Version control system of CAD documents and PLC projects

Khudyakov, P. Yu.; Kisel'Nikov, A. Yu.; Startcev, I. M.; Kovalev, Alexey Anatolyevich

doi:10.1088/1742-6596/1015/4/042020

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…In this sense, there are VCS proposals for specific data with semantics different from textual information. Therefore, there are systems adapted to topics such as Model-Driven Software Development [23], 3D modeling [24], CAD modeling [25], Building Information Modeling [26] or image editing [27]. As for point clouds, there are no specific systems that allow the storing of intermediate versions efficiently, so the only alternative is to use systems such as Git, which store the data completely in each version.…”

Section: Time Main Branchmentioning

confidence: 99%

A Version Control System for Point Clouds

Ogayar-Anguita,

López-Ruiz,

Segura-Sánchez

et al. 2023

Remote Sensing

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This paper presents a novel version control system for point clouds, which allows the complete editing history of a dataset to be stored. For each intermediate version, this system stores only the information that changes with respect to the previous one, which is compressed using a new strategy based on several algorithms. It allows undo/redo functionality in memory, which serves to optimize the operation of the version control system. It can also manage changes produced from third-party applications, which makes it ideal to be integrated into typical Computer-Aided Design workflows. In addition to automated management of incremental versions of point cloud datasets, the proposed system has a much lower storage footprint than the manual backup approach for most common point cloud workflows, which is essential when working with LiDAR (Light Detection and Ranging) data in the context of spatial big data.

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Section: Time Main Branchmentioning

confidence: 99%

A Version Control System for Point Clouds

Ogayar-Anguita,

López-Ruiz,

Segura-Sánchez

et al. 2023

Remote Sensing

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“…This is completely independent of adding any additional, untested logic. It seems not to be uncommon for a service technician to alter the machine code slightly to make up for some local problem and not always return the changes to the company's server [14]. Another source of diverging code bases is working with an internal code platform and adapting the resulting code to each customer.…”

Section: Integration In Field Busmentioning

confidence: 99%

“…Source code versioning systems common in the software development are only slowly evolving in current PLC programming environments. Especially for graphic programming from the IEC 61131 PLC languages version control with concurrent versions and merge strategies is a complex task [14]. Thus, the usage of those techniques seems to be even more limited in a practical approach.…”

Section: Integration In Field Busmentioning

confidence: 99%

Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

2021

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Much research is done on data analytics and machine learning for data coming from industrial processes. In practical approaches, one finds many pitfalls restraining the application of these modern technologies especially in brownfield applications. With this paper, we want to show state of the art and what to expect when working with stock machines in the field. The paper is a review of literature found to cover challenges for cyber-physical production systems (CPPS) in brownfield applications. This review is combined with our own personal experience and findings gained while setting up such systems in processing and packaging machines as well as in other areas. A major focus in this paper is on data collection, which tends be more cumbersome than most people might expect. In addition, data quality for machine learning applications is a challenge once leaving the laboratory and its academic data sets. Topics here include missing ground truth or the lack of semantic description of the data. A last challenge covered is IT security and passing data through firewalls to allow for the cyber part in CPPS. However, all of these findings show that potentials of data driven production systems are strongly depending on data collection to build proclaimed new automation systems with more flexibility, improved human–machine interaction and better process-stability and thus less waste during manufacturing.

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