Recipe-based Integrated Semantic Product, Process, Resource (PPR) Digital Modelling Methodology

Agyapong-Kodua, Kwabena; Haraszkó, Csaba; Németh, István

doi:10.1016/j.procir.2014.03.118

Cited by 16 publications

(5 citation statements)

References 15 publications

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“…To program welding robots, information is required concerning the product design, the process and the resources needed to perform the weldments [9]. The systematic overview on product-process-resource information is also known as the "PPR perspective" and it is formalised in existing literature through PPR models [10][11][12]. PPR models include in their scope details about the resource capability, or skill required to conduct the manufacturing processes necessary to product an item.…”

Section: Ppr Modelsmentioning

confidence: 99%

Automatic Welding-Robot Programming Based on PPR Models

Sarivan,

Madsen,

Wæhrens

2023

Preprint

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This paper describes a novel end-to-end approach for automatic welding-robot programming based on a product-process-resource (PPR) model, for one-of-a-kind manufacturing systems. For welding-robot programming to be possible, in a one-of-a-kind manufacturing system, a large range of information about the product is required e.g., customer requirements, product design features, manufacturing process parameters and resource capability. Traditionally, this information is processed and transferred along the manufacturing organisation’s value chain by using several stand-alone digital systems which require extensive human input and high skill to operate. A PPR model is proposed through this research as a platform for storing and processing the necessary information along the value chain seamlessly. This is achieved through the digital integration of CAD software tools compliant with the ISO 2553:2019 standard, and offline programming systems (OLP) for welding-robots. The PPR model enables the automatic programming of welding robots and drastically reduces human input. The applicability in manufacturing of the theoretical concept is demonstrated through technical implementations tested in laboratory and on the value chain of an engineering-to-order (ETO) industrial partner involved in the metal fabrication industry. The experiments were conducted by creating several products using the proposed artefact. Experiments show that automatic programming of welding robots can be achieved using PPR models, drastically reduce the human input necessary to obtain a welding-robot program, and drastically reduce the ETO product’s lead time.

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Section: Ppr Modelsmentioning

confidence: 99%

Automatic Welding-Robot Programming Based on PPR Models

Sarivan,

Madsen,

Wæhrens

2023

Preprint

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“…Nevertheless, the added functionalities and availability of information in P&P systems do offer new opportunities to address the ramp-up process. In fact, this links to well-established concepts from multiple advances in P&P, which provide so-called Skills, Skill Requirements and Recipes [22], [23]. Simply put, these concepts are used to describe an assembly process with an underlying relationship of the Product, Process and Resource (PPR).…”

Section: Problem Definitionmentioning

confidence: 99%

Towards a Decision-support Framework for Reducing Ramp-up Effort in Plug-and-Produce Systems

Zimmer

Ferreira

Danny

et al. 2019

2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS)

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Nowadays, shorter and more flexible production cycles are vital to meet the increasing customised product demand. As any delays and downtimes in the production towards time-to-market means a substantial financial loss, manufacturers take an interest in getting the production system to full utilisation as quickly as possible. The concept of plug-andproduce manufacturing systems facilitates an easy integration process through embedded intelligence in the devices. However, a human still needs to validate the functionality of the system and more importantly must ensure that the required quality and performance is delivered. This is done during the ramp-up phase, where the system is assembled and tested first-time. System adaptations and a lack of standard procedures make the ramp-up process still largely dependent on the operator's experience level. A major problem that currently occurs during ramp-up, is a loss of knowledge and information due to a lack of means to capture the human's experience. Acquiring this information can be used to simplify future ramp-up cases as additional insights about change actions and their effect on the system could be revealed. Hence, this paper proposes a decisionsupport framework for plug-and-produce assembly systems that will help to reduce the ramp-up effort and ultimately shorten ramp-up time. As an illustrative example, a glueing station developed as part of the European project openMOS is considered.

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“…A recipe-based semantic approach to assembly has been presented in [7] as a methodology for product-resource requirement integration, without taking the concept of a recipe explicitly into account. International standardization for process control ANS/ISA-88-2010 [8] defines a procedural control models for batch process control.…”

Section: Related Workmentioning

confidence: 99%

Recipe-Based Engineering and Operator Support for Flexible Configuration of High-Mix Assembly

Verhoosel¹,

Bekkum²

2017

Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing

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Nowadays, manufacturers must be increasingly flexible to quickly produce a high mix of on-demand, customer-specific, low volume product types. This requires flexible assembly lines with operators that are wellsupported in their constantly changing assembly task, while producing highquality, first-time-right, zero-defect products. Information coming from various supporting systems, such as ERP, MES and operator support systems, needs to be combined by the operator that configures the assembly line with materials, instructions and machine initialization settings. In this paper, we present a knowledge model that captures the main concepts and their relations in flexible manufacturing to deal with these challenges. This model is constructed by integrating existing manufacturing ontologies and can be used as the basis for information collection, exchange and analysis in information systems used in flexible manufacturing. The model supports (1) easy definition of recipe-based manufacturing instructions for engineers and operators, and (2) flexible, modular and adaptive support for human/cobot instructions. We also describe a demonstration setup with an existing operator support system (OPS) in which the recipe concept is used in the engineering process to easily reuse existing modular components for assembling different product types.

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Recipe-based Integrated Semantic Product, Process, Resource (PPR) Digital Modelling Methodology

Cited by 16 publications

References 15 publications

Automatic Welding-Robot Programming Based on PPR Models

Automatic Welding-Robot Programming Based on PPR Models

Towards a Decision-support Framework for Reducing Ramp-up Effort in Plug-and-Produce Systems

Recipe-Based Engineering and Operator Support for Flexible Configuration of High-Mix Assembly

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