Process Cost Model Research Articles

A learned cost model for big data query processing

The efficiency of query processing in the Spark SQL big data processing engine is significantly affected by execution plans and allocated resources. However, existing cost models for Spark SQL rely on hand-crafted rules. While learning-based cost models have been proposed for relational databases, they do not consider available resources. To address this issue, we propose a resource-aware deep learning model capable of automatically predicting query plan execution times based on historical data. To train our model, we embed query execution plans within a query plan tree and extracted features from allocated resources. An adaptive attention mechanism is integrated into the deep learning model to enhance prediction accuracy. Additionally, we extract sufficient features to represent data information and learn the effect of the data on query execution. This approach reduces the need for model retraining owing to data changes. The experimental results demonstrate that our deep cost model outperforms traditional rule-based methods and relational database learning-based optimizers in predicting query plan execution times.

An Integrated Analytics of PEM Electrolyzer and Steel Making

It is widely accepted that the hydrogen-water couple can elevate the green economy circuit if powered by renewable sources of energy. Proton-exchange membrane water electrolyzer (PEMWE) being a clean electrochemical device is expected to provide an amenable solution to the hydrogen-water nexus and act as an enabler to achieving decarbonization of the steel-making industry. In this work, we throw light on the performance of a PEMWE that feeds target hydrogen volumes to a direct iron ore reduction process. The response of the electrolyzer under the presence of a host of operational stressors and variable electricity input is delineated through a macroscale modeling framework. The techno-economics associated with specific energy consumption of the downstream steel-making pathways will be further discussed based on insights from a process cost model.

A novel milling parameter optimization method based on improved deep reinforcement learning considering machining cost

The cutting parameters in the part machining process have a great impact on the energy efficiency and economy of the machining system. The cutting parameters in traditional machining are often selected according to the operator's experience, which lack attentions to energy saving and economy. Therefore, a milling process parameter optimization method based on deep reinforcement learning (DRL) is proposed in this paper. Taking the machining cost composed of cutting energy efficiency and machining time cost as the optimization goal, the spindle speed and feed speed under the combination of different cutting depth and cutting width parameters are optimized. Firstly, the machine tool energy consumption model is established by back propagation neural network (BPNN) regression method to realize the continuity of machine tool energy consumption state prediction, and the processing cost model is established as the optimization objective function. Then, the process parameter optimization problem is formally expressed as a Markov decision process (MDP), and the corresponding states, actions, reward functions and constraints are defined. Finally, combined with the machine tool power consumption model and machining cost model, the simulation environment is established, and the BP-TD3 method is proposed to solve the Markov decision problem of milling parameter optimization. Taking the machining center as an example, the aluminum alloy workpiece is milled. Compared with the classical optimization algorithm, the proposed method can save 95 % optimization calculation time, and ensure that the average processing cost after optimization is close to the minimum processing cost obtained by the classical optimization algorithm.

Modeling and software implementation of manufacturing costs in additive manufacturing

Quantification and analysis of manufacturing costs in additive manufacturing (AM) are important research tasks, since materials and machines for AM are relatively expensive. To enable the cost estimation in AM, a number of studies have focused on the development of cost estimation software tools for AM. However, these studies mainly focused on single AM systems and materials pre-selected for individual cases. The literature still lacks of a more general perspective on the cost factors and cost structure in different AM processes. Addressing this issue, this paper contributes a general cost model for AM processes and the development of a cost estimation application (App) based on the model. The developed App covers a variety of AM materials and systems. In order to validate the usability of the App, three use cases are performed. The result of the use cases demonstrates that the App, as well as the underlying cost model, is a powerful tool for estimating and assessing the build cost of AM in different design issues.

A scalable cost modelling architecture for evaluating the production cost-effectiveness of novel joining techniques for aircraft structures

This work introduces a cost modelling architecture in order to determine the cost-effectiveness of the latest joining technology developments. Riveting is the conventional joining method in the aerospace industry, but is a time-consuming, expensive process that adds excessive weight to a structure. As part of the JTI Clean Sky 2 Joint Technology Initiative, the OASIS project (“Optimization of Friction Stir Welding (FSW) and Laser Beam Welding (LBW) for assembly of structural aircraft parts”) aims to demonstrate the feasibility and cost-effectiveness of novel joining technologies. The technologies being investigated are LBW, FSW and Friction Stir Spot Welding (FSSW). Physical demonstrators, simulation studies and access to industry leading technical expertise from OASIS project partners have helped develop detailed production process maps and input accurate process metrics to determine manufacturing costs. To this end, an activity-based cost modelling architecture has been developed to predict the cost-effectiveness of the joining technologies and assess them against both manual and automatic riveted solutions. The model has been designed in a manner that enables integration into current manufacturing eco-systems, has scalability for large aerospace companies and the ability to perform multi-fidelity process cost models that can be integrated with one another as required.

Estimating the Capital Costs of Electrowinning Processes

Novel electrolysis processes remain strong technological contenders for advances in sustainable materials processing, in particular metals, yet will need to compete economically with currently-deployed production facilities. To evaluate the technoeconomic efficacy of new electrolytic metal extraction processes, an understanding of the capital and operating costs of electrowinning is necessary. Estimation of electrochemical operating costs has been afforded due attention, yet capital cost (CAPEX) trends are far less understood. Herein, we attempt to show that estimating the capital costs of electrowinning processes via conventional chemical engineering scaling laws is not possible. Instead, we propose a capital cost model for electrochemical processes based on relevant operating parameters such as current density, temperature, and voltage. The new model for capital cost describes within ±30 to 100% the capex for existing electrochemical processes, sufficient for order of magnitude and preliminary design capital cost estimation.

A Gibbs sampling based coordination scheduling method with fixed I/O constraint

Distributed production technology is the basic technology of modern production. Distributed production needs to consider the fixed input and output constraint, which is different from traditional centralized production. The objective of distributed production scheduling with fixed I/O constraint is to find the optimal scheduling route in production network. In this paper, we proposed the GSS (Gibbs Sampling Scheduling) method to schedule the schedulable jobs, according to a scheduling metric. GSS guarantees each device has opportunities to be allocated, to reduce the load on critical devices. The scheduling metric utilizes PCM (processing cost model) and TCM (transportation cost model) as parameters. The PCM represents the impact of schedulable jobs on the unscheduled jobs. The TCM represents the importance of devices’ positions in the production network. We designed experiments to analyze the parameters of the GSS, and carried out the test on makespan and load balance. These experiments can verify the effectiveness and efficiency of the proposed GSS method.

Weighted signal-to-noise ratio robust design for a new double sampling [formula omitted] chart

Recently, to monitor the mean shifts of a process by using attribute inspection, a new np chart, called npx chart, was proposed to synthesize the advantages of attribute and variable charts. Attracted by the easy-implementation property and good performance of this chart, in this paper, we propose a double-sampling (DS) npx chart to improve the efficiency of the single-sampling npx chart. We build up a process cost model to compare the performance of DS npx chart and the traditional npx chart. To minimize the process cost with uncertainty, we introduce a robust design method based on the “weighted signal-to-noise ratio (WSNR)”. Specifically, we take into account the weighted expectation on multiple scenarios and regard signal-to-noise ratio as a response to variance. Compared to the traditional designs, the WSNR robust design not only preserves the statistical strengths and economic efficiency, but also shows the advantages of flexibility and adaptability. Then, numerical experiments are conducted to measure the performance of our model. The results demonstrate that the DS npx chart is superior to the npx chart in controlling the ARL1 and the process cost. By analyzing the process cost, we find that the larger the scenario range is, the better the WSNR robust design performs. In addition, the performance of the proposed robust design presents clear advantages to the existing robust measures (e.g., absolute robustness, robust deviation, and relative robustness), for which the WSNR will be a promising approach for the practitioners.

A Model for Prioritization of Improvement Opportunities Based on Quality Costs in the Process Interdependency Context

In this paper, the PAF (preventive-appraisal-failure) quality costs model is used on the process level, in order to identify those processes which are critical as a top-priority for improvement. The elements of quality costs are considered in relation to the outputs of the processes, where the principle that one process affects the quality of another process by its outputs is taken into account. That is why the failure costs of the affected processes should also be assigned to the quality costs of the observed process. Using the factors proposed in the Process cost model, costs are identified and calculated for each process for two moments: the current (before improvements) and desired (after improvements). Improvements are implemented on the basis of the causes of nonconformity analysis and according to the PAF model assumption that preventive and appraisal activities reduce failure costs and the total costs of quality. The difference between the total costs of quality in two observed moments is seen as the greatest possible quality cost reduction for the observed process. This value is used as a criterion for ranking the processes according to their priority for improvements, taking into account the assumption of the PAF model that greater costs mean lower quality and hence a higher priority for improvement.

Roll’s Model of Bid Ask Spread: Implicit Evidence from BSE

This paper empirically studies the bid ask spread model as proposed by Richard Roll using the data from Bombay Stock Exchange (henceforth, BSE). The objective is to understand and determine whether the impact of various events like the abolition of Badla, introduction of Electronic Trading and Futures Trading in BSE, are sufficiently captured by Roll’s model or not. The order processing cost model was taken into consideration for the three period event study, namely: i. Pre and post impact when the Badla was banned, ii. Introduction of Electronic trading in BSE, and iii. Commencement of futures trading in BSE Although the empirical findings presented here are those of the stocks which comprises the Sensex, but it gives a good picture of the market response as a whole as the volume of trading in the BSE is highly skewed in favor of the Sensex scripts.The results indicate that Roll’s model does not fit the data very well. There is a large number of covariance of price change which is positive, which is contrary to the proposed model. The variation of the calculated spread is also significant on a month to month basis. Further research is needed to propose a theoretical framework for Indian stock market.

Evaluación técnico-económica y diseño conceptual de una planta de biofertilizantes líquidos

Los biofertilizantes se han convertido en una alternativa de bajo costo, efectiva y amigable con el medio ambiente en comparación con los fertilizantes químicos. En el presente trabajo se desarrollaron los modelos de ingeniería de proceso y costo de una planta de biofertilizantes líquidos con una capacidad de 44 toneladas por año (568 kg/lote). Los modelos fueron obtenidos empleando el simulador de procesos SuperPro Designer® versión 8.5 (Intelligen, 2012), mientras que el diseño conceptual en 3D y dimensionamiento de la planta se desarrolló mediante el software OptiPlant (ASD Global, 2015). Se requiere una inversión total de USD $ 3 975 000 para erigir la planta, el costo de producción unitario de una botella de 1,5 L de biofertilizantes líquido es de USD $ 24,009, mientras que los indicadores económicos Valor Actual Neto (VAN) y Tasa Interna de Retorno (TIR) tuvieron valores de USD $ 716 000 y 2,55 %, respectivamente. También se obtienen ganancias totales de USD $ 985 000/año y un valor del Período de Retorno de la Inversión de 6,70 años.

Process selection, modelling and optimization of a water scrubbing process for energy-self-sufficient biogas upgrading plants

This study focuses on the techno-economic optimization of an energy-self-sufficient biomethane production plant based on anaerobic digestion of agricultural residues and maize silage and an optimized water scrubbing process. The biomethane production plant features a biogas production system, a water scrubbing upgrading section and an internal combustion engine fed with a fraction of the produced biogas. The engine is properly sized to provide both the electric and thermal power required to operate the whole plant. A thorough literature review is performed to select the most promising water scrubbing layout, which features a scrubber, a flash and a stripping column. Accurate process and preliminary cost models of the main equipment units are developed and validated against literature and industrial data. A techno-economic bi-objective optimization of the upgrading process is then performed in order to determine the set of Pareto optimal solutions with maximum net biomethane recovery efficiency and minimum capital cost. Process optimization is performed with a black-box strategy using the PGS-COM single-objective algorithm and the NSGA-II multi-objective algorithm. The resulting Pareto front achieves net biomethane recovery figures in the range 88.1–89.7% and specific Total Equipment Costs between 0.95 and 1.03 k€/(Nm3BM/h). Compared to the conventional plant configuration, the proposed integration allows to avoid 188 g CO2/Nm3BM emissions and to save between 6.8 and 8.5% of primary energy, with an estimated increase of Total Equipment Cost of approximately 11–13.5% (anaerobic digestion section included).

Abrasive Water Jet Machining of Multidirectional CFRP Laminates

Abrasive water jet machining (AWJM) is widely used in aerospace, marine and automotive industries for trimming composites. However, AWJM demonstrates some challenges when cutting carbon fibre reinforced plastic (CFRP) composites materials such as cut accuracy and quality. More experimental work is needed to provide sufficient machinability databases for manufacturing engineers. This paper presents an experimental study and statistical analysis for cutting 2 lay-up configurations of multidirectional CFRP laminates. Different AWJM conditions including jet pressure, feed rate, and standoff distance are experimented using full factorial design of experiments. Machining process responses such as top and bottom kerf width, kerf taper, machinability and surface characteristics have been evaluated using analysis of variance (ANOVA) technique. A process cost model for the AWJM is presented.

A digital process optimization, process design and process informatics system for high-energy abrasive mass finishing

This research describes a new digital-based system to improve the efficiency and to reduce costs of high-energy abrasive mass finishing processes. The system is developed from a rigorous programme of theoretical analyses, technical experiments and industry validations. The system is able to predict the response of the process, in the context of component surface roughness and cycle time, due to employed input parameters: machining speeds, rotational velocities, immersion depth and abrasive media type. A graphical user interface (GUI) was designed to permit on-screen analyses and determination of system response under a wide range of parameters. The system converges to an optimized machining solution using optimization methods and convergence theory. The output from the system associates optimized machining parameters with output criteria that may be a target surface roughness, a minimum cycle time or a production planning period. This facilitates use of the system as a cycle design tool, as production decision support or as a process cost model. The system is generic in design and with minor modification of input and output criteria and has potential application to many other processes and applications including for example, pharmaceutical, food processing, agriculture and automotive.

PHYSICAL-MATEMATICALSCIENCE MECHANICS SIMULATION CHALLENGES IN OPTIMISING THEORETICAL METAL CUTTING TASKS

Abstract. Objectives In the article, problems in the optimising of machining operations, which provide end-unit production of the required quality with a minimum processing cost, are addressed. Methods Increasing the effectiveness of experimental research was achieved through the use of mathematical methods for planning experiments for optimising metal cutting tasks. The minimal processing cost model, in which the objective function is polynomial, is adopted as a criterion for the selection of optimal parameters. Results Polynomial models of the influence of angles φ, α, γ on the torque applied when cutting threads in various steels are constructed. Optimum values of the geometrical tool parameters were obtained using the criterion of minimum cutting forces during processing. The high stability of tools having optimal geometric parameters is determined. It is shown that the use of experimental planning methods allows the optimisation of cutting parameters. In optimising solutions to metal cutting problems, it is found to be expedient to use multifactor experimental planning methods and to select the cutting force as the optimisation parameter when determining tool geometry. Conclusion The joint use of geometric programming and experiment planning methods in order to optimise the parameters of cutting significantly increases the efficiency of technological metal processing approaches.

Economic and Ecological Evaluation of Hybrid Additive Manufacturing Technologies Based on the Combination of Laser Metal Deposition and CNC Machining

Hybrid additive manufacturing technologies combine selective material deposition with a conventional milling process in one machine, enabling the production of complex metal parts and reducing the need for part specific tools. The hybrid technology offers technological advantages compared to more established additive fabrication processes, such as powder bed fusion. Compared to powder bed based additive processes, which are currently in a prevailing positon regarding AM adaption, hybrid additive technologies enable increased build rates, enhanced build volumes and a reduction of machine changes. In the Laser Metal Deposition (LMD) process, metal powder is deposited through a nozzle and melted by a laser on the surface of the part. By integrating the LMD process into a machining center, good surface roughness and low tolerances can be realized by means of e. g. milling without reclamping. In comparison to powder bed based processes, cost and resource input have not been investigated in detail. In this study, hybrid additive manufacturing technologies are analyzed regarding cost and resource input. A cost model for hybrid additive processes is introduced that enables the analysis of the manufacturing cost structure for a given part. Furthermore, the resource inputs for the operation of a hybrid production machine are estimated.

