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OPERATORKNOWLEDGE

Challenge

A first technological barrier is the difficulty to efficiently generate work instructions for lack of an automatic procedure to derive work instructions from CAD files. The possibility to generate work instructions from CAD files will enable operators to create and adapt work instructions, starting from the very first prototype that has been assembled. 

A second technological barrier is the lack of a technological solution that can be used to capture operator feedback during the execution of their work instructions. No operator-friendly and easy-to-use feedback systems are available that enable operators to either adapt existing assembly instructions or create new assembly instructions for the tasks they are performing. This can be solved by developing a system to capture operator feedback, either implicitly or explicitly, with limited overhead. In both cases, several technological challenges need to be resolved:

  • To allow operators to give explicit feedback on the work instructions in a flexible way, they will need various input channels. Examples include picture or video captures, special annotations, personal notes and voice recordings.
  • In addition to this explicit feedback, implicit monitoring of the assembly workspace will also reveal improvement opportunities. Deviations between the instructions and the actual execution may indicate poor instruction quality. Comparing the performance of alternative assembly task executions or sequences, executed by the same or by different operators, can help to develop a new best practice for the relevant assembly task or to implement a crucial quality check. Tools need to be developed that can easily collect, merge and analyse sensor signals in support of the adaptation process of the instructions based on the detected assembly methodology.

The third technological barrier is the lack of tooling for the methods engineer to efficiently update work instructions based on the input of operators. First of all, a version management system is required. In extreme situations, every operator should be allowed to document his or her personalised instructions. Still, a proliferation of versions makes it difficult to keep track of their dependencies and to apply engineering changes to them. An instruction inheritance approach will be required to reduce information redundancy and optimise change management. Secondly, when operators will start providing feedback on work instructions and giving suggestions for new work instructions, a lot of information will become available to the methods engineer. This will create yet another challenge: the methods engineer will have to be able to process this information to efficiently come up with a new set of updated work instructions. A quantification of the quality of work instructions will allow to monitor the quality evolution of a set of instructions and to compare different sets to one another.

Project goals

The first goal of the project is to develop a tool that is able to automatically derive work instructions from CAD files.

The second goal of the project is to develop a technological solution to capture the assembly knowledge of experienced operators without imposing excessive data entry and registration tasks on them. Both a solution to capture implicit and explicit feedback will be developed:

  • explicit feedback solution: the objective is to develop a methodology for selecting suitable sensors for the majority of assembly operations and to create a user interface to efficiently capture online operator feedback. Furthermore, tools will be developed to define inline memory points that can be documented in further detail later on during the offline creation or adaptation of assembly instructions.
  • implicit feedback solution: the objective is to develop knowledge extraction algorithms that can automatically suggest potential improvements for the work instructions to the methods engineer.

The third goal of the project is to develop tools for version management and automatic work instruction suggestions, which can be used by the methods engineer to efficiently update work instructions based on the input of operators.

To this end, the operator will need a digital platform, where he can view (customised) instructions and comment on them. Ideally, the methods engineer builds a framework designating what has to be done and the operator can complement this with how it should be done. As a result, manufacturing companies will be able to employ more lower-skilled operators per high-skilled methods engineer, improving the demand/supply balance in the job market. Moreover, the efforts to make and use the instructions are directly correlated to their added value: most attention will be paid to difficult operations.

Economic value

Integrators and technology experts will use this project to combine and enhance the capabilities of their current products with a complete, operator-centred adaptive system for assembly instructions. They will extend their commercial offerings with the results of this project. The modular structure of the project allows each partner to focus on its own core business and further enhance its current products to promote their introduction and growth in the manufacturing industry. Additionally, the integration of their complementary solutions will provide future clients with a complete system that has been tested and evaluated by well-known manufacturing companies. These companies will integrate the system in their current production facilities and, as such, act as reference customers for integrators.

Product manufacturers will already have demonstration cases available during the project. After the project, they will further industrialise these results (from TRL 5 to TRL 9). If appropriate, collaborations between the product manufacturers and research partners on the one hand and/or the integrators and technology experts on the other will be set up. For the product manufacturers in the consortium, the envisaged benefits from the project results and the ensuing efforts are as follows:

  • a reduction of the cognitive load, stress, learning time and failure rate
  • an increase of the overall efficiency thanks to the lean character of this new system
  • improved management of the increasing complexity and diversity in their product range

This will support them in their continuous struggle for market competitiveness and will contribute to the further positioning of their Flemish manufacturing site and its activities as lead plant within the whole of their organisation.

Contact

Sonia Vanderlinden - sonia.vanderlinden@flandersmake.be 

 

Project partners 

Duration: 
1/01/2018 to 31/12/2019