Manufacturing Cost Estimation Research Articles

A supplementary labor cost model for estimating the fixed capital cost of commercial-scale algae photobioreactors plants

Scaling up algae photobioreactors (PBR) for commercial production presents challenges in estimating fixed capital investment costs, particularly the labor costs associated with installing low-cost equipment. Using fractions of the equipment cost to estimate the installation labor cost does not necessarily provide a representative estimate for the labor cost as the labor required is no longer proportionate to the equipment cost. In this study, we developed a supplementary labor cost model to estimate equipment installation and overhead costs for scaling up algae PBRs using a manufacturing cost estimation approach. The model consists of four main steps: identifying and sequencing all processes, estimating labor time using the Methods-Time Measurement procedure, estimating process time, and calculating the overall labor cost. As a proof-of-concept, a pilot-sized PBR has been adapted from the literature to illustrate how to implement these four steps to estimate the labor cost for the commercial size. The model provides more accurate labor cost for fixed capital cost estimation to supplement existing fixed capital cost estimation methods. The model also provides insights into the main cost drivers in the equipment installation process that can be used for bioreactor redesign and further capital cost reduction.

Manufacturing Cost Estimation Using Piecewise Function Approaches

<p><big>This paper describes two novel approaches to cost estimation of manufactured products where a data set of similar products have known manufactured costs. The methods use the notion of piecewise functions and are (1) clustering and (2) splines. Cost drivers are typically a mixture of categorical and numeric data which complicates cost estimation. Both clustering and splines approaches can accommodate this. Through four case studies, we compare our approaches with the often-used regression models. Our results show that clustering especially offers promise in improving the accuracy of cost estimation. While clustering and splines are slightly more complex to develop from both a user and a computational perspective, our approaches are packaged in an open-source software. This paper is the first known to adapt and apply these two well-known mathematical approaches to manufacturing cost estimation.</big></p>

METHOD FOR REFERENCE-BASED MANUFACTURING COST ESTIMATION – EVALUATION STUDY USING A PROTOTYPE

AbstractWithin product development, manufacturing cost estimation provides a sound basis for design and management decisions. This secures companies profitability, but the effort is high and deep knowledge at the interface of design, manufacturing and costs is needed. These issues can be eased with automation enabled by semantic technologies. Therefore, the authors developed a method for reference-based manufacturing cost estimation and created a prototype. This research evaluates the method and the prototype. Observation, interview and questionnaire were conducted with ten experienced cost engineers at a large German manufacturing company.Based on its results, the study shows the methods contribution to lower estimation effort, while the impact on transparency and the knowledge base was only partly verified. The method steps show different automation potential, so an incremental automation should be considered. Even though semantic technologies show high potential for identifying reference system elements in this study, the limiting factor for automation in manufacturing cost estimation remains the low availability of product and manufacturing information and missing knowledge of its connection within product development.

MACHINE LEARNING FOR PARAMETRIC COST ESTIMATION OF AXISYMMETRIC COMPONENTS

AbstractMachine learning (ML) is a well-established research topic in Industry 4.0 is boosting its adoption. ML is also used for manufacturing cost estimation during design. Such approaches are commonly used to estimate the cost of mass-produced parts. Many consolidated historical data are available for training the regression models. Unfortunately, very often, such a database of data is not available.The paper defines an ML approach for parametric cost estimation of axisymmetric components. The data for training the ML model derives from automatic software for analytically estimating the manufacturing cost. With a proper set of simulations, the tool can generate a large amount of data for training. The paper presents the steps for developing a parametric cost model using ML. The approach is based on CRoss Industry Standard Process for Data Mining method. The proposed method was used to develop one cost model (to estimate the total cost that considered raw material and manufacturing cost). The obtained Relative Error is 23.52% ± 1.37%, coherent with E2516 − 11, Standard Classification for Cost Estimate Classification System.

Manufacturing cost estimation based on similarity

ABSTRACT Accurate and rapid estimation of manufacturing costs can improve the competitiveness of manufacturers and optimize part design. Several factors affect the cost, including the part design model, machining processing progress, and materials. Extracting a unified dataset from the manufacturing information is complicated owing to different information expressions, such as geometrical-topological, digital, text, and symbol representations. This study proposed a manufacturing cost estimation method based on similarity. The main contribution of this study is the calculation of the similarity of manufacturing factors, particularly the similarity of 3D geometrical models: ① the geometrical-topological representations of a model was transformed into point cloud data and a point cloud similarity calculation method based on the implicit shape model was developed, from which the similarity of the 3D model of the part was obtained;② the manufacturing factors were transformed into digital representations and the similarity of these factors was calculated using Euclidean distance. The weights of these factors were determined using subjective weight coefficients and the similarity of the parts was obtained based on the weights. Finally, the manufacturing cost was estimated using the smoothing index method. The accuracy of the proposed method has a state-of-the-art effect compared to other methods.

Combined manufacturing and cost complexity scores-based process selection for hybrid manufacturing

Smart manufacturing involves the use of emergent technologies and requires dynamic feedback of customer’s demands. These concerns need a rapid Decision Support System (DSS) considering emergent manufacturing processes such as Additive (AM) and Hybrid (HM) Manufacturing and tracking the product changes. This paper proposes a DSS for process selection based on manufacturing complexity and cost. The complexity parameters, deduced from design for manufacturing (DFM), design for additive manufacturing (DFAM) and design for hybrid manufacturing (DFHM) rules, are automatically extracted from computer aided design (CAD) model to follow the product changes. Cost models are defined for each manufacturing process type. In design phase, the manufacturing cost estimation allows considering the cost as a selection factor. The combined complexity based on manufacturing difficulty and cost represents a new paradigm for process selection. The case studies show the reliability of the proposed DSS and its ability to respect the company resources and strategy.

A novel method based on a convolutional graph neural network for manufacturing cost estimation

With the widespread application of mass customization strategy, estimating the manufacturing cost of products to provide suitable references for the quotations of products can assist enterprises to adapt to the competitive market. Moreover, estimating the manufacturing cost of products in the design stage can assist designers in optimizing product designs. With the continuous development of deep learning, studies on part manufacturing cost estimation based on deep learning have started in recent years. However, the existing deep learning-based methods ignore the part’s precision information (e.g. roughness, tolerance) that is important to the manufacturing cost, making them less practical. To this end, how to take the precision information of the parts into account in the manufacturing cost estimation to make deep learning-based methods more applicable is a difficulty that needs to be solved. In this context, an innovative convolutional graph neural network (ConvGNN)-based manufacturing cost estimation approach that considers precision information is suggested. Specifically, an attribute graph that is based on machining features and includes precision information is defined to represent the 3D computer-aided design (CAD) model of a part first. Then, a novel ConvGNN framework named Cost Estimation Network (CEN) is constructed that combines spectral-based convolutional layers and spatial-based convolutional layers. The input of CEN is the attribute graph of a part, while the output is the manufacturing cost. After being trained with historical data and an improved loss function based on Root Mean Squared Log Error (RMSLE), CEN can be applied to estimate manufacturing costs. In addition, since the deep learning model is treated as a black box, a modified gradient-weight class activation mapping (Grad-CAM) process is developed to explain the rationale of the manufacturing cost decision and differentiate the degree of influence of various machining features on the cost of parts. In experimental studies, the computer numerical control (CNC) machined rotary parts are used as examples to verify the feasibility and effectiveness of the presented approach.

Reliability-based bottom-up manufacturing cost optimisation for composite aircraft structures

A novel methodology is presented for the reliability-based manufacturing cost optimisation of composite aircraft structures. A comprehensive bottom-up costing approach is employed, enabling precise manufacturing cost estimation in terms of material, machine, labour, tooling, and indirect costs. This approach splits the manufacturing process into many individual activities, which can be combined in many different ways, allowing the proposed optimisation methodology to be applied to a wide range of composite aircraft structures. A genetic algorithm (GA) is coupled with a deep neural network (DNN) to efficiently determine the optimal composite ply stacking sequence for every part of an assembled structure. A numerical example featuring a composite-stiffened aircraft fuselage panel is investigated. The reliability of the panel is measured in terms of its buckling resistance, and its manufacturing cost is estimated based on the individual costs of over 20 activities. The labour times for each activity were estimated based on data collected from an aerospace company specialising in the manufacture of advanced composite aircraft structures. Results indicate that material, machine, labour, and tool costs can vary significantly depending on the level of structural reliability required, demonstrating the importance of accounting for non-material costs when designing composite aircraft structures.

