innovation via Computer Modeling and Simulation

A new approach is proposed in this paper that describes the development of flexible rolling technology in industrial processing of C – Mn and Low C - microalloyed steels. Scientific knowledge of industrially-significant processes for these materials is presently fragmented and scattered in published literature, which causes impediment to process innovation and optimization. In the current work, it is demonstrated that new process sequences could be developed by breaking down existing process routes in to key elements and then by recombining them to generate novel alternative and more efficient hot processing sequences. The proposed methodology establishes a platform for a more realistic assessment of existing process routes and the development of new hybrid process routes that combine ideas from alternative processes. This enables the identification of an optimal process sequence for specified steel compositions that also satisfies simultaneous design criteria such as process feasibility and property maximization. Application of the proposed algorithm in industrial-scale rod rolling of a medium C-Mn steel is demonstrated and discussed.

Optimization of Bulkhead Processing Sequence for Multi-Frame Monolithic Components by FEM

When machining aerospace monolithic components, most of materials could be removed, resulting in severe deformation of the parts due to the release and redistribution of the blank’s original residual stress, together with the action of cutting loads and clamping force. A finite element model (FEM) is built for predicting the deformation caused by those factors mentioned above. In this model, some key techniques such as material properties, initial residual stress model, and application of dynamic cutting loads and transformation of boundary condition are discussed in details. The proposed model predicts the machining deformation for multi-frame monolithic components. Particular attention is paid to the influence of the bulkhead processing sequence on part deformation. At last the paper puts forwards optimal bulkhead processing sequence based on minimizing the machining deformation.

06/01010 Distribution of energy consumption and the 2000 W/capita target: Spreng, D. Energy Policy, 2005, 33, (15), 1905–1911.

With conventional energy reserves diminishing and atmospheric pollution increasing due to excessive usages of conventional fuel products, we need to switch to cleaner replacements of energy urgently. In this respect, microalgae have many advantages over other bio-energy options. Microalgae can yield very high lipid content, which can be processed to biodiesel. However, the major concern for large scale application of microalgae as an energy-resource is the relatively high cost associated with the entire process. The presence of several batch processes, which are inherently dynamic in nature, provides a significant challenge. Thus, optimisation study needs to be carried out to ensure efficient and effective application of diverse potential of microalgae in an economical way. Model-based optimisation provides the necessary tools to achieve this goal. In this work, we have studied various alternatives for algae growth and harvesting steps. An economic model has been formulated with cost data of various alternatives. The model was applied to a case of fixed daily biodiesel demand of 230550 kg, with the objective of net annualised life cycle cost minimisation. The optimisation model developed using the software of General Algebraic Modeling System (GAMS) problem was solved using the CPLEX solver. The optimal process sequence obtained was growth in 1095 numbers of ponds of 100000 m2 for 20 days with chicken waste as nutrient, followed by settling in a series of two settling tanks without the aid of any flocculant. 19 primary tanks of 25000 m3 capacity each and one secondary tank of 24000 m3 capacity were selected by the model. The subsequent centrifugation of the resulting solution was carried out by 43 centrifuges of capacity 0.01009 m3/sec. (160 gallons per minute) each. The computed net cost of growth and harvesting steps of biodiesel production was 1.446 $/kg (1.258 $/l).

High Throughput Screening of Chromatographic Phases for Rapid Process Development

AbstractThe necessity of being “first on the market” paired with the need to cut production costs highlights the need for strategies and techniques allowing a fast process development while at the same time covering a high number of potential process parameters in order to reach an optimal process sequence. This can only be met by the use of high throughput screening (HTS) techniques. In this paper HTS applications presently applied in bioprocess development are reviewed, their use in chromatographic process parameter evaluation is discussed and concepts for the rational design of HTS experiments are presented. A rational approach for chromatographic parameter screening comprehends an initial screening procedure for ligand selection followed by the incorporation of specific adsorbent features using HTS pipetting systems finalized by a fine tuning step using packed mini‐columns. The analytical part of these screening techniques clearly sets the bottleneck of these procedures with a trade off between analytical information and analysis time. Finally, intelligent HTS strategies should in addition include efficient data processing paired with modern tools for design of experiments or model based process design.

