A Portrait of an ISO STEP Tolerancing Standard as an Enabler of Smart Manufacturing Systems

2015 ◽  
Vol 15 (2) ◽  
Author(s):  
Allison Barnard Feeney ◽  
Simon P. Frechette ◽  
Vijay Srinivasan
Keyword(s):  
Manufacturing Systems ◽  
Information Model ◽  
International Organization ◽  
Smart Manufacturing ◽  
Model Data ◽  
Product Model ◽  
Information Models ◽  
Flow Through ◽  
Iso 10303 ◽  
Smart Manufacturing Systems

The International Organization for Standardization (ISO) has just completed a major effort on a new standard ISO 10303-242 titled “Managed Model Based 3D Engineering.” It belongs to a family of standards called STEP (STandard for the Exchange of Product model data). ISO 10303-242 is also called the STEP Application Protocol 242 (STEP AP 242, for short). The intent of STEP AP 242 is to support a manufacturing enterprise with a range of standardized information models that flow through a long and wide “digital thread” that makes the manufacturing systems in the enterprise smart. One such standardized information model is that of tolerances specified on a product’s geometry so that the product can be manufactured according to the specifications. This paper describes the attributes of smart manufacturing systems, the capabilities of STEP AP 242 in handling tolerance information associated with product geometry, and how these capabilities enable the manufacturing systems to be smart.

Semantic Interoperability of GD&T Data Through ISO 10303 Step AP242

2016 ◽  
Author(s):  
Adarsh Venkiteswaran ◽  
Sayed Mohammad Hejazi ◽  
Deepanjan Biswas ◽  
Jami J. Shah ◽  
Joseph K. Davidson
Keyword(s):  
Data Exchange ◽  
Smart Manufacturing ◽  
Context Model ◽  
Product Model ◽  
Shape Information ◽  
Computer Aided Process Planning ◽  
Model Based ◽  
Computer Aided ◽  
Iso 10303 ◽  
Manufacturing Applications

Industries are continuously trying to improve the time to market through automation and optimization of existing product development processes. Large companies vow to save significant time and resources through seamless communication of data between design, manufacturing, supply chain and quality assurance teams. In this context, Model Based Definition/Engineering (MBD) / (MBE) has gained popularity, particularly in its effort to replace traditional engineering drawings and documentations with a unified digital product model in a multi-disciplinary environment. Widely used 3D data exchange models (STEP AP 203, 214) contains mere shape information, which does not provide much value for reuse in downstream manufacturing applications. However, the latest STEP AP 242 (ISO 10303-242) “Managed model based 3D engineering” aims to support smart manufacturing by capturing semantic Product Manufacturing Information (PMI) within the 3D model and also helping with long-term archival. As a primary, for interoperability of Geometric Dimensions & Tolerances (GD&T) through AP 242, CAx Implementor Forum has published a set of recommended practices for the implementation of a translator. In line with these recommendations, this paper discusses the implementation of an AP 203 to AP 242 translator by attaching semantic GD&T available in an in-house Constraint Tolerance Graph (CTF) file. Further, semantic GD&T data can be automatically consumed by downstream applications such as Computer Aided Process Planning (CAPP), Computer Aided Inspection (CAI), Computer Aided Tolerance Systems (CATS) and Coordinate Measuring Machines (CMM). Also, this paper will briefly touch base on the important elements that will constitute a comprehensive product data model for model-based interoperability.

scholarly journals A Model for Working Environment Monitoring in Smart Manufacturing

2021 ◽  
Vol 11 (6) ◽  
pp. 2850
Author(s):  
Dalibor Dobrilovic ◽  
Vladimir Brtka ◽  
Zeljko Stojanov ◽  
Gordana Jotanovic ◽  
Dragan Perakovic ◽  
...  
Keyword(s):  
Open Source ◽  
Student Teaching ◽  
Manufacturing Systems ◽  
Working Environment ◽  
Smart Manufacturing ◽  
Environment Monitoring ◽  
Application Development ◽  
Testing Platform ◽  
Proposed Model ◽  
Smart Manufacturing Systems

The growing application of smart manufacturing systems and the expansion of the Industry 4.0 model have created a need for new teaching platforms for education, rapid application development, and testing. This research addresses this need with a proposal for a model of working environment monitoring in smart manufacturing, based on emerging wireless sensor technologies and the message queuing telemetry transport (MQTT) protocol. In accordance with the proposed model, a testing platform was developed. The testing platform was built on open-source hardware and software components. The testing platform was used for the validation of the model within the presented experimental environment. The results showed that the proposed model could be developed by mainly using open-source components, which can then be used to simulate different scenarios, applications, and target systems. Furthermore, the presented stable and functional platform proved to be applicable in the process of rapid prototyping, and software development for the targeted systems, as well as for student teaching as part of the engineering education process.

Are smart manufacturing systems beneficial for all SMEs? Evidence from Korea

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Soojeen Jang ◽  
Yanghon Chung ◽  
Hosung Son
Keyword(s):  
Human Resources ◽  
Financial Performance ◽  
Manufacturing Systems ◽  
Contingency Theory ◽  
Ordinary Least Squares ◽  
Operational Efficiency ◽  
Smart Manufacturing ◽  
Least Squares Regression ◽  
Content Type ◽  
Smart Manufacturing Systems

PurposeThrough the resource-based view (RBV) and contingency theory, this study empirically investigates the impacts of smart manufacturing systems' maturity levels on the performance of small and medium-sized enterprises (SMEs). Moreover, it aims to examine how industry types (i.e. high- and low-tech industries) and human-resource factors (i.e. the proportion of production workers to total workers) as contingency factors influence the effects of smart manufacturing systems.Design/methodology/approachThe study conducted an empirical investigation of a sample of 163 Korean manufacturing SMEs. This study used an ordinary least squares regression to examine the impacts of the maturity levels of smart manufacturing systems on financial performance. Moreover, the impacts on operational efficiency were analysed using data envelopment analysis based on bootstrap methods and Tobit regression.FindingsThe RBV results indicate that the higher the maturity levels of smart manufacturing systems, the higher the financial performance and operational efficiency. Moreover, based on contingency theory, this study reveals that the effect of the maturity levels of smart manufacturing systems on financial performance and operational efficiency depends on firms' industry types and the proportion of production workers.Research limitations/implicationsThis study shows that the introduction of smart manufacturing systems can help SMEs achieve better financial performance and operational efficiency. However, their effectiveness is contingent on firms' industry types and the characteristics of their human resources.Practical implicationsSince the effects of the maturity levels of smart manufacturing systems on SME performance differ depending on their industries and the characteristics of human resources, managers need to consider them when introducing or investing in smart manufacturing systems.Originality/valueBased on the RBV and contingency theory, this is the first empirical study to examine the moderating effects of industry types and the proportion of production workers on the impacts of the maturity levels of smart manufacturing systems on the financial performance and operational efficiency of SMEs.

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