A top-down method for the derivation of metrics for the assessment of design automation potential

Rigger, Eugen; Vosgien, Thomas; Shea, Kristina; Stanković, Tino

doi:10.1080/09544828.2019.1670786

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…The method chosen to evaluate the DHAM in industrial design scenarios and with expert designers is a series of workshops with three engineering companies. This evaluation method and results reporting is based specifically on the work of Hamraz & Clarkson (2015), but workshops as a method for evaluation of design methods in industry are commonly reported (Laverne et al 2015;Kumke et al 2017;Rigger et al 2019;Dordlofva & Törlind 2020;Prabhu et al 2020a). This evaluation method is chosen because it focusses on evaluation of the method and not of the individual results generated by each company.…”

Section: Methodsmentioning

confidence: 99%

“…If both subgroups are working on the same product or concept, each subgroup freely brainstorms for 45 minutes. Then, the ideas generated by one subgroup, in the form of annotated sketches, are passed to the other and each subgroup uses the other subgroup’s ideas as the basis for a Brainwriting 6-3-5 session (Rhorbach 1969), adjusted to the number of ideas and participants. This second portion of the ideation session lasts no more than 15 minutes.…”

Section: Methodsmentioning

confidence: 99%

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Industrial evaluation of design heuristics for additive manufacturing

Blösch-Paidosh

Shea

2022

Des. Sci.

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Additive manufacturing (AM) has gained prominence over the last 15 years and become a viable manufacturing option. Since there is great industrial interest to implement serial production of products using AM, the education of engineers in design for additive manufacturing (DfAM) is important today. DfAM extends on design for manufacturing to provide knowledge about the new design opportunities enabled by AM. A set of design heuristics for additive manufacturing (DHAM) to assist designers with conceptual-level DfAM has previously been proposed. In this paper, these DHAM are evaluated through workshops with 3 engineering companies and 19 engineers, in which the participants re-design existing company products for AM using the DHAM as an aid, and then give feedback. The DHAM are well received by the workshop participants, and they find that the DHAM are good for teaching others about the capabilities of AM, provide a checklist of items to consider to help avoid oversights, and encourage the generation of new ideas. Criticisms include the number of examples provided and the lack of information about how to implement the ideas concretely. It is also found that the DHAM fulfil 16 of 18 criteria for early design phase DfAM methods, identified in academic literature.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Industrial evaluation of design heuristics for additive manufacturing

Blösch-Paidosh

Shea

2022

Des. Sci.

Self Cite

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“…redundant data generation that causes time to maintain and synchronize. This allows to derive related metrics [29] that can be used to validate the initially defined goal. In addition, the identified sources of waste permit to narrow down the amount of relevant data that needs to be investigated, e.g.…”

Section: Value Stream Mapping/dpfmeamentioning

confidence: 99%

“…Approximately 70% of the product costs are determined during the design stage, making it crucial to come to informed decisions at this stage of product development [2,9,10]. In this respect, the use of computational methods and tools has shown to be an enabler to increase design performance [14] regarding efficiency and effectiveness of product development [29,33]. Nevertheless, state of practice still does not reflect the opportunities computational methods provide in the early stages of engineering design [28].…”

Section: Introductionmentioning

confidence: 99%

Participative Method to Identify Data-Driven Design Use Cases

Rädler¹,

Rigger²

2020

Product Lifecycle Management Enabling Smart X

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Paradigms such as smart factory and industry 4.0 enable the collection of data in enterprises. To enhance decision making in design, computational support that is driven by data seems to be beneficial. With this respect, an identification of data-driven use cases is needed. Still, the state of practice does not reflect the potential of data-driven design in engineering product development. With this respect, a method is proposed addressing the business and data understanding in industrial contexts and corresponding Product Lifecycle Management (PLM) environments. This allows to identify use cases for data-driven design taking into account business processes as well as the related data. In the proposed method, first the main process tasks are analyzed using a SIPOC analysis that is followed by a process decomposition to further detail and highlight corresponding applications using Enterprise Architecture principles. Following this, value stream mapping and design process failure mode effect analysis are used to identify sources of waste and the related causes. With this, a feature analysis of given data is proposed to identify use cases and enable to further use standard data science methods like CRISP-DM. The method is validated using the infrastructure of the Pilotfabrik at TU Vienna. The use case shows the applicability of the method to identify features that influence the cost of a product during the manufacturing without changing the functional specifications. The results highlight that different methods need to be combined to attain a comprehensive business and data understanding. Further, a comprehensive view of the processes is yielded that enables to further identify use cases for data-driven design. This work lays a foundation for future research with respect to data-driven design use cases identification in engineering product development. Keywords: Data-driven-design • PLM • Enterprise architecture • ArchiMate • SysML This work has been partially supported and funded by the Austrian Research Promotion Agency (FFG) via the "Austrian Competence Center for Digital Production" (CDP) under the contract number 854187.

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“…Further, the engineering domain is characterized by domain specific tools featuring support for highly specific tasks, e.g. CAx [30] or design automation [31], [32]. Still, the works presented in [33], [34] have shown that the principles of enterprise architecture are also applicable in a PLM context.…”

Section: Enterprise Architecturementioning

confidence: 99%

Enterprise Architecture Method for Continuous Improvement of PLM Based on Process Mining

Rigger¹,

Vosgien²,

Bitrus³

et al. 2020

Product Lifecycle Management Enabling Smart X

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The complexity and dynamics of IT landscapes and related PLM strategies of engineering enterprises are continuously growing due to trends such as Industry 4.0 and ever shorting product development cycles. To ensure interoperability, robustness, flexibility and efficiency of the IT systems and PLM, methods are needed that can handle these dynamics and complexities. In this paper, a method is presented that combines principles from enterprise architecture as well as business process mining to enable continuous improvement of PLM processes and the related IT systems. In particular, process mining is applied to validate the alignment of IT systems with related PLM processes. The method is demonstrated using an industrial case study that highlights the requirements from industrial practice and the applicability of the approach for PLM related processes. The method is shown to be particularly beneficial for the enterprise architects to support them with quantitative data as a basis for the design of continuous improvement cycles to make the PLM evolve. Future work will address the application of process mining for PLM related processes with distributed IT systems and the handling of the related complexity.

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A top-down method for the derivation of metrics for the assessment of design automation potential

Cited by 8 publications

References 23 publications

Industrial evaluation of design heuristics for additive manufacturing

Industrial evaluation of design heuristics for additive manufacturing

Participative Method to Identify Data-Driven Design Use Cases

Enterprise Architecture Method for Continuous Improvement of PLM Based on Process Mining

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