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The decision-making for additive manufacturing (AM) process selection is typically applied in the end of the product design stages based upon an already finished design. However, due to unique characteristics of AM processes, the part needs to be designed for the specific AM process. This requires potentially feasible AM techniques to be identified in early design stages. This paper aims to develop such a decision-making methodology that can seamlessly be integrated in the product design stages to facilitate AM process selection and assist product/part design.

The decision-making methodology consists of four elements, namely, initial screening, technical evaluation and selection of feasible AM processes, re-evaluation of the feasible process and production machine selection. Prior to the design phase, the methodology determines whether AM production is suitable based on the given design requirements. As the design progresses, a more accurate process selection in terms of technical and economic viability is performed using the analytic hierarchy process technique. Features that would cause potential manufacturability issues and increased production costs will be identified and modified. Finally, a production machine that is best suited for the finished product design is identified.

The methodology was found to be able to facilitate the design process by enabling designers to identify appropriate AM technique and production machine, which was demonstrated in the case study.

This study addresses the gap between the isolated product design and process selection stages by developing the decision-making methodology that can be integrated in product design stages.

As the last link of product remanufacturing, reassembly process is of great importance in increasing the utilization of remanufactured parts as well as decreasing the production cost for remanufacturing enterprises. It is a common problem that a large amount of remanufactured part/reused part which past the dimension standard have been scrapped, which have increased the production cost of remanufacturing enterprises to a large extent. With the aim to improve the utilization of remanufacturing parts with qualified quality attributes but exceed dimension, the purpose of this paper is to put forward a reassembly classification selection method based on the Markov Chain.

To begin with, a classification standard of reassembly parts is proposed. With the thinking of traditional ABC analysis, a classification management method of reassembly parts for remanufactured engine is proposed. Then, a homogeneous Markov Chain of reassembly process is built after grading the matching dimension of reassembly parts with different variety. And the reassembly parts selection model is constructed based on the Markov Chain. Besides, the reassembly classification selection model and its flow chart are proposed by combining the researches above. Finally, the assembly process of remanufactured crankshaft is adopted as a representative example for illustrating the feasibility and the effectiveness of the method proposed.

The reassembly classification selection method based on the Markov Chain is an effective method in improving the utilization of remanufacturing parts/reused parts. The average utilization of remanufactured crankcase has increased from 35.7 to 80.1 per cent and the average utilization of reused crankcase has increased from 4.2 to 14 per cent as shown in the representative example.

The reassembly classification selection method based on the Markov Chain is of great importance in enhancing the economic benefit for remanufacturing enterprises by improving the utilization of remanufactured parts/reused parts.

This paper aims to propose a view similarity-based shape complexity metric to guide part selection for additive manufacturing (AM) and advance the goals of design for AM. The metric helps to improve the selection process by objectively screening a large number of parts and identifying the parts most suited for AM and enabling experts to prioritize parts from a smaller set based on relevant subjective/contextual factors.

The methodology involves calculating a part’s shape complexity based on the concept of view similarity, that is, the similarity of different views of the outer shape and internal cross-sectional geometry. The combined shape complexity metric (weighted sum of the external shape and internal structure complexity) has been used to rank various three dimensional (3D) models. The metric has been tested for its sensitivity to various input parameters and thresholds are suggested for effective results. The proposed metric’s applicability for part selection has also been investigated and compared with the existing metric-based part selection.

The proposed shape complexity metric can distinguish the parts of different shapes, sizes and parts with minor design variations. The method is also efficient regarding the amount of data and computation required to facilitate the part selection. The proposed method can detect differences in the mass properties of a 3D model without evaluating the modified parameters. The proposed metric is effective in initial screening of a large number of parts in new product development and for redesign using AM.

The proposed metric is sensitive to input parameters, such as the number of viewpoints, design orientation, image resolution and different lattice structures. To address this issue, this study suggests thresholds for each input parameter for optimum results.

This paper evaluates shape complexity using view similarity to rank parts for prototyping or redesigning with AM.

