Tiny Photonic Sensors Could Make Composite Manufacturing Cheaper and Greener

Composite materials have transformed modern engineering, from the wings of passenger aircraft to the blades of wind turbines and the body panels of high-performance cars. Their appeal lies in an extraordinary combination of properties: stiffness that rivals metals at a fraction of the weight, resistance to corrosion, and the ability to be tailored to almost any structural demand. But composites are also unforgiving to manufacture. Resin must flow into fibre beds at exactly the right temperature and pressure, and curing must progress on schedule. When something goes wrong inside the tool, the first sign is often a defective part worth thousands of pounds, along with the energy and material that were wasted making it. A new study published in Clean Technologies and Environmental Policy now asks a question that has largely been ignored in the rush to embed smart sensors inside composite tooling: what does it actually cost, in euros and in emissions, to put a photonic sensor inside a manufacturing tool?

The research, carried out by Xiaofei Cui of TWI Limited together with Akram Zitoun and Sadik Omairey of the Brunel Composites Centre at Brunel University London, examines a photonic-integrated sensor system developed under the European Union-funded SEER project. The sensor is designed to be embedded directly within composite tooling, where it monitors refractive index, temperature, and pressure in real time. These three parameters are the vital signs of composite production: refractive index reveals how the resin is flowing and curing, temperature governs the chemistry of the cure cycle, and pressure indicates whether the part is being consolidated properly. Because the sensor sits inside the tool surface, it can detect anomalies as they happen rather than after the part has been removed and inspected, offering the prospect of fewer scrapped components and more repeatable processes.

At the heart of the device is a photonic-integrated circuit, or PIC, fabricated using silicon-on-insulator technology. The prototype chips were produced through a multi-project wafer run, starting from a 725-micrometre silicon substrate topped with a 2-micrometre buried oxide layer. Two types of silicon waveguides, each 220 nanometres thick, guide light through the chip: a strip waveguide and a rib waveguide with a 70-nanometre etched slab. A 520-nanometre low-temperature oxide layer and a thin silicon nitride cap complete the stack. Light travelling through these waveguides interacts with its surroundings, so changes in the refractive index of the resin, or in temperature and pressure at the sensor surface, shift the optical signal in ways that can be read out with high precision. The chip is then post-processed, with reactive ion etching or wet etching used to create the refractive index sensing features and anisotropic etching or substrate bonding used to form the pressure-sensing membrane.

What makes the study unusual is that the authors did not stop at demonstrating that the sensor works. They built a combined life cycle costing and life cycle assessment framework to evaluate the device from prototype fabrication through to industrial scale-up. The economic side used a hybrid bottom-up and activity-based costing model, in which the total cost of production is the sum of the costs of every individual manufacturing activity, each broken down into labour, materials, energy, and equipment. The environmental side was modelled in GaBi software in accordance with the ISO 14040 and ISO 14044 standards, taking a single sensor as the functional unit and covering raw material supply, manufacturing, distribution, use, and end-of-life pathways including reuse and landfill.

The cost findings are striking. Across the prototype stage, labour was the dominant expenditure in both recurring and non-recurring costs, dwarfing the contributions of materials, energy, and equipment. At the process level, assembly dominated the recurring costs, substantially exceeding PIC fabrication, testing and validation, and coating. This is not surprising when the assembly sequence is examined in detail: the PIC with its micro-lens must be manually inserted into a metal adapter, a separate lens mounted on a ceramic ferrule holding the optical fibre must be aligned using a six-degree-of-freedom positioner, and each joint must be secured with epoxy and cured. These delicate optical alignment steps are precisely the operations that photonic packaging engineers identify as the bottleneck in moving integrated photonics from the laboratory to volume production.

