Soft Computing Decision Support for a Steel Sheet Incremental Cold Shaping Process

It is known that the complexity inherited in most of the new real world problems, for example, the cold rolled steel industrial process, increases as the computer capacity does. Higher performance requirements with a lower amount of data samples are needed due to the costs of generating new instances, specially in those processes where new technologies arise. This study is focused on the analysis and design of a novel decision support system for an incremental steel cold shaping process, where there is a lack of knowledge of which operating conditions are suitable for obtaining high quality results. The most suitable features have been found using a wrapper feature selection method, in which genetic algorithms and neural networks are hybridized. Some facts concerning the enhanced experimentation needed and the improvements in the algorithm are drawn

Meta-heuristic improvements applied for steel sheet incremental cold shaping

In previous studies, a wrapper feature selection method for decision support in steel sheet incremental cold shaping process (SSICS) was proposed. The problem included both regression and classification, while the learned models were neural networks and support vector machines, respectively. SSICS is the type of problem for which the number of features is similar to the number of instances in the data set, this represents many of real world decision support problems found in the industry. This study focuses on several questions and improvements that were left open, suggesting proposals for each of them. More specifically, this study evaluates the relevance of the different cross validation methods in the learned models, but also proposes several improvements such as allowing the number of chosen features as well as some of the parameters of the neural networks to evolve, accordingly. Well-known data sets have been use in this experimentation and an in-depth analysis of the experiment results is included. 5 × 2 CV has been found the more interesting cross validation method for this kind of problems. In addition, the adaptation of the number of features and, consequently, the model parameters really improves the performance of the approach. The different enhancements have been applied to the real world problem, an several conclusions have been drawn from the results obtained

Tekes projekti SuperMachines loppuraportti

Tutkimuksessa kerättiin best practice aineistoa ja kehitettiin internet alusta kerätyn aineiston tutkimiseen ja hakujen suorittamiseen. Aineisto löytyy internet osoitteesta: http://www.amcase.info/. Rekisteröitymällä kuka vain voi syöttää alustalle lisää aineistoa. Kappaleiden suunnitteluohjeet on julkaistu Suomen pikavalmistusyhdistyksen sivuilla: http://firpa.fi/html/am-tietoa.html. Ohjeesta löytyy mm. suositeltu minimi seinämänvahvuus, suositellun pienimmän yksityiskohdan koko, tyypillinen markkinoilta löytyvä rakennuskammin koko, sekä tyypilliset materiaalit. Valmiiden kokoonpanojen ja mekanismien suunnitteluun muodostettiin Objet 30 ja UPrint SE+ laitteelle ohjeistus josta löytyy pienin radiaalinen välys, aksiaalinen välys, sekä pienin rako riippuen rakennussuunnasta. Tutkimusprojektin aikana seurattiin alan teknologian kehitystä. Kahden vuoden aikana markkinoille ilmaantui noin. 50 uutta laitevalmistajaa, sekä noin 300 erilaista laitetta, sekä lukuisia materiaaleja. Merkittävimmät uudistukset listattiin ja pohdittiin mahdollisia kehityssuuntia. Kaikki uudet toimijat ja laitteet päivitettiin Firpan ylläpitämään tietokantaan: http://firpa.fi/html/am-tietoa.html. Markkinoilla on selvä suuntaus tuotantokomponenttien valmistamiseen, kotitulostimien hintojen laskemiseen, sekä isompien kappaleiden valmistamiseen. Muovilevy komponenttien muovaamista tutkittiin laserin ja alipaineen avulla DDShape laitteella. Laitteella onnistuttiin tekemään testikappaleita ja laitetta saatiin kehitettyä eteenpäin. Laitteiston kehittämiseksi ja kaupallistamisen tueksi Tekes on myöntänyt "Tutkimusideoista uutta tietoa ja liiketoimintaa" (TUTLI) rahoituksen. ISF mini projektissa onnistuttiin kehittämään edullinen pienten kappaleiden painomuovauskone. Samalla kartoitettiin laitteelle soveltuvat parametrit ja rajoitukset. Laseravusteisella muovaamisella päästään kuparilla isompaan seinämän kaltevuuteen ja pinnalaatu pysyy hyvänä. Teräksellä laserista ei ollut juuri hyötyä ja alumiinilla muovattavuus kyllä parani, mutta pinnalaatu huononi. AM kappaleiden viimeistelykoneistuksessa tutkittiin muovisten kappaleiden viimeistely jyrsimällä, sekä metallikappaleiden automaattista hiontaa. Jyrsinnässä vertailtiin eri menetelmillä tehtyjä kappaleita, sekä mitattiin kappaleiden mittatarkkuutta ja geometrisia toleransseja. Huonosta kotitulostimella tehdystä kappaleesta on vaikea saada hyvää kappaletta vaikka se viimeisteltäisiin koneistamalla. Suurimmat ongelmat liittyvät kappaleiden vääntymiseen johtuen lämpöjännityksistä valmistusprosessin aikana. Kappaleiden automaattisessa hionnassa parhaat tulokset saatiin DMLS kappaleille käyttämällä hionta-aineena teräshauleja ja pyörittämällä niitä hiottavat kappaleen kanssa rummussa. Ra arvo parani tällöin noin seitsemästä mikrometristä kolmeen mikrometriin