Additive manufacturing (AM) allows companies to create additional value in the processes of new product development and order fulfillment. One of the challenges for engineers is to identify suitable parts and applications for additive manufacturing. The purpose of this paper is to investigate the relation between value creation and the design process. The implications of this relation provide an orientation on the methods for identifying parts and applications for additive manufacturing.

Mapping the value clusters of AM on design strategies allows determining the expected degree of change in design. A classification into major and minor design changes is introduced to describe the predictability of the impact of AM on past performance and business model. The ability to predict the future properties of an AM part determines the suitability of identification and selection methods from literature. The mapping is validated by an identification process that creates a shortlist of potential AM parts based on the strategic decision for a value cluster. Shortlisted parts are then evaluated based on the criteria technology readiness, required post-processing, customer benefit and manufacturer benefit.

The mapping of value clusters on expected design changes determines the type of selection process. For minor design changes, automated part identification serves as a powerful tool while major design changes require the judgment of skilled engineers.

The mapping of value clusters to design strategies and degree of change in design is based on empirical observations and conclusions. The mapping has been validated in an industrial context in different identification and selection processes. Nevertheless the versatility of AM and industrial environments impede a universal validity of high-level concepts.

This value-driven process of identification and selection was applied in technology transfer projects and proved to be useful for AM novices and experts. The mapping supports the identification and selection process, as well as the general product development process by providing an indication of the design effort for implementing AM.

The novel mapping links the economic domain of value creation to the engineering domain of design strategies to provide guidance in the selection of economically and technically suitable parts for additive manufacturing.

The selection of part types for simultaneous processing in a Flexible Manufacturing System (FMS) is one of the most responsible phases of the short‐time production planning. The batching approach to part type selection is more popular in practice but this leads to the requirement for a preliminary evaluation of the results obtained. Using simulation is a convenient, no‐risk and cheapest way to do this, which is shown and discussed in the paper. The objective of batching approach is to maximize the number of part types in a batch taking into account constraints on tool magazine capacity and tool type availability. Two alternative ways of assigning weights in the objective function of the part type selection model are explored to direct the search to a different set of part types. A procedure to determine the mix ratios of the selected part types is used so as to balance the aggregate machine workloads. The model aggregations have been accounted for in a simulation experiment conducted to evaluate the performance of the batching approach and to investigate the sensitivity of total makespan and machine utilizations.

This paper aims to present a methodology to help end-users to find appropriate part candidates for the use of the additive manufacturing (AM) technology. These shall be capable of bringing AM into their businesses. The concept furthermore includes approaches for redesigning current available parts and helps to estimate the economic implications of the use of the technology.

The approach starts to discuss general economic aspects for the successful use of AM. While describing the introduction of new technologies into existing businesses, the importance of an appropriate part selection for AM is pointed out. A methodology for a part selection process is presented, and the different criteria are developed. An approach for a redesign of the selected parts, including the gathering of requirements, is given based on different sample parts. A variation of criteria to include measures for product piracy is highlighted.

The methodology has proven applicability in several research and industry projects in aerospace applications. Independent part selections from experts analyzed within a project of the European Space Agency had a 90 per cent overlap with the results. It allows companies with only basic AM knowledge to start a part screening for applicable AM candidates in their own company with a reasonable effort.

The methodology for the redesign process helps to identify the main functions of the products targeted and the relevant environment, so one can benefit from the various advantages that AM has to offer. The selection methodology helps to ask the right questions and to reduce the effort.

Suggests that, without question, while every step in a systematic approach to the design of parts for assembly using integral snap‐fit features is important, none is more important than selecting locking features. After all, it is these features that hold the assembly together. While quite different in appearance and details of their operation, all integral locking features comprise a latch and a catch component to create a locking pair. Proper, no less optimum, function requires that such locking pairs be selected using a systematic approach. Presents that approach as a six‐step methodology, but first, defines and describes latch and catch components, bringing order to their apparent boundless variety. Demonstrates the utility of the methodology with a real‐life case study.

