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Smart manufacturing, materials science, and industrial innovation
6 Problem Statements in this Domain (5 Standard + 1 Open Innovation)
Weld defects (porosity, incomplete fusion, cracking) that are not caught at the point of manufacture can lead to structural failure later, but reliable non-destructive weld inspection (X-ray, ultrasonic testing) requires equipment and trained technicians that are economically out of reach for most small-scale manufacturing units, which currently rely largely on visual inspