Automated Product Functionality and Design Optimization Instancing a Product-Service System

Wolniak, Philipp; Sauthoff, Bastian; Kloock-Schreiber, D.; Lachmayer, Roland

doi:10.1017/dsd.2020.323

Cited by 4 publications

(6 citation statements)

References 14 publications

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“…20 1.0 1.0 1.0 1.0 0.6 0.7 0.7 1.0 1.0 1.0 0.9 0.8 0.9 0.9 0.9 0.9 1.0 1.0 1.0 1.0 Exp. 21 1.0 1.0 1.0 1.0 0.7 0.7 0.7 1.0 1.0 1.0 0.9 0.8 0.9 0.9 0.9 0.9 1.0 1.0 1.0 1.0 1.0 Exp. 22 1.0 1.0 1.0 1.0 0.6 0.7 0.7 1.0 1.0 1.0 0.9 0.8 0.9 0.9 0.9 0.9 1.0 1.0 1.0 1.0 In addition, a Cronbach's alpha was performed giving the 0.9924, confirming the obtained results are consistent and confidence of the PFDA-FMEA method.…”

Section: Resultsmentioning

confidence: 99%

“…Product Design is a complex process which have multiple different problems to solve during the different design phases [21], the risk analysis is essential during all the PD process, [22] and beside the multiple problems to solve, we also have some gaps in the tools that we use to solve those problems, that is the case of the FMEA tool. There are cracks identified over the FMEA assessment [23].…”

Section: Introductionmentioning

confidence: 99%

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PFDA- FMEA. An Integrated Method Improving FMEA Assessment in Product Design

Garcia¹,

Pérez-Domínguez²,

Luviano-Cruz³

et al. 2020

Preprint

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Product Design is getting nowadays new challenges on developing new products, since the industry is in a rush to introduce products into the marketplace, where customers demand products faster, cheaper, and free of failures. In the meantime, global companies are always trying to improve their Product Design process to get advantages over their competitors using proven tools like FEMA and mixing methodologies like Fuzzy theories with FMEA. Even today using all this tools and combination of methodologies there is a gap to address and it is required a robust risk analysis solving current issues in the electronic industry. This document aims to reveal a novel integrated method, where Failure Mode and Effect Analysis, Pythagorean Fuzzy Sets and Dimensional Analysis are cohesive into a model that minimize the uncertainty of the ranking and prioritization over the Failure Mode and Effect Analysis execution, helping to identify risks within an accurate grade over possible failures in the Product Design process. A real practical example is used to show the proposed method, where it is identified a robust methodology integration and solid and results using a sensitivity analysis.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PFDA- FMEA. An Integrated Method Improving FMEA Assessment in Product Design

Garcia¹,

Pérez-Domínguez²,

Luviano-Cruz³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Product Design is a complex process that has multiple different problems to solve during different design phases [48]. Risk analysis is essential during the process, as mentioned in [49][50][51][52][53][54].…”

Section: Risk Assessment In Pd a Literature Reviewmentioning

confidence: 99%

PFDA-FMEA, an Integrated Method Improving FMEA Assessment in Product Design

et al. 2021

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Product Design (PD) currently faces challenges in new product development, since the industry is in a rush to introduce new products into the market, with customers demanding products that are faster, cheaper, and free from failure. In addition, global companies are trying to improve their product design risk assessment process to gain advantages over competitors, using proven tools like Failure Mode and Effect Analysis (FMEA) and mixing risk assessment methods. However, with current risks assessment tools and a combination of other methods, there is the opportunity to improve risk analysis. This document aims to reveal a novel integrated method, where FMEA, Pythagorean Fuzzy Sets (PFS), and Dimensional Analysis (DA) are cohesive in one model. The proposed method provides an effective technique to identify risks and remove uncertainty and vagueness of human intervention during risk assessment using the Failure Mode and Effect Analysis method. A real-life problem was carried out to illustrate the proposed method. Finally, the study was substantiated by using a correlation and sensitivity analysis, demonstrating the presented integrated method’s usefulness in decision-making and problem-solving.

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“…This physical representation is used as a restriction check for the overall size of the machine and the installation space of the components. For a more detailed description refer to (Wolniak et al, 2020b;Wolniak et al, 2020a). The robustness region in the objective function space is defined by ± 1 liter for the water consumption and ± 15 minutes for the operating time.…”

Section: Case Studymentioning

confidence: 99%

“…Secondly, we consider the influence of uncertainties regarding the user-scenarios by adding a Gaussian distribution to simulate a user deviation. As a case study we use the multi-objective optimization of a coffee-machine as presented in (Wolniak et al, 2020b). The basis for the optimization is a user-scenario given by a customer with timely defined demands of coffee.…”

Section: Introductionmentioning

confidence: 99%

Robust Multi-Objective Optimization in Product Development With Respect to User-Scenario and Manufacturing Uncertainties

2021

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In product development, user-scenarios are a way of tailoring requirements to defined customer groups. Furthermore, a product design often involves multiple conflicting objectives that are analyzed within an iterative process. The models typically used for the analysis often do not accurately reflect the real-world representation. This can be alleviated by finding robust product designs. While usually uncertainties due to manufacturing tolerances are investigated, we additionally consider uncertainties in the user-scenario. Therefore, we present a robustness evaluation in a multi-objective numerical optimization in product development. For this, we consider manufacturing tolerances using an adjusted Latin Hypercube Sampling as well as deviations in the user-scenario by means of a Gaussian distribution. In the case study, we present the robust development of a customer specific coffee machine, where we show the robustness evaluation and the impact of the proposed adjustments. The advantage of the presented process is a product design tailored to the customer's requirements under specified uncertainties. In addition, this enables a time benefit in the product development due to the automated analysis used in the optimization.

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Automated Product Functionality and Design Optimization Instancing a Product-Service System

Cited by 4 publications

References 14 publications

PFDA- FMEA. An Integrated Method Improving FMEA Assessment in Product Design

PFDA- FMEA. An Integrated Method Improving FMEA Assessment in Product Design

PFDA-FMEA, an Integrated Method Improving FMEA Assessment in Product Design

Robust Multi-Objective Optimization in Product Development With Respect to User-Scenario and Manufacturing Uncertainties

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