Fixtureless automated incremental sheet metal forming

Die-based forming is a technology used by many industries to form metal panels. However, this method of forming lacks flexibility and cost effectiveness. In such cases, manual panel beating is typically undertaken for incremental forming of metal panels. Manual panel forming is a highly skilled operation with very little documentation and is disappearing due to non-observance and a lack of interest. Confederation of British Metal forming (CBM) and Institution of Sheet Metal Engineering (ISME) have realised the need for capturing and understanding manual skills used by panel beaters to preserve the knowledge. At the same time, industries seek for alternative panel forming solutions to produce high quality and cost-effective parts at low volume and reduce the repetitive, yet adaptive parts of the panel forming process to free up skilled workers to concentrate on the forming activities that are more difficult to automate. Incremental forming technologies, currently in practice, lack adaptability as they require substantial fixtures and dedicated tools. In this research a new proof-of-concept fixtureless automated sheet metal forming approach was developed on the basis of human skills captured from panel beaters. The proposed novel approach, named Mechatroforming®, consists of integrated mechanisms to form simple sheet metal parts by manipulating the workpiece using a robotic arm under a repetitive hammering tool. Predictive motion planning based on FEA was analysed and the manual forming skills were captured using a motion capture system. This facilitated the coordinated hammering and motion of the part to produce the intended shape accurately. A 3D measurement system with a vertical resolution of 50μm was also deployed to monitor the formation of the parts and make corrections to the forming path if needed. Therefore, the developed mechatronic system is highly adjustable by robotic motion and was closed loop via the 3D measurement system. The developed automated system has been tested rigorously, initially for bowl shape parts to prove the principle. The developed system which is 98% repeatable for depth and diameter, is able to produce targeted bowl shape parts with ±1% dimensional accuracy, high surface quality, and uniform material thickness of 0.95mm when tested with aluminium. It is envisaged that by further research, the proposed approach can be extended to form irregular and more complicated shapes that are highly in demand in various industries

A Methodological Approach to Knowledge-Based Engineering Systems for Manufacturing

A survey of implementations of the knowledge-based engineering approach in different technological sectors is presented. The main objectives and techniques of examined applications are pointed out to illustrate the trends and peculiarities for a number of manufacturing field. Existing methods for the development of these engineering systems are then examined in order to identify critical aspects when applied to manufacturing. A new methodological approach is proposed to overcome some specific limitations that emerged from the above-mentioned survey. The aim is to provide an innovative method for the implementation of knowledge-based engineering applications in the field of industrial production. As a starting point, the field of application of the system is defined using a spatial representation. The conceptual design phase is carried out with the aid of a matrix structure containing the most relevant elements of the system and their relations. In particular, objectives, descriptors, inputs and actions are defined and qualified using categorical attributes. The proposed method is then applied to three case studies with different locations in the applicability space. All the relevant elements of the detailed implementation of these systems are described. The relations with assumptions made during the design are highlighted to validate the effectiveness of the proposed method. The adoption of case studies with notably different applications also reveals the versatility in the application of the method