Feature-Based and Process-Based Manufacturing Cost Estimation

The demand for mass custom parts is increasing, estimating the cost of parts to a high degree of efficiency is a matter of great concern to most manufacturing companies. Under the premise of machining operations, cost estimation based on features and processes yields high estimation accuracy, but it necessitates accurately identifying a part’s machining features and establishing the relationship between the feature and the cost. Accordingly, a feature recognition method based on syntactic pattern recognition is proposed herein. The proposed method provides a more precise feature definition and easily describes complex features using constraints. To establish the relationships between geometric features, processing modes, and cost, this study proposes a method of describing the features and the processing mode using feature quantities and adopts deep learning technology to establish the relationship between feature quantities and cost. By comparing a back propagation (BP) network and a convolutional neural network (CNN) it can be concluded that a CNN using the “RMSProp” optimizer exhibits higher accuracy.

Knowledge graph for manufacturing cost estimation of gear shafts - a case study on the availability of product and manufacturing information in practice

Growing cost pressure forces companies to actively manage their product costs to secure profitability. Here, manufacturing cost estimation within product development estimates manufacturing and material costs. As most products are developed in generations, needed product and manufacturing information can origin from reference system elements (RSE), for example similar components of prior product generations. Problematically, this product and manufacturing information as well as the knowledge of its interrelation is often stored in an unstructured way, document based or at least not machine-readable. This makes manufacturing cost estimation an effortful, time consuming and mainly manual activity with low traceability, where a wide manufacturing knowledge is required. Trends in production, like new manufacturing processes and production systems further increase the need for manufacturing information and knowledge. Knowledge graphs as semantic technologies can improve the findability and reusability of reference system elements and enable automatic information processing.Within this research, cost estimation of research and development of a large automotive supplier was used as research environment. Guided by the model of PGE an ontology for the manufacturing cost estimation domain was developed. Then, a knowledge graph was instantiated based on product and manufacturing information from gear shafts of electric axles. A case study was carried out to evaluate process-specific cycle time calculation as exemplary use case of the knowledge graph. Process-specific cycle times are generally effortful estimated based on detailed manufacturing information and then used together with machine hourly rates to estimate manufacturing costs. Here, the structured and machine-readable manufacturing information of identified reference system elements is extracted from the knowledge graph to reduce the effort, increase the traceability and enable future automation. The case study shows exemplary, how a knowledge graph can support manufacturing cost estimation of gear shafts where product and manufacturing information is automatically identified using reference system elements.

Explainable artificial intelligence for manufacturing cost estimation and machining feature visualization

Studies on manufacturing cost prediction based on deep learning have begun in recent years, but the cost prediction rationale cannot be explained because the models are still used as a black box. This study aims to propose a manufacturing cost prediction process for 3D computer-aided design (CAD) models using explainable artificial intelligence. The proposed process can visualize the machining features of the 3D CAD model that are influencing the increase in manufacturing costs. The proposed process consists of (1) data collection and pre-processing, (2) 3D deep learning architecture exploration, and (3) visualization to explain the prediction results. The proposed deep learning model shows high predictability of manufacturing cost for the computer numerical control (CNC) machined parts. In particular, using 3D gradient-weighted class activation mapping proves that the proposed model not only can detect the CNC machining features but also can differentiate the machining difficulty for the same feature. Using the proposed process, we can provide a design guidance to engineering designers in reducing manufacturing costs during the conceptual design phase. We can also provide real-time quotations and redesign proposals to online manufacturing platform customers.

An approach of CAD/CAM data reuse for manufacturing cost estimation

ABSTRACT The manufacturing industries must correctly and quickly predict the production cost of a new product to face increasingly stiff competition. The proper reuse of the company’s know-how guarantees the correct estimation of the manufacturing cost while respecting the required quality. The use of the Digital Mock-Up (DMU) leads to the rapid determination of the manufacturing cost from the CAD data. Thus, this paper describes an approach to estimate manufacturing cost and to produce the machining strategy of a new model by the reuse of the manufacturing expertise, acquired during old projects. The new product closely related and similar to a previous design must be found. Thus, a comparison tool is developed, in manufacturing semantics. This tool quantifies the similarity between the query CAD model and company database. The tolerance values as well as geometrical and topological parameters are considered in the similarity calculation. Feature data modelling under a standard structure is used. Two cost computation methods are proposed. Consequently, the estimated cost and machining strategy, of the studied model, are deduced. A case study is presented to show the feasibility of the proposed approach to estimate the cost and to suggest a machining strategy during the design phase.

