44: Industry in the Future: UTC an academic partner for enterprise

The ongoing digital transformation of industry is a major societal challenge. For UTC, accompanying a growing number of companies during the changes, the phenomenon represents an increasingly strategic field for studies. This Dossier zooms in on the university’s main activities and on the specific nature of its approach to the industries of the future.

44: Industry in the Future: UTC an academic partner for enterprise

Plastic Omnium: from simple reporting to ‘spec drift’ prediction

Plastic Omnium is about to engage on research in automated data analysis in a collaborative venture with the UTC-Roberval and UTC-Heudiasyc laboratories’.
Objective – to attain a new level in process control.

The Auto Inergy Division de Plastic Omnium, the world’s prime supplier of plastic fuel systems (tanks, piping …) and depollution systems for private vehicles, has 35 factories located in 19 different countries, with one site at Venette, in the Oise ‘Department’, including the company’s global R&D centre. Two sites close to UTC, with whom the industrialist has just signed a partnership on the theme of automated production data analysis. “As we see it,” notes Philippe Convain, Digital Manufacturing Director for the Division, “DA will be the key asset for ‘Industry in the Future’. Today, we have peaked out in terms of performance levels for controlling our processes. By better exploiting our data, we hope to be able to attain a new level, resulting in lower manufacturing costs and, in the long run, gains in flexibility and our capacity to rapidly change production, if the need arises”.


Less rejects, less stressful work

In its factories, Auto Inergy now collects and records huge amounts of data; data relating to the manufacturing process: when a fuel tank is pressure-formed, for example, some 5 000 parameters (temperatures, pressures, etc.) are recorded … a figure to be multiplied by the 20 million, i.e., the number of fuel tanks manufactured each year by the Group. There are also data about the products themselves (diameters, lengths, fuel proof assurance …) and the production environment (temperature in the assembly hall, etc.). “Traceability of our production proves very useful to explain a posteriori the reasons for a spec drift in product quality”, notes Philippe Convain. “Using automated data analysis should enable us to go much further down this road, and in the first instance, it will enrich our knowledge base about the processes we employ. Today for example, we measure the thickness of our tank walls. Without this tool of data analytics, we could not control tank wall thickness and we are talking about 5 000 data recorded each tank we pressure form and we use them to deduce the physical laws that describe the links between process parameters and the product characteristic specifications. If we can attain this goal, we shall no doubt discover a host of unsuspected links –links that we had in the back of our minds but for which we are now able to quantify to assess the real impact. And above other considerations, we shall be able to move forward from simple production reports to prediction of spec drifts: for a process that lasts from one minute, even if it takes two to three seconds computation to anticipate problem we would have enough time to react and thereby avoid a tank reject. This way, we should be able to reduce reject rates quite significantly”.

Yet another challenge consists of making the production operatives’ tasks easier. For complex processes such as pressure forming of tanks, the machines can set off hundreds of alarm signals that need to be interpreted in order to make the right decisions. This is a skill that requires years of experience. “If we had the appropriate tools capable of guiding the choice of a relevant corrective measure in the case of a spec drift, the process operators could acquire this know-how fairly easily”, thinks Philippe Convain. “Moreover, process monitoring would be less stressful and enable the operatives to focus more on improving process productivity. »


Multidisciplinary support

In order to accompany Plastic Omnium faced with these challenges, UTC will combine the expertise available at UTC-Roberval and UTC-Heudiasyc laboratories. Nassim Boudaoud, the research scientist who will be supervising the work for UTC-Roberval, defended his PhD thesis on system control at UTC-Heudiasyc and will be in a position to offer the industrialist partner his double culture in process engineering and data analysis. UTC-Heudiasyc will support the work, contributing to solving issues in process and product diagnostics and in the development of predictive and prescriptive models.

As a first stage, the collaborative agreement with Plastic Omnium will see the hiring of a PhD student, who will be assigned to the project area that the industrialist has reserved in the Venette factory. “Our processes are complex and there can be hundreds of reasons for a product ‘reject’”, explains Philippe Convain. “First and foremost, we have to prove the concept for a few identified faults, by experimenting via the demonstrator installed on the factory site. Then, gradually we will be able extend the tests to analyse other kinds of fault. And we shall test the predictive techniques on other pilot installations before we deploy them to all our factories”.