The MTS Philosophy
Overall, it is key to achieve a significant step change in the effectiveness and efficiencies of CNC manufacturing equipment for all sectors. The need to capitalise on existing practical know-how to leverage the process of translating digital instruction into physical action based on data driven engineering within a high production output environment in manufacturing components. When evaluating the digital manufacturing process in a regulated production setting, there is significant process flow challenges in place which may be addressed by cognitive algorithms being processed at the point of action. The competitive advantage will be realized by the ability to make engineering process decisions driven directly by data. These tools are in unsurpassed demands within the manufacturing sector as the cost erosion of components becomes more prominent. Therefore, we all need to accelerate the uptake of closed loop digital instruction into physical action based on data driven engineering.
Goals derived by data
The overall goal is to translate digital instruction into physical action based on data driven engineering to enable a significant step change in process efficiency and as a result deliver enhanced cost competitiveness. To capitalize on the advanced in-house machining capabilities, to develop an industrial Internet of Things (IoT) platform which will allow for machining analytics to be transformed into actions. Digital manufacturing may be viewed as an intersection of IoT technologies, data generation and machine learning algorithms. Developing a successful understanding of how to best allow algorithms to learn in a manufacturing environment would potentially result in a reduction of labour costs and unplanned downtimes along with an increase in production speed. IoT technologies will allow for a deployable platform that is scalable to large volumes of data while also offering data encryption for security purposes. This will involve developing an end-to-end machine learning based framework that utilizes information contained in the data to report insights and decision aids back to operators.
Vision and Competitive advantage
The competitive advantage will be realised by having the functionality to store data streams in a structured format before producing insights and analytics of each machine/device. By streaming data in real time and establishing inter machine connectivity, machine learning algorithms will be deployed to each device to predict potential problems in the manufacturing process before they occur.
It is important to have a vision of accelerating the pace of digital transformation within a highly regulated manufacturing environment. However, there are limited options associated with the required technology investments. We envisage an interconnected machining environment which allows clients to meet market demands in a more efficient manner whilst also accelerating the use of advanced statistical technologies to fully align with Industry 4.0. Although data offers numerous opportunities for improving manufacturing practices, making use of the available information presents challenges such as data storage, data processing and the ability to extract meaningful insights. These challenges have prevented clients from realising the entire potential of Industry 4.0. Our service aims to provide clients with a solution to the breakdown in the data pipeline of manufacturing systems.