To understand how costs might evolve as production scales up, the team applied learning curve analysis, a well-established manufacturing economics technique that captures how labour hours per unit fall as workers gain experience and processes mature. The analysis assumed learning rates of 80, 85, and 87 per cent, drawn from the literature, and revealed a pattern familiar from aircraft and semiconductor manufacturing. Early gains are dramatic: at low volumes, suitable investment can improve productivity by around one per cent per unit. But the improvements saturate quickly. After the 191st unit, the potential gain drops to just 0.1 per cent, and after the 365th unit it falls to 0.05 per cent. Beyond that endpoint, further investment in training and tooling will not pay for itself, a conclusion with direct consequences for how manufacturers should time their automation investments.

The sensitivity analysis added a geopolitical dimension to the economics. Labour accounted for 77 per cent of total recurring costs in the scale-up scenario, so regional wage differences matter enormously. Applying Eurostat data from 2022, which showed average hourly labour costs ranging from €8.2 in Bulgaria to €50.7 in Luxembourg and €55.6 in Norway, the researchers found that in high-wage countries labour represents more than 80 per cent of total production costs, while in lower-wage regions it falls to around 40 per cent. For a technology whose commercial viability hinges on labour-intensive assembly, decisions about where to manufacture and how much to automate could determine whether embedded photonic sensing reaches the factory floor at all.

The environmental assessment told a complementary story. PIC fabrication and post-processing emerged as the most energy-intensive stages, together accounting for more than 63 per cent of the total energy required to manufacture and use the sensor, a burden attributed to the energy-hungry clean-room operations and the complexity of photonic device fabrication. The same fabrication stage also returned the highest global warming potential and the highest ozone depletion rate in the life cycle impact results, partly because prototype production was performed manually and involved multiple research iterations. At the other extreme, sensor testing and validation consumed less than two per cent of the total energy, using small, low-emission equipment. Downstream processes such as coating, packaging, and integration contributed minimally to the overall footprint.

Perhaps the most valuable insight of the study is that the economic and environmental hotspots largely coincide. The labour-intensive assembly operations that dominate recurring costs and the PIC fabrication and post-processing stages that dominate energy demand and emissions are the same targets that automation, wafer-level batch processing, and improved first-time-right manufacturing would address. The authors recommend focusing automation specifically on the manual insertion, alignment, epoxy dispensing, and packaging steps; prioritising reductions in repeated prototype iterations during chip fabrication; comparing etching routes not only technically but in terms of energy demand and yield; and concentrating investment in productivity improvements before the learning curve saturates. They also caution against over-investing in environmental improvements to testing and validation, which is simply too small a contributor to matter.

The broader significance extends beyond one sensor design. As industries under pressure to decarbonise turn to lightweight composites, and as smart manufacturing promises to squeeze waste and defects out of production, embedded sensing is likely to become standard practice. But this study demonstrates that the sustainability case for such technologies cannot be assumed; it must be quantified. By showing that cost and carbon concentrate in the same early manufacturing stages, and that learning curves, labour rates, and automation strategies jointly determine commercial viability, the researchers have provided a reusable framework for evaluating emerging photonic sensing technologies as they make the difficult transition from research prototype to industrial product. For composite manufacturers weighing whether to embed intelligence in their tooling, the answer, it turns out, depends as much on economics and energy as on optics.

Subject of Research: Environmental and economic life cycle assessment of an embedded photonic sensor system for real-time composite manufacturing monitoring

Article Title: Environmental and economic assessment of an embedded photonic sensor system for composite manufacturing monitoring

Article References: Cui, X., Zitoun, A., & Omairey, S. (2026). Environmental and economic assessment of an embedded photonic sensor system for composite manufacturing monitoring. Clean Technologies and Environmental Policy, 28(10), Article 265. https://doi.org/10.1007/s10098-026-03614-5

Image Credits: AI Generated

DOI: 10.1007/s10098-026-03614-5

Keywords: composite manufacturing, photonic sensors, photonic integrated circuits, life cycle assessment, life cycle costing, learning curve, industrial automation, sustainability, silicon photonics, process monitoring, labour costs, clean technology