Challenges towards large-scale fuel cell production: Results of an expert assessment study

Fuel cell electric vehicles are a promising alternative on the way to emission-free mobility. However, there is still a great deal of uncertainty as to how this change can be implemented technologically. Despite various research and development activities on fuel cells in the past two decades, a real breakthrough of fuel cell technology has not yet been reached. The aim of this paper is therefore to identify barriers to a commercialized production of PEM fuel cell stacks. For this purpose, a comprehensive expert study is performed, consisting of a qualitative, exploratory and a quantitative, hypothesis-confirming step. As a result, technical and non-technical barriers are examined and described in this paper. A cost estimation of today's actual manufacturing cost is presented as identified in the study. Conclusively, future research topics and needs for action are derived.

Manufacturing cost estimation based on the machining process and deep-learning method

With the extensive application of mass customization, fast and accurate responses to customer inquiries can not only improve the competitive advantage of an enterprise but also reduce the cost of parts at the design stage. Most cost estimation methods establish a regression relationship between features and cost based on the processing features of parts. Traditional methods, however, encounter certain problems in feature recognition, such as the inaccurate recognition of processing features and low efficiency. Deep-learning methods have the ability to automatically learn complex high-level data features from a large amount of data, which are studied to recognize processing features and estimate the cost of parts. First, this study proposes a novel three-dimensional (3D) convolutional neural network (CNN) part-feature recognition method to achieve highly accurate feature recognition. Furthermore, an innovative method of using the quantity to express the identified features and establishing the relationship between them and cost is proposed. Then, support vector machine and back propagation (BP) neural network methods are employed to establish a regression relationship between the quantity and cost. Finally, in comparison with the mean absolute percentage error values, the BP neural network yields a more accurate estimation, which has considerable application potential.

Additive manufacturing cost estimation models—a classification review

With the recent evolution of additive manufacturing (AM), accurate cost prediction models are of increasing importance to assist decision-making during product development tasks. Estimating the cost is a challenging task in that it requires a vast amount of manufacturing knowledge that has to be synchronised with many aspects from design to production. As a result, various AM cost models have been developed. This review is performed with the aim of providing an overview of the costing models being developed and utilised associated with the additive manufacturing product development phases. For a better understanding in this field, it is required to become familiar with the various terminologies, perspectives, concepts, techniques, and approaches used in developing these models. It was observed that the contexts and views described during the development of the models were often targeted at specific applications as well as technologies and were classified in many ways. Accordingly, the paper compiles different aspects of the cost estimation classification technique and provides definitions of some of the key terminologies. The main motivation is to provide broad and in-depth reviews of the estimation models developed over the past three decades using a systematic classification approach. From the review, a visualisation of future insights into the AM cost-oriented estimation framework from the perspective of various AM users can be better understood.

Economically Sustainable Growth of Perovskite Photovoltaics Manufacturing

Summary The significant capital expense of photovoltaics manufacturing has made it difficult for new cell and module technologies to enter the market. We present two technoeconomic models that analyze the sustainable growth of perovskite manufacturing for an R2R single-junction technology and a perovskite-silicon tandem module, focusing on the impacts of economies of scale and average selling price on profitability. We establish a cost range of $3.30/W to $0.53/W for flexible modules manufactured in factory sizes ranging from 0.3 MW/year to 1 GW/year. In addition, we model the cost to manufacture a tandem module consisting of a single-junction perovskite cell stacked in 4-terminal configuration onto a silicon cell and show how an existing manufacturer can grow at a faster rate by co-investing in tandems. Our analyses highlight potential routes to market for perovskite photovoltaics and the possibility to sustainably grow a photovoltaics manufacturing company even in markets with higher labor rates.