Global optimization of a feature-based process sequence using GA and ANN techniques

Operation sequencing has been a key area of research and development for computer-aided process planning (CAPP). An optimal process sequence could largely increase the efficiency and decrease the cost of production. Genetic algorithms (GAs) are a technique for seeking to ‘breed’ good solutions to complex problems by survival of the fittest. Some attempts using GAs have been made on operation sequencing optimization, but few systems have intended to provide a globally optimized fitness function definition. In addition, most of the systems have a lack of adaptability or have an inability to learn. This paper presents an optimization strategy for process sequencing based on multi-objective fitness: minimum manufacturing cost, shortest manufacturing time and best satisfaction of manufacturing sequence rules. A hybrid approach is proposed to incorporate a genetic algorithm, neural network and analytical hierarchical process (AHP) for process sequencing. After a brief study of the current research, relevant issues of process planning are described. A globally optimized fitness function is then defined including the evaluation of manufacturing rules using AHP, calculation of cost and time and determination of relative weights using neural network techniques. The proposed GA-based process sequencing, the implementation and test results are discussed. Finally, conclusions and future work are summarized.

High-efficiency screen-printed belt co-fired solar cells on cast multicrystalline silicon

High-efficiency 4cm2 untextured screen-printed solar cells were achieved on cast multicrystalline silicon. These cells were fabricated using a simple manufacturable process involving POCl3 diffusion for emitter, PECVD SiNx:H deposition for a single-layer antireflection coating and rapid co-firing of Ag grid, Al backcontact, and Al-BSF in a belt furnace. An optimized process sequence contributed to effective impurity gettering and defect passivation, resulting in high average bulk lifetimes in the range of 100–250 μs after the cell processing. The contact firing contributed to good ohmic contacts with low series resistance of <1Ωcm2, low backsurface recombination velocity of <500cm∕s, and high fill factors of ∼0.78. These parameters resulted in 16.9% and 16.8% efficient untextured screen-printed cells with a single layer AR coating on heat exchanger method (HEM) and Baysix mc-Si. The identical process applied to the untextured float zone wafers gave an efficiency of 17.2%. The same optimized co-firing cycl...

Scheduling flow shops in the environment of multi-functional machine tools

The concept of virtual machine tool (VMT) is introduced in this paper to specify machining resources of multi-functional machine tools. A two-level model for makespan minimization in a flow shop is developed in multi-functional machine tool environments. The model is first optimized with an Hopfield neural network to find the optimal processing sequence for components to visit all the necessary VMTs. Secondly, according to a set of shop floor control rules, the VMTs for components machining are matched with real machine tools. This procedure is driven by the events taking place in components machining, such as the arriving or the finishing of a component. Numerical experiments are conducted in a number of flow shop scheduling problems up to 8×8 and the mean relative optimizing rate is employed to assess the results of numerical experiments.

Electrical and optical characterization of regrown PHEMT layer structures on etched GaAs surfaces

Selective regrowth, which involves the growth of a device structure, patterning, etching and epitaxial regrowth of another device structure is an important technique for monolithic integration. The nature of the surface before the regrowth is crucial for realizing good quality regrown devices. In this study, the effect of different GaAs surface preparation processes on the electrical and optical characteristics of a regrown pseudomorphic high electron mobility transistor (PHEMT) is studied. Using photoluminescence, Hall, atomic force microscopy and d.c. and microwave measurements on devices, an optimized process sequence has been identified to fabricate PHEMT structures with equivalent characteristics of those grown on epiready wafers.

An expert system for cold forging process design based on a depth-first search

Due to the non-deterministic nature of process sequence design for multi-stage cold forging, various process designs are available depending on the initial billet geometry and the order of basic processes such as forward/backward extrusion, upsetting and trimming process. Therefore, various process sequences should be determined and compared to obtain an optimal solution. For this purpose, a depth-first search, a searching technique used in artificial intelligence, has been introduced in developing an expert system for multi-stage cold forging process design. As a result, process designers can select the optimal process sequence from the searched feasible solutions by estimating the values of evaluation functions that are introduced to represent the important design characteristics. In the present investigation, the distributions of the global effective strains in the final product and the forming loads required at each forging stage were selected to be controlled. In general, a more realistic process sequence should be determined by taking into account manufacturing conditions such as the number of forming stages, the forming loads, the shearing diameter of the coil, the open upsetting diameter, and the knock-out lengths of the die and punch. In this paper, a methodology of applying the searching technique for process sequence design is discussed, and the flexibility of the introduced searching technique is evaluated by generating design examples of a shaft part, a wrench and hexagonal bolts of AISI 1045.