Quality Cost Monitoring Models in Practice of Woodworking Company in Slovakia

Abstract Since the 1950s, a considerable amount of attentions has been given on the cost of quality in theory and practice. Quality is now seen as an economic category, which can be measured. The paper is focused on the comparison of two models that monitor quality cost - the model PAF (prevention, appraisal a failure) and the process cost model in the selected woodworking company in Slovakia. There were evaluated advantages and disadvantages of these models, solutions and appropriate steps for improvement of management in the area of quality cost were proposed. The importance of this paper can be considered mainly from the point of view to pay attention to the monitoring the quality cost in practical conditions of enterprises. It can be concluded that both models have a great economic benefit for the business. Thanks to them, there were discovered gaps in the area of quality management of the woodworking company which defence the success of the company on the timber market of Slovakia. These gaps can be eliminated through setting the administrative measures and solutions in the investigated area.

Deployment of the Process Cost Model : Consideration as a Tool of Quality Cost Collection

Deployment of the Process Cost Model : Consideration as a Tool of Quality Cost Collection

Optimal Heat Integration in a Coal-Natural Gas Energy Park with CO2 Capture

Computational techniques are used to optimize the design of an integrated energy park consisting of a coal-fired power plant, a CO2 capture system, and an auxiliary natural gas combined cycle plant. Emphasis is placed on the design of heat integration in the combined cycle system, as this heat constitutes most of the energy required for temperature-swing CO2 capture. The facility is constrained to meet a maximum CO2 emission intensity limit while flexibly capturing CO2 to maximize profit. The process and capital cost models of the facility include a detailed treatment of the heat recovery steam generator (HRSG). Computational optimization techniques are used to select gas turbine size, CO2 capture capacity, and the sizes and pressures of HRSG components for HRSG configurations with one, two, and three pressure levels. Facility design is jointly optimized with dispatch using an electricity price- duration curve and natural gas price scenarios of $3/MMBtu, $4.50/MMBtu and $6/MMBtu. System configuration is shown to have a significant impact on economics, with spread in net present value (NPV) among configurations of $39-54 million (2.6-26% of NPV). Joint optimization of design with optimized flexible dispatch is observed to improve NPV by $18-56 million (1.2-27%) as compared to optimization with constant dispatch. Gains from optimization increase with higher natural gas price. Optimal capital cost, approximately $2.1-2.2 billion in all configurations, is higher for configurations with higher number of pressure levels, but does not exhibit strong trends with gas price.

Cost model development using virtual manufacturing and data mining: part I—methodology development

This paper reports on research, the aim of which has been to investigate the use of virtual manufacturing and data mining techniques to automate the identification of manufacturing process time estimating relationships that form the basis of product and process cost models. Such models provide information that is critical to all stages of the product development process and to ensuring that development of cost-effective product design and production methods. Use of virtual manufacturing models and data mining techniques enables the majority of the activities involved in the cost model development process to be automated. Hence, reducing the time and effort required, reducing the current high level of reliance on expert judgment and enabling higher levels of cost detail to be estimated. The research reported, therefore, focuses on the use of virtual manufacturing to generate datasets which are then analysed using data mining to identify suitable cost models for manufacturing processes. Part I, of this two-part paper, describes the development of a model development methodology that makes use of virtual manufacturing models and data mining techniques and uses case study data to validate this methodology. Part II will then examine in detail the effectiveness of alternative data mining algorithms in terms of their ability to develop relationships that are (1) representative of the real causal relationships that exist and (2) provide a high level of estimating accuracy.