Introducing additive manufacturing (AM) in a multinational corporation with a global spare parts operation requires tools for a dynamic supplier selection, considering both cost and delivery performance. In the switchover to AM from conventional manufacturing, the objective of this study is to find situations and ways to improve the spare parts service to end customers.

In this explorative study, the authors develop a procedure – in collaboration with the spare parts operations managers of a case company – for dynamic operational decision-making for the selection of spare parts supply from multiple suppliers. The authors' design proposition is based on a field experiment for the procurement and delivery of 36 problematic spare parts.

The practice intervention verified the intended outcomes of increased cost and delivery performance, yielding improved customer service through a switchover to AM according to situational context. The successful operational integration of dynamic additive and static conventional supply was triggered by the generative mechanisms of highly interactive model-based supplier relationships and insignificant transaction costs.

The dynamic decision-making proposal extends the product-specific make-to-order practice to the general-purpose build-to-model that selects the mode of supply and supplier for individual spare parts at an operational level through model-based interactions with AM suppliers. The successful outcome of the experiment prompted the case company to begin the introduction of AM into the company's spare parts supply chain.

Because of the significant differences in the features and requirements of specific products and the capabilities of various additive manufacturing (AM) solutions, selecting the most appropriate AM technology can be challenging. This study aims to propose a method to solve the complex process selection in 3D printing applications, especially by creating a new multicriteria decision-making tool that takes the direct certainty of each comparison to reflect the decision-maker’s desire effectively.

The methodology proposed includes five steps: defining the AM technology selection decision criteria and constraints, extracting available AM parameters from the database, evaluating the selected AM technology parameters based on the proposed decision-making methodology, improving the accuracy of the decision by adopting newly proposed weighting scheme and selecting optimal AM technologies by integrating information gathered from the whole decision-making process.

To demonstrate the feasibility and reliability of the proposed methodology, this case study describes a detailed industrial application in rapid investment casting that applies the weightings to a tailored AM technologies and materials database to determine the most suitable AM process. The results showed that the proposed methodology could solve complicated AM process selection problems at both the design and manufacturing stages.

This research proposes a unique multicriteria decision-making solution, which employs an exclusive weightings calculation algorithm that converts the decision-maker's subjective priority of the involved criteria into comparable values. The proposed framework can reduce decision-maker's comparison duty and potentially reduce errors in the pairwise comparisons used in other decision-making methodologies.

The purpose of this paper is to change the traditional supplier selection methods by shifting the emphasis from using a single model to using multiple models in the unstructured decision‐making context and to provide a tool for decision makers to make informed decisions of supplier selection in the multiple perspectives.

There are various supplier selection models available in the literature. However, using the result of a single model as a basis for making the final decision could lead to a biased decision given the fact that any model has its limitations. The qualities of the decision‐making process and the decision itself increase by applying a multiple perspectives approach rather than a single model. The multiple perspectives decision‐making allows collaboration and knowledge sharing among the participants which leads to a less‐biased decision. This study examines commonly applied supplier selection models, formulates general perspectives of these models, and proposes a framework of multiple perspectives decision making for supplier selection. It further provides a structure of supplier selection system based on the proposed approach. Through a prototype of web portal, the study demonstrates the usefulness of the proposed multiple perspective system approach in the decision context of collaboration and knowledge sharing.

The general finding from this study is that the multiple perspectives approach to supplier selection enables the decision makers to actively participate and fully understand the decision‐making process through knowledge sharing which in turn ensures high quality of the final decisions.

Supplier selection decision makers can make more informed decisions through collaboration among all decision‐making participants in the multiple perspectives. It informs supply chain managers of the potentially positive effect of knowledge sharing on the decision‐making process in supplier selection.

Multiple perspectives decision making provides a novel approach that emphasizes on the knowledge sharing and collaboration between the experts, who are familiar with the supplier relations, and the decision makers who are responsible for the final decisions.