A framework concept for data visualization and structuring in a complex production process

This paper provides a concept study for a visual interface framework together with the software Sequence Planner for implementation on a complex industrial process for extracting process information in an efficient way and how to make use of a lot of data to visualize it in a standardized human machine interface for different user perspectives. The concept is tested and validated on a smaller simulation of a paint booth with several interconnected and supporting control systems to prove the functionality and usefulness in this kind of production system.The paper presents the resulting five abstraction levels in the framework concept, from a production top view down to the signal exchange between the different resources in one production cell, together with additional features. The simulation proves the setup with Sequence Planner and the visual interface to work by extract and present process data from a running sequence

SelfPaint-A self-programming paint booth

In this paper we present a unique Fraunhofer approach that aims towards a vision to automate the product preparation in paint shops by automatically generating robot paths and process conditions that guarantee a certain wanted paint coverage. This will be accomplished through a combination of state-of-the-art simulation technology, inline quality control by novel terahertz thickness measurement technology, and surface treatment technology. The benefits of the approach are a shortened product preparation time, increased quality and reduced material and energy consumption. The painting of a tractor fender is used to demonstrate the approach

Performance measurement on automotive assembly line

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Automação). Universidade do Porto. Faculdade de Engenharia. 201

Komponenttien luokittelu ja parhaat käytännöt tuotantosimulaation mallinnuksessa

Production simulation software plays a major role in validation, optimization and illustration of production systems. Operation of production simulation is generally based on components and their interaction. Components typically represent factory floor devices, but in addition, there can be components to provide visualization, statistics, control or other input to simulation. The demand for having high-quality, easy-to-use and compatible components emphasizes the importance of component modelling. The objectives of this thesis were to develop component classes based on industrial devices, to standardize component modelling solutions and best practices in component modelling. Other objectives were to identify and analyse future prospects of production simulation. This focuses on the concept of digital twin, which could be described as reflective real-time simulation model from the physical system. In addition, focus is also set on formal modelling languages. The outcome of this thesis presents component classes and best practices in component modelling. In component classification, the focus was set to development of generic components, which can be controlled with signal-based logic. This enables components from the software to be externally controlled. In addition, automatic model creation tool wizard, is implemented to instantly generate components based on the defined component classes. Best practices were based on the selected modelling fields that are most relevant for general use. In the development of best practices, interviewing method was utilized to receive input from simulation experts.Tuotantosimulaatio on tärkeässä osassa tuotantojärjestelmien validoinnissa, optimoinnissa ja visualisoinnissa. Tuotantosimulaation toiminta perustuu yleisesti komponentteihin ja niiden väliseen vuorovaikutukseen. Komponentit esittävät tyypillisesti tehtaasta löytyviä laitteita ja esineitä, mutta komponentteja voidaan käyttää myös visualisointiin, statistiikan keräämiseen, järjestelmän ohjaukseen tai muuhun tarpeeseen simuloinnissa. Tämän diplomityön tavoitteita oli kehittää komponenttiluokkia teollisuudesta valittujen laitteiden perusteella, mikä mahdollistaa mallinnusratkaisujen standardoinnin. Sen lisäksi tavoitteena oli kehittää parhaat käytännöt komponenttimallinnukseen. Muita tavoitteita oli tunnistaa ja analysoida tulevaisuuden näkymiä tuotantosimulaatiolle. Tämä keskittyi pääosin digitaaliseen kaksoseen, jota voidaan kuvata reaaliaikaisesti peilautuvaksi simulaatiomalliksi todellisesta järjestelmästä. Tämän lisäksi työssä keskityttiin formaaleihin mallinnuskieliin. Diplomityön lopputulos esittää kehitetyt komponenttiluokat ja parhaat käytännöt komponenttimallinnuksessa. Komponenttien luokittelussa keskityttiin kehittämään geneerisiä komponentteja, joita voidaan ohjata signaalipohjaisilla komennoilla. Tämä mahdollistaa komponentin ohjaamisen myös simulointiohjelman ulkopuolelta. Tämän lisäksi automaattista komponenttien luomistyökalua käytettiin luokiteltujen komponenttien luomisessa. Parhaat käytännöt komponenttimallinnuksessa pohjautuivat mallinnuksen oleellisimpiin osa-alueisiin tavanomaisissa mallinnustilanteissa. Parhaiden käytäntöjen kehityksessä haastateltiin simulointiammattilaisia, joiden mielipiteistä muodostettiin perusta käytäntöjen kehitykselle

Performance measurement on automotive assembly line.

