'Smart’ manufacturing technologies such as data analytics and IoT has created the need for a higher level of engineering skills and expertise than has been required to date
Mech

 

Authors: Denis P Dowling and Eamonn Ahearne, School of Mechanical and Materials Engineering, UCD

Manufacturing increasingly uses techniques such as data analytics, advanced process monitoring and the internet of things, in order to enhance productivity. The adoption of these ‘smart’ manufacturing technologies has created the need for a higher level of engineering skills and expertise, than has been required to date.

During manufacturing operations it can be difficult to halt production in order to provide process-specific staff training. For this reason larger companies have set up internal training facilities that simulate the manufacturing environment in order to provide this training.

A ‘hands-on’ approach to education


These dedicated, close to production training activities are referred to as Learning Factories. The aim of these factories is to enhance a trainee’s competence in a production process and it is thus a ‘hands-on’ approach to education.

The advantages of ‘Learning by doing’ have been recognised for a long time. For example, in the 1960s Edward Dale proposed through his ‘Cone of Learning’, that after two weeks, we remember only 10 per cent of what we read, but 90 per cent of what we do the Learning Factory approach helps to address this, by providing hands-on training in manufacturing.

These facilities can be located within either academic institutions or companies. Examples of the latter Learning Factories are the lean manufacturing facilities at Daimler Chrysler, Mannheim (turbo charger manufacture) and that at Volkswagen, Wolfsburg (auto parts). Using the close to production line facilities, courses are tailored to meet the training needs of different grades of company staff. Associated lectures support the practical work carried out in the Learning Factory.

A feature of the Learning Factory is that groups of workers can learn to work in teams to address specific production problems. Thus problem solving, team work as well as process expertise are among the learning outcomes. The trainees acquire competencies in a structured, active learning environment.

iFactory tailored to address training needs in production/assembly line operations


The Learning Factory approach has been found to be so successful that some service providers now provide bespoke Learning Factories for specific industry sectors. An example is Festo Ltd who have produced their modular factory called iFactory, which is tailored to address training needs in production/assembly line operations.

The iFactory can be tailored to meet the specific end user requirements, but it can include training in automation, pneumatics, computer interface, camera inspection etc. The modular production line allows simulation of multiple assembly scenarios.

As outlined earlier the Learning Factory approach has also been introduced into academic institutions, with a small number of universities introducing dedicated facilities. Among these are:

  • TU Munich (Germany) – Automobiles (Energy conversion);
  • University of Patras (Greece) – Automation;
  • TU Darmstadt (Germany) – Automobiles (Lean production);
  • Free University of Bozen-Bolzano (Italy) – Pneumatic cylinder production.

These facilities are used to provide training programmes for undergraduate students to help them prepare for the working environment. Thus they also provide an insight into the use of more advanced processing technologies in manufacturing. A further objective is that the academic based Learning Factories can also provide a training resource for engineers based in industry.

Lifelong learning has become crucially important particularly for production engineers, as many manufacturing processes have shorter product life cycles and involve an increasing variety of new technologies. The challenge for companies as well as for universities is therefore to establish more effective and sustainable methods for both knowledge acquisition and knowledge transfer.

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Part of the UCD Learning Factory in Smart Precision Manufacturing

The UCD Learning Factory in Smart Precision Manufacturing was established in September this year. Its aim is to provide training on the use of ICT-enabled technologies in conjunction with machining.

The activity consists of a manufacturing process chain including: a five-axis computer controlled (CNC) machining centre (Mori Seiki), a CNC turning centre, and other machine tools, in conjunction with sample handling, metrology (CMM), high levels of process monitoring and ‘supply chain’ interconnectivity using an RFID system.

As part of this demonstrator students are involved in the fabrication of a two-part assembly comprising a standard spindle taper mounting and a high precision grinding tool adapter. This involves the following activities:

  1. Component design (CAD)
  2. Materials selection
  3. Machining
  4. Process monitoring (sensors for force, temperature, torque etc)
  5. Analysis of process data using data analytics
  6. Demonstration of RFID capability
  7. Final part inspection (dimensional, roughness etc)

In order to obtain data in real time it was necessary to directly connect the five axis DMG Mori Machining Centre into a central computer database, which also monitored data from other sensors and metrology in the process chain. The interface with the DMG Mori machine was achieved using a system based on the ‘MT Connect’ industry standard and further developed in a collaboration with Prof D Dornfeld’s research group at the UC Berkeley.

Sensors mounted on equipment can measure key process parameters


Using the internet, process data can be monitored and analysed remotely. Thus, sensors mounted on the equipment can measure key process parameters such as force, acoustic emission levels, spindle speed, axis positions, feed rates and power. This data is collected and analysed using data analysis techniques. The latter is supported through the work of the SFI INSIGHT Centre (www.insight-centre.org).

Their role has been to analyse the vast amounts of machine, product and process related data that will be available in a real-time manner. The results to date are promising with indications that it should be possible to predict  machine tool performance more accurately and also to predict the onset of wear related phenomena (tool wear in particular).

The Learning Factory RFID system incorporates both static and active tags (with power), enabling exchange of data between the machine tools and tags on the part or fixture. All of the systems and sensors are connected wirelessly or hardwired to a powerful computing infrastructure with advanced software for process analytics.

The UCD Learning Centre is currently focused on the training of undergraduate manufacturing students. To date more than 80 students taking the Manufacturing II module have carried out experimental work in the Learning Factory. Many of the team-based case studies that the students undertake are also based around the manufacturing activities associated with this facility.

A number of final year/graduate students also have projects associated with the Learning Factory, ranging from machining, to supply chain and statistical process control. These involve six academic staff members. Going forward, it is planned to establish a dedicated programme for the training of industry staff involved in managing manufacturing process chains at different levels, from operations to senior engineering levels.

In addition to training activities, there are already a small number of company supported research projects which use the Learning Factory. These facilitate the investigation of the potential of data analytics as a means of predicting the outcomes of material processing treatments. In summary therefore the Learning Factory provides a key resource for both academic and industry training as well as providing a state-of-the-art, smart manufacturing research facility.

 

http://www.engineersjournal.ie/wp-content/uploads/2016/02/aaacol1a1-1024x576.jpghttp://www.engineersjournal.ie/wp-content/uploads/2016/02/aaacol1a1-300x300.jpgDavid O'RiordanMechmanufacturing,UCD
  Authors: Denis P Dowling and Eamonn Ahearne, School of Mechanical and Materials Engineering, UCD Manufacturing increasingly uses techniques such as data analytics, advanced process monitoring and the internet of things, in order to enhance productivity. The adoption of these ‘smart’ manufacturing technologies has created the need for a higher level of...