Optimal processing of marine high-resolution seismic reflection (Chirp) data

Chirp frequency-modulated (FM) systems offer deterministic, repeatable source-signatures for high-resolution, normal incidence marine seismic reflection data acquisition. An optimal processing sequence for uncorrelated Chirp data is presented to demonstrate the applicability of some conventional seismic reflection algorithms to high-resolution data sets, and to emphasise the importance of a known source-signature. An improvement of greater than 60dB in the signal- to-noise ratio is realised from correlating the FM reflection data with the transmitted pulse. Interpretability of ringy deconvolved data is enhanced by the calculation of instantaneous amplitudes. The signal-to-noise ratio and lateral reflector continuity are both improved by the application of predictive filters whose effectiveness are aided by the repeatability of the Chirp source.

Design Method on Optimal Flexible Manufacturing Systems

This paper presents a methodology for designing optimal flexible process sequencing using SFC (sequential function charts) based approach. In this approach SFC is used as a unified framework for representing the optimal process sequence by detennining the optimum condition for transition between two macro steps, completing the flexible manufacturing cycle at the minimum cost. Dynamic programming has been used to find the optimal trajectory, in which a strong correspondence with its sequential function chart is stated.

Modelling and Optimization of Production Processes in Overhaul Activities

The paper studies the problem of sequence optimisation inoverhaul activities production process modelling. Possibletasks, optimisation criteria and methods are analysed. Methodsfor solving two- or multi-phase production processes areillustrated on examples from practice. Special attention is paidto rules and criteria for selecting the optimal sequence in multi-phase production processes. For the given examples the optimalprocess sequence is presented graphically with the resultsanalysis.

Process sequencing and process clustering in process planning using state space search

Process sequencing, as a very important part of process planning, has been the subject of many research reports in the area of process planning, but is usually treated as a feature-sequencing problem. This paper presents a novel algorithm for process sequencing, which considers the feature precedence network, different process candidates, and machine and tool constraints. The algorithm consists of two parts: process clustering and process sequencing. For clustering we used a notion of the same resource usage for different features, while for sequencing we applied the best-first search method algorithm to generate an optimal process sequence. The algorithm has been applied on several examples with realistic complexities, and it showed satisfactory results.

Si/SiGe/Si heterostructure growth without interface roughness at high germanium mole fractions by low temperature low pressure chemical vapour deposition

A new low temperature low pressure chemical deposition process for Si/SiGe/Si heterostructures containing high germanium mole fractions has been developed. The silicon surface and the SiSiGe interface roughness strongly depend on the deposition temperature and the germanium concentration. For germanium concentrations around 20%, flat surfaces can be realized at 550 °C. High concentrations of more than 50% Ge require much lower growth temperatures, namely 450 °C for SiGe and 525 °C for the silicon capping layer. A new optimized process sequence is given to grow Si/SiGe/Si heterostructures with atomically flat surfaces and interfaces containing more than 50% Ge.

Optimal process sequence identification and optimal process tolerance assignment in computer-aided process planning

An approach for generating the optimal process tolerances and for evaluating alternative process sequences in process planning is presented. The formulations for evaluating production costs of a process sequence and its production operations are established. Several new production cost-tolerance models for production operations, which can significantly improve the modelling accuracy of empirical data of typical production processes, are introduced. Constrained nonlinear optimization is applied to identify the optimal process sequence and to determine the optimal process tolerances with least production costs. The method can improve present CAPP methods by introducing quantitative analysis. It can be combined with the knowledge-based generative CAPP approach to automatically generate the optimal sequence of production operations and the optimal process tolerances for a given design feature. An example is used to illustrate the method.