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Komponenttien luokittelu ja parhaat käytännöt tuotantosimulaation mallinnuksessa

Production simulation software plays a major role in validation, optimization and illustration of production systems. Operation of production simulation is generally based on components and their interaction. Components typically represent factory floor devices, but in addition, there can be components to provide visualization, statistics, control or other input to simulation. The demand for having high-quality, easy-to-use and compatible components emphasizes the importance of component modelling. The objectives of this thesis were to develop component classes based on industrial devices, to standardize component modelling solutions and best practices in component modelling. Other objectives were to identify and analyse future prospects of production simulation. This focuses on the concept of digital twin, which could be described as reflective real-time simulation model from the physical system. In addition, focus is also set on formal modelling languages. The outcome of this thesis presents component classes and best practices in component modelling. In component classification, the focus was set to development of generic components, which can be controlled with signal-based logic. This enables components from the software to be externally controlled. In addition, automatic model creation tool wizard, is implemented to instantly generate components based on the defined component classes. Best practices were based on the selected modelling fields that are most relevant for general use. In the development of best practices, interviewing method was utilized to receive input from simulation experts.Tuotantosimulaatio on tärkeässä osassa tuotantojärjestelmien validoinnissa, optimoinnissa ja visualisoinnissa. Tuotantosimulaation toiminta perustuu yleisesti komponentteihin ja niiden väliseen vuorovaikutukseen. Komponentit esittävät tyypillisesti tehtaasta löytyviä laitteita ja esineitä, mutta komponentteja voidaan käyttää myös visualisointiin, statistiikan keräämiseen, järjestelmän ohjaukseen tai muuhun tarpeeseen simuloinnissa. Tämän diplomityön tavoitteita oli kehittää komponenttiluokkia teollisuudesta valittujen laitteiden perusteella, mikä mahdollistaa mallinnusratkaisujen standardoinnin. Sen lisäksi tavoitteena oli kehittää parhaat käytännöt komponenttimallinnukseen. Muita tavoitteita oli tunnistaa ja analysoida tulevaisuuden näkymiä tuotantosimulaatiolle. Tämä keskittyi pääosin digitaaliseen kaksoseen, jota voidaan kuvata reaaliaikaisesti peilautuvaksi simulaatiomalliksi todellisesta järjestelmästä. Tämän lisäksi työssä keskityttiin formaaleihin mallinnuskieliin. Diplomityön lopputulos esittää kehitetyt komponenttiluokat ja parhaat käytännöt komponenttimallinnuksessa. Komponenttien luokittelussa keskityttiin kehittämään geneerisiä komponentteja, joita voidaan ohjata signaalipohjaisilla komennoilla. Tämä mahdollistaa komponentin ohjaamisen myös simulointiohjelman ulkopuolelta. Tämän lisäksi automaattista komponenttien luomistyökalua käytettiin luokiteltujen komponenttien luomisessa. Parhaat käytännöt komponenttimallinnuksessa pohjautuivat mallinnuksen oleellisimpiin osa-alueisiin tavanomaisissa mallinnustilanteissa. Parhaiden käytäntöjen kehityksessä haastateltiin simulointiammattilaisia, joiden mielipiteistä muodostettiin perusta käytäntöjen kehitykselle

AI for in-line vehicle sequence controlling: development and evaluation of an adaptive machine learning artifact to predict sequence deviations in a mixed-model production line

