MathWorks addresses engineers as well as R&D managers
Under the impulse of trends like ‘big data’, MathWorks, a leading developer of mathematical computing software for engineers and scientists, is broadening its scope, from the technical disciplines to other – more business and service-oriented – domains. In order to promote the broad spectrum of applications for its tools and their role in improving the effectiveness of R&D processes and hence the competitiveness of companies, MathWorks has started organising joint events for engineers and R&D managers. Link Magazine reports on the March 2016 event in Eindhoven, the Netherlands.
MathWorks’ main products are MATLAB and Simulink. MATLAB is the language of technical computing that provides a programming environment for algorithm development, data analysis, visualisation, and numeric computation. Simulink is a graphical environment for simulation and Model-Based Design of multi-domain dynamic and embedded systems. Traditionally these tools have been used for R&D and systems development in automotive, aerospace, communications, electronics, and industrial automation industries. However, these tools are also increasingly being utilised for modelling and simulation in other application fields, ranging from predictive maintenance to financial services to computational biology.
In parallel with this development, MathWorks is evolving from a software supplier to a strategic business partner, Marcel Stakenborg, managing director of MathWorks Benelux, explains during the one-day event in Eindhoven. ‘On days like this, we do not just want to show our products. We also present trends in industry and demonstrate the business aspects of working with our products. I see developments happening very fast, so we have to stay connected to the industry.’ Among other things, Stakenborg refers to Industry 4.0, or Smart Industry as it is called in the Netherlands, the ongoing automation, robotisation and digitisation of industry. ‘It is gathering momentum, but the problem is that a lot of talk about these subjects is rather abstract and academic, directed at the long term. People disconnect and management are hesitant to adopt this kind of innovation even if significant savings lie ahead. A lot of companies are reluctant to embrace this new technology. Many higher level managers in de machine building industry have a mechanical engineering background, and software is new to them. We want to take away some of this fear by showing that it already works in practice. The gap between traditional engineering disciplines and new software-based approaches has to be bridged. Therefore, we want to demonstrate the business value of using tools like ours.’ In the theme for the event, this is articulated as follows: ‘In order to stay competitive, machine builders need to embrace simulation-based R&D, hardware-independent software development, and data analytics.’ In Eindhoven, representatives from MathWorks customers present their perspective on the matter and illustrate it with appealing cases.
The first topic, simulation-based R&D, is covered by Lucas Koorneef, senior mechatronic systems designer at MI-Partners, the Eindhoven-based R&D company for high-end mechatronic products and systems. ‘We help customers in the early stages of product development to precisely define the specifications for their new machines. Sometimes the customers’ expectations are unrealistic. By means of modelling, we can check whether specs are viable. If not, we can tell the customer not to try to design the impossible, and show that if some parameters are changed, a working machine can be designed. Based on our simulations, we can come up with ideas the customer has not thought of before. Using models, we evaluate these ideas and build up confidence in a design before we have actual hardware to performing the testing. Then we still build a prototype in order to test the concept on actual hardware. As a mechatronic designer, you have to get your hands dirty. Building and testing helps you develop a gut feeling if things will work in reality or not. But we couldn’t do without modelling. It’s a basic tool in all our R&D and design processes.’ Machines are so complex nowadays, you have to take many things into account, yet there is no time to build extensive prototypes, test them and modify their design. ‘These steps can be eliminated, now that we are virtually working first-time-right’, Lucas Koorneef says in conclusion, pointing to the savings realized with simulation-based R&D in the design phase, which in turn give rise to business gains due to the accelerated market launch.
Another benefit of Model-Based Design (MBD) is knowledge management, adds Vincent Theunynck, co-founder of Vintecc, a Belgium-based service provider that assists organisations in implementing MBD. ‘Traditionally, there may be some formal documents, but most of the innovation is in the heads of people. If you ‘externalise’ their knowledge through models, it is easier to share information and to set-up communication between all the disciplines involved.’ But perhaps the biggest advantage of MBD, according to Theunynck, is that software development has become hardware-independent. Besides simulation/modelling, the second feature of MBD tools is automatic code generation. This code, for system control, can be tested using the models that were generated. It is independent of the final hardware implementation (mechanics as well as electronics), which can therefore be determined in a late stage of the development process. This will speed up development and reduce (hardware) failure costs.
To stay competitive, machine builders need to embrace data analytics, simulation-based R&D, and
hardware-independent software Development.
Theunynck presents the case of a multi-axle harvesting machine. Because of a short harvesting season, time for hardware testing was tight. MBD accounted for shortening of the development cycle by months, with 90% of the design being verified before hardware was available. This resulted in first field deployment without a single software error and, on top of that, the design delivered more features than the customer had initially requested. ‘There is a great need in industry for these kinds of workflows, with automatic code generation, rapid prototyping and functional software-based testing. Yes, all this is possible with MBD.’
And a lot more is possible using software and applied mathematics, as is demonstrated by Tim Pattenden, analytics consultant with Tessella, an international analytics and data science consulting services company. For example, his company has developed complex control systems for aerospace applications, but Pattenden’s topic is getting a lot of attention by Machine builders nowadays: the use of data analytics for predictive maintenance. The idea is that data can be gathered on the performance of a system or a process using all kinds of sensors. Subsequently, these data can be analysed using mathematical (including statistical) tools and software models to make predictions about the future failure of system components for optimal planning of preventive maintenance. Pattenden discusses three cases, two successful ones (remote condition monitoring in a nuclear waste plant and improvement of train fleet reliability) and an unsuccessful one (bioreactor contamination prevention), in order to reflect on the success criteria: which data analytics cases deliver business value and which ones don’t? It’s all about first deciding on the goals and the careful set-up of a data analytics business case, including the definition of feasible interventions, before just starting to gather data and analyse them, with the risk of finding ‘random’ correlations or ‘false-positive’ incidents.
The business value question is highly relevant, according to Pattenden. ‘For example, most of the blue-chip companies have bought big data analytics frameworks, but relatively few have used them to do anything successful. Companies like the Amazons, Facebooks and Googles of this world have large amounts of similar data which they have to process in real time. For them, a big data platform can provide a lot of value. But, for instance, pharmaceutical or oil & gas companies have a hundred different parts of the business, each with its own types of data and problems. In my experience, using one big data platform for the entire organisation is not helping their individual business units to get the data solutions they need. What they need is people who understand the analytics and the particular domain they are working in, rather than a 20 million euro big data platform.’ Software or hardware, it’s people who make the difference and MathWorks tools enable them to succeed.