Metal material extrusion is a family of metal additive manufacturing that includes atomic diffusion additive manufacturing (ADAM) and bound metal deposition (BMD). In the literature, there are just a few cost models for ADAM and no one for BMD. The paper presents an analytic cost model for BMD. It considers the entire process: pre-processing, printing and post-processing. The total manufacturing cost is split into material, machine, labour, energy and consumables items. The cost model validation on a 3D-printed part determined an accuracy of 98%.

Within 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 (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.

Laser Powder Bed Fusion is the most widespread additive manufacturing process for metals. In literature, there are several analytical models for estimating the manufacturing cost. However, few papers present sensitivity analyses for evaluating the most relevant product and process parameters on the production cost. This paper presents a cost model elaborated from previous studies used in a sensitivity analysis. The most relevant process parameters observed in the sensitivity analysis are the 3D printer load factor, layer thickness, raw material price and laser speed.

The ever-increasing competitiveness, due to the market globalisation, has forced the industries to modify their design and production strategies. Hence, it is crucial to estimate and optimise costs as early as possible since any following changes will negatively impact the redesign effort and lead time.

This paper aims to compare different parametric cost estimation methods that can be used for analysing mechanical components. The current work presents a cost estimation methodology which uses non-historical data for the database population. The database is settled using should cost data obtained from analytical cost models implemented in a cost estimation software. Then, the paper compares different parametric cost modelling techniques (artificial neural networks, deep learning, random forest and linear regression) to define the best one for industrial components.

Such methods have been tested on 9 axial compressor discs, different in dimensions. Then, by considering other materials and batch sizes, it was possible to reach a training dataset of 90 records. From the analysis carried out in this work, it is possible to conclude that the machine learning techniques are a valid alternative to the traditional linear regression ones.

Estimating the costs of products during development to design a cost efficent product is a well established process. But in the case of Product-Service Systems estimating the costs of the individual product is not sufficent. Instead it is necessary to calculate the cost incured over the entire life cycle of the product. Because with Product-Service Systems the majority of costs is not incurred during manufacturing of the product but instead during the operation. One of the major cost components accruing during the operation of the product are the maintennace costs. Therefore, current life cycle costing models show the impoact of component design on the maintennace cost of the Product-Service System. But they do not show how different maintennace strategies that can have an impact on the overall life cycle costs of the Product-Service System. Thus, this paper shows a method for the implementation of different maintennace strategies into life cycle costing and applies it in an industrial use case.

Additive manufacturing (AM), which was first applied for rapid prototyping, is now becoming a real option for small-batch production of final products. Further expansion of AM is closely correlated to production costs. AM can only become competitive to traditional manufacturing methods if a product is designed for AM already from the beginning as it is an expensive technology that should only be applied if it adds enough value to the product. The aim of this paper is to increase cost awareness in the conceptual design phase and to support product developers in doing AM cost estimation and process selection. The proposed model integrates design for AM and costs calculation. The input data to the process is preliminary design and design requirements. The main contribution of this paper is the multi-criteria AM function, which enables concurrent consideration of different technical and economical criteria. The multi-criteria AM function helps to compare how AM processing and product design parameters influence the product cost. The holistic overview of different options increases the solution space and enables product optimization in several iterations.

Analytical cost estimation of investment casted products during design phase is a complex task since the quantity of parameters to be evaluated. So far, there is a short literature on such cost estimation models. This paper attempts to improve the cost model presented by Boothroyd and Dewhurst. Improvements (mainly focused on cluster assembly and investment, sintering and melting phases) were defined and verified in cooperation with two foundries. Tested on eight components, deviation between estimated and actual costs is around 14% for manual production lines and 6% for automatic ones.

In the design phase of product development (PD) process, most new products face significant uncertainties and risks. Uncertainty is typically associated with a lack of information, while learning is a process that acquires information. Therefore, learning fast and at low cost decreases the uncertainty and increases the efficiency of the product design phase. This paper investigates the concept of the cost of learning in PD's design phase. Reviewing the literature, we conceptualize the cost of learning and review the learning methods considering three aspects in the design phase of the PD process: (1) costs associated with learning from mistakes and failures, (2) learning methods and (3) categories of learners. This paper thus provides the conceptual foundations for future work to increase the efficiency of the PD process by reducing the cost of learning from mistakes and failures.

The aerospace industry experiences a considerable growth in product development costs. Many research works aim at identifying evolution laws characterizing this large-scale phenomenon and at developing design strategies which could help mitigate it. This paper aims to clarify the evolution dynamics governing this phenomenon by studying how the products delivered by these costly projects evolve with time. Increasing complexity is often held responsible for surging costs. If complexity is generally defined as the price to be paid for improving product functionalities, it is rarely specified whether the improvement affects existing functionalities or involves new ones. We aim to identify the patterns of cost growth which can be associated with phenomena of existing functionalities upgrade and new functionalities introduction, and to identify the associated design capabilities that designers need to deploy in order to keep product change and cost growth under control. To that end, we introduce a model which generates curves, each of which featuring a trend of cost growth, specific to a scenario of product evolution and being interpretable as a signature of a strategy used by designers.

Recent reports and predictions indicate a consistent and continuous growth in the field of Research and Development. Such growth leads to increased resource investments, which have to be managed effectively to eventually achieve value maximization. This management is cohesive with budgeting. In changing environments, said effectiveness can be difficult to attain.

Agile development is supposed to provide the necessary flexibility for uncertain situations and has recently seen a stark adoption incline. Unfortunately, budgeting and resource allocation have not yet been resolved for agile approaches: a comprehensive research including recent publications showed a lack of models and frameworks for the adoption and application of budgeting with agile development. Due to this lack of a comprehensive approach, as well as limitations and restrictions of existing research, this paper describes the design of a budgeting approach suitable for and compatible with agile product development. The developed solution, the Structured Agile Budgeting Process, provides a holistic and interdisciplinary way to allocate resources while still allowing the flexibility and benefits of agile development.

With the spread of product-service systems as business models the life cycle costs are of increasing importance as a measurement of product cost. A key factor that drives these costs is the desired reliability of the products used to provide the service. Since the customer usually expects as uninterrupted service availability, it is imperative to achieve the the required reliability. Therefore a large variety of methods has been developed to maximize the reliability of a product. But these approaches focus on the maximization of the reliability and disregard the resulting product costs. This can lead to designs that over perform concerning their reliability requirements but also exceed their target costs. Which will result in the product-service system not being competitive in the marketplace or lowering the company's profit. This paper shows an approach on how to use markov chains to enable a quick comparison of life cycle costs from different product-service system designs With this it will be possible to make better informed decisions about the costs of a system while still meeting the reliability targets.