Customers in the manufacturing sector, especially in the automotive industry, have a high demand for individualized products at price levels comparable to traditional mass production. The contrary objectives of providing a variety of products and operating at minimum costs have introduced a high degree of production planning and control mechanisms based on a stable order sequence for mixed-model assembly lines. A major threat to this development is sequence scrambling, triggered by both operational and product-related root causes. Despite the introduction of just-in-time and fixed production times, the problem of sequence scrambling remains partially unresolved in the automotive industry. Negative downstream effects range from disruptions in the just-in-sequence supply chain to a stop of the production process. A precise prediction of sequence deviations at an early stage allows the introduction of counteractions to stabilize the sequence before disorder emerges. While procedural causes are widely addressed in research, the work at hand requires a different perspective involving a product-related view. Built on unique data from a real-world global automotive manufacturer, a supervised classification model is trained and evaluated. This includes all the necessary steps to design, implement, and assess an AI artifact, as well as data gathering, preprocessing, algorithm selection, and evaluation. To ensure long-term prediction stability, we include a continuous learning module to counter data drifts. We show that up to 50% of the major deviations can be predicted in advance. However, we do not consider any process-related information, such as machine conditions and shift plans, but solely focus on the exploitation of product features like body type, powertrain, color, and special equipment

Real-Time Analysis and Control of Serial Production Lines for Energy Efficient Manufacturing

Productivity analysis, operation control and energy consumption reduction have been the central topics in manufacturing research and practice. They are closely related to each other. Control of production operations is considered as one of the most economical methods to improve energy efficiency in manufacturing systems, while system performance analysis serves as the base of production control. On the other hand, effective operation control can result to energy efficiency in manufacturing. Steady state analysis has been investigated extensively; however, transient analysis remained largely unexplored. Our research focuses on system modeling, performance analysis, and real-time operation control of serial production lines with unreliable machines and finite buffers, especially in transient period, with Bernoulli or geometric reliability. Analytical results, practical case studies and applications for energy efficient manufacturing are provided. A simulation using ARENA software to reproduce and analyze brewery production line is performed

Towards a holistic methodology of efficient virtual preparation and commissioning for production systems

The industry elaborates on the possibilities of applying virtual engineering work to excel in production system development. For example, Virtual Commissioning as a concept for testing and validating system performance in advance of on-site commissioning has proven beneficial in multiple areas of development. Some areas include reducing on-site commissioning time, guaranteeing functional behavior, and removing potential errors, resulting in a smoother integration of new and upgraded systems.Nevertheless, it has been hard to prove the financial benefits and actual gain from VC compared to the more trusted traditional methods. The lack of standards mixed with the increasing complexity of systems and experience from prior attempts is one of many reasons.This thesis has identified different vital areas crucial for adopting virtual elements into the value chain of the development process within the automotive industry. It is of the highest importance to understand the prerequisites of a project’s ability to integrate virtual preparation for efficient commissioning and further break down the technical requirements of modeling and simulation in a multidisciplinary digital architecture.With more quantified data and insight from Virtual Commissioning attempts, it is possible to adopt knowledge to future projects and find ways to increase the utilization of the invested virtual engineering work.The thesis investigates the challenges of implementing virtual preparational methods for efficient commissioning to achieve flawless launches for all implementation projects of production systems. In addition, the research aims to find ways to increase the utilization of the constructed models, decrease the cost of virtual development and testing, and verify functionality and accuracy for optimal levels of simulation

The Car Resequencing Problem with Pull-Off Tables

The car sequencing problem determines sequences of different car models launched down a mixed-model assembly line. To avoid work overloads of workforce, car sequencing restricts the maximum occurrence of labor-intensive options, e.g., a sunroof, by applying sequencing rules. We consider this problem in a resequencing context, where a given number of buffers (denoted as pull-off tables) is available for rearranging a stirred sequence. The problem is formalized and suited solution procedures are developed. A lower bound and a dominance rule are introduced which both reduce the running time of our graph approach. Finally, a real-world resequencing setting is investigated.mixed-model assembly line, car sequencing, resequencing