How can Computer Aided Tolerancing Support Closed Loop Tolerance Engineering?

Schleich, Benjamin; Wartzack, Sandro

doi:10.1016/j.procir.2014.03.129

Cited by 9 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, the concept of Skin Model, and its operationalization through Skin Model Shapes, has been proposed as representation of non-ideal surfaces [5] and digital twin of manufactured parts [6]. Skin Model Shapes have applications in product development, manufacturing, and inspection [7] and in linking the associated tolerancerelated interactions among these applications [8]. A single digital representation of a product can be used to update assumed geometric characteristics and perform tolerance analysis and finite element analysis [9].…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection in Skin Model Shapes using machine learning classifiers

Yacob

Semere

Nordgren

2019

Int J Adv Manuf Technol

View full text Add to dashboard Cite

The concept of Skin Model Shapes has been proposed as a method to generate digital twins of manufactured parts and is a new paradigm in the design and manufacturing industry. Skin Model Shapes use discrete surface representation schemes, such as meshes and point clouds, to represent surfaces, which makes them enablers to perform an accurate tolerance analysis and surface inspection. However, online inspection of manufactured parts through use of Skin Model Shapes has not been extensively studied. Moreover, the existing geometric variation inspection techniques do not detect unfamiliar changes within tolerance, which could be the precursors to the onset of the manufacturing of out of tolerance part. To detect the unfamiliar changes, as anomalies, and categorize them as systematic and random variations, some unique surface characteristics can be extracted and studied. Random surface deviations exhibit narrow normal distributions, and systematic deviations, on the other hand, exhibit wide, skewed, and multimodal distributions. Using those surface characteristics as key traits, machine learning classifiers can be used to classify deviations into systematic and random variations. To illustrate the method, multiple samples from a truck component manufacturing line were scanned and the collected 3D point cloud data was used to extract features. A prediction score of 97-100% can be achieved by decision tree, k-nearest neighbor, support vector machines, and ensemble classifiers. The purposed approach is expected to extend the existing online inspection approaches and applications of Skin Model Shapes in quality control.

show abstract

Section: Introductionmentioning

confidence: 99%

Anomaly detection in Skin Model Shapes using machine learning classifiers

Yacob

Semere

Nordgren

2019

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…A special challenge in this context is the tolerance allocation of components, because component deviations also influence not only the geometry but also the properties of a product. Tolerance management has been established to ensure that all the unique products produced meet the required set properties from a geometric and functional point of view (Schleich and Wartzack, 2014b). When awarding component tolerances, the tolerance engineer is usually in the area of tension between production and quality assurance.…”

Section: Introductionmentioning

confidence: 99%

A Concept for Process-Oriented Interdisciplinary Tolerance Management Considering Production-Specific Deviations

Heling¹,

Oberleiter²,

Rohrmoser³

et al. 2019

Proc. Int. Conf. Eng. Des.

Self Cite

View full text Add to dashboard Cite

To meet rising customer requirements, increasingly complex products have to be virtually validated. To achieve this within the framework of virtual product development, a wide range of aspects has to be taken into account. In this context, tolerance analysis has established itself as a proven tool to evaluate the consequences of geometric part deviations on geometric product characteristics. Existing approaches, however, do not sufficiently take into account production-specific deviations, leading to time-consuming iterations during the product development process. Therefore, the focus of this contribution is on process-oriented interdisciplinary tolerance management that allows the integration of manufacturing simulations into the tolerance analysis. In contrast to the conventional approach, this novel methodology allows to avoid unnecessary iterations in the context of product development and validation. Following the presentation of the novel procedure, the application on a case study of an X- ray shutter is carried out, whereby surrogate models are integrated in order to reduce the computing time.

show abstract

“…Geometrical variation is present in any production process and needs to be controlled, to ensure compliance with design requirements and minimize costs. There is a consensus in the scientific community about the importance of managing geometrical variations since the design phase [1][2][3]. Tolerancing plays a key role in this regard, by establishing maximum permissible limits of variation and influencing all product development processes.…”

Section: Introductionmentioning

confidence: 99%

Historical measurement data reuse and similarity analysis for dimensional production tolerancing of injected plastic parts

Oliveira

Donatelli

2017

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

Geometrical variation is present in any production process and needs to be controlled, to ensure compliance with design requirements and minimize costs. Tolerancing of injected plastic parts (IPPs) is mostly performed in practice without using consistent criteria, many times resulting in tolerances which are unnecessarily narrow and incompatible with the respective manufacturing process capabilities. In this paper, a new method is proposed to support IPPs dimensional production tolerancing in early design phases, based on historical measurement data reuse and similarity analysis. Rule-based reasoning has been employed together with a scoring system to identify reliable and similar cases stored in a database, involving attributes of both the parts and the dimensional characteristics. Two different approaches for data recovery were considered, providing the ability to choose between a simplified and a more accurate analysis. The method feasibility is demonstrated by a case study involving 20 different IPPs and 24 dimensional characteristics from three suppliers. The proposed method outperformed the use of standard DIN 16742 to estimate production tolerances. Comparisons between the estimated production tolerances and the respective process capabilities resulted in maximum mean absolute deviation of 10% and Pearson's r of about 0.6.

show abstract

How can Computer Aided Tolerancing Support Closed Loop Tolerance Engineering?

Cited by 9 publications

References 23 publications

Anomaly detection in Skin Model Shapes using machine learning classifiers

Anomaly detection in Skin Model Shapes using machine learning classifiers

A Concept for Process-Oriented Interdisciplinary Tolerance Management Considering Production-Specific Deviations

Historical measurement data reuse and similarity analysis for dimensional production tolerancing of injected plastic parts

Contact Info

Product

Resources

About