Knowledge-driven Artificial Intelligence in Steelmaking: Towards Industry 4.0

With the ongoing emergence of the Fourth Industrial Revolution, often referred to as Indus-try 4.0, new innovations, concepts, and standards are reshaping manufacturing processes and production, leading to intelligent cyber-physical systems and smart factories. Steel production is one important manufacturing process that is undergoing this digital transfor-mation. Realising this vision in steel production comes with unique challenges, including the seamless interoperability between diverse and complex systems, the uniformity of het-erogeneous data, and a need for standardised human-to-machine and machine-to-machine communication protocols. To address these challenges, international standards have been developed, and new technologies have been introduced and studied in both industry and academia. However, due to the vast quantity, scale, and heterogeneous nature of industrial data and systems, achieving interoperability among components within the context of Industry 4.0 remains a challenge, requiring the need for formal knowledge representation capabilities to enhance the understanding of data and information. In response, semantic-based technologies have been proposed as a method to capture knowledge from data and resolve incompatibility conflicts within Industry 4.0 scenarios. We propose utilising fundamental Semantic Web concepts, such as ontologies and knowledge graphs, specifically to enhance semantic interoperability, improve data integration, and standardise data across heterogeneous systems within the context of steelmaking. Addition-ally, we investigate ongoing trends that involve the integration of Machine Learning (ML)techniques with semantic technologies, resulting in the creation of hybrid models. These models capitalise on the strengths derived from the intersection of these two AI approaches.Furthermore, we explore the need for continuous reasoning over data streams, presenting preliminary research that combines ML and semantic technologies in the context of data streams. In this thesis, we make four main contributions: (1) We discover that a clear under-standing of semantic-based asset administration shells, an international standard within the RAMI 4.0 model, was lacking, and provide an extensive survey on semantic-based implementations of asset administration shells. We focus on literature that utilises semantic technologies to enhance the representation, integration, and exchange of information in an industrial setting. (2) The creation of an ontology, a semantic knowledge base, which specifically captures the cold rolling processes in steelmaking. We demonstrate use cases that leverage these semantic methodologies with real-world industrial data for data access, data integration, data querying, and condition-based maintenance purposes. (3) A frame-work demonstrating one approach for integrating machine learning models with semantic technologies to aid decision-making in the domain of steelmaking. We showcase a novel approach of applying random forest classification using rule-based reasoning, incorporating both meta-data and external domain expert knowledge into the model, resulting in improved knowledge-guided assistance for the human-in-the-loop during steelmaking processes. (4) The groundwork for a continuous data stream reasoning framework, where both domain expert knowledge and random forest classification can be dynamically applied to data streams on the fly. This approach opens up possibilities for real-time condition-based monitoring and real-time decision support for predictive maintenance applications. We demonstrate the adaptability of the framework in the context of dynamic steel production processes. Our contributions have been validated on both real-world data sets with peer-reviewed conferences and journals, as well as through collaboration with domain experts from our industrial partners at Tata Steel

Mass Production Processes

It is always hard to set manufacturing systems to produce large quantities of standardized parts. Controlling these mass production lines needs deep knowledge, hard experience, and the required related tools as well. The use of modern methods and techniques to produce a large quantity of products within productive manufacturing processes provides improvements in manufacturing costs and product quality. In order to serve these purposes, this book aims to reflect on the advanced manufacturing systems of different alloys in production with related components and automation technologies. Additionally, it focuses on mass production processes designed according to Industry 4.0 considering different kinds of quality and improvement works in mass production systems for high productive and sustainable manufacturing. This book may be interesting to researchers, industrial employees, or any other partners who work for better quality manufacturing at any stage of the mass production processes

A comparison of processing techniques for producing prototype injection moulding inserts.

This project involves the investigation of processing techniques for producing low-cost moulding inserts used in the particulate injection moulding (PIM) process. Prototype moulds were made from both additive and subtractive processes as well as a combination of the two. The general motivation for this was to reduce the entry cost of users when considering PIM. PIM cavity inserts were first made by conventional machining from a polymer block using the pocket NC desktop mill. PIM cavity inserts were also made by fused filament deposition modelling using the Tiertime UP plus 3D printer. The injection moulding trials manifested in surface finish and part removal defects. The feedstock was a titanium metal blend which is brittle in comparison to commodity polymers. That in combination with the mesoscale features, small cross-sections and complex geometries were considered the main problems. For both processing methods, fixes were identified and made to test the theory. These consisted of a blended approach that saw a combination of both the additive and subtractive processes being used. The parts produced from the three processing methods are investigated and their respective merits and issues are discussed