Manufacturing cost estimation based on a deep-learning method

In the era of the mass customisation, rapid and accurate estimation of the manufacturing cost of different parts can improve the competitiveness of a product. Owing to the ever-changing functions, complex structure, and unusual complex processing links of the parts, the regression-model cost estimation method has difficulty establishing a complex mapping relationship in manufacturing. As a newly emerging technology, deep-learning methods have the ability to learn complex mapping relationships and high-level data features from a large number of data automatically. In this paper, two-dimensional (2D) and three-dimensional (3D) convolutional neural network (CNN) training images and voxel data methods for a cost estimation of a manufacturing process are proposed. Furthermore, the effects of different voxel resolutions, fine-tuning methods, and data volumes of the training CNN are investigated. It was found that compared to 2D CNN, 3D CNN exhibits excellent performance regarding the regression problem of a cost estimation and achieves a high application value.

Synthesis of cyclopentadiene trimer (tricyclopentadiene) over various zeolites: A feasibility study for scale-up reactions and comparative economic analysis between two-step and one-pot processes using an engineering estimation

Several researchers have continuously studied the development of HEDFs using tricyclopentadiene (TCPD) as a raw material. However, the production of high-energy-density fuels (HEDFs) using the TCPD raw material has been performed mainly in a laboratory-scale process, and there is no clear standard to judge the value of commercialization. Therefore, in this study, the scale-up effects were investigated for four reaction cases (1. cycloaddition of endo-dicyclopentadiene (DCPD) over Al-MCM-41; 2. isomerization of endo-tetrahydrotricyclopentadiene (THTCPD) over HY(30); 3. one-pot DCPD oligomerization and TCPD isomerization over MMZ-Hβ(300); 4. one-pot DCPD oligomerization and TCPD isomerization over Hβ(25)) for manufacturing highly valuable TCPD. These four cases were analyzed and compared with the results of corresponding laboratory-scale tests for checking the reproducibility of each reaction to some degree. Additionally, the measurement and analysis of the important major physical properties of the final products from each reaction case were carried out according to ASTM (American Society for Testing and Materials) methods such as specific gravity, heat of combustion, viscosity and freezing point. Finally, the comparative economic analysis was performed between two-step process of the separate oligomerization/isomerization and one-pot oligomerization/isomerization considering the regeneration of catalysts through the estimation of the unit manufacturing cost for each pilot scale production using an engineering estimate method based on the reaction conditions for the scale-up test results. As a result, the two-step process has a strong point in the relatively higher TCPD yield despite the disadvantage of the relatively longer process step.

Biodiesel Production from Castor Oil by Two-Step Catalytic Transesterification: Optimization of the Process and Economic Assessment

The use of biodiesel and the requirement of improving its production in a more efficient and sustainable way are becoming more and more important. In this research work, castor oil was demonstrated to be an alternative feedstock for obtaining biodiesel. The production of biodiesel was optimized by the use of a two-step process. In this process, methanol and KOH (as a catalyst) were added in each step, and the glycerol produced during the first stage was removed before the second reaction. The reaction conditions were optimized, considering catalyst concentration and methanol/oil molar ratio for both steps. A mathematical model was obtained to predict the final ester content of the biodiesel. Optimal conditions (0.08 mol·L−1 and 0.01 mol·L−1 as catalyst concentration, 5.25:1 and 3:1 as methanol/oil molar ratio for first and second step, respectively) were established, taking into account the biodiesel quality and an economic analysis. This type of process allowed cost saving, since the amounts of methanol and catalyst were significantly reduced. An estimation of the final manufacturing cost of biodiesel production was carried out.

Uncertainty propagation and sensitivity analysis in composite manufacturing cost estimation: ALPHA-framework and cost tool development

The presented ALPHA cost tool is a novel highly flexible bottom-up parametric hybrid cost estimation framework. It combines the benefits of both methods with the aim of providing cost information during all product development phases. The software offers full transparency to the user and advanced two-level uncertainty management to not only understand any project’s cost structure but also aid to identify its cost driving parameters. The implementation of sensitivity analysis makes the intrinsic uncertainty inevitable embedded in cost estimation become graspable. Gaussian error propagation offers direct feedback without extra calculation time while classic Monte Carlo Simulation gives detailed insight through post estimation analysis. From the vast number of commercially available or self-developed cost tools many probably already incorporate uncertainty measures similar to those proposed here. But this article shows both the potential of the additionally obtainable information from uncertainty propagation and demonstrates a way of integrating these risk considerations into a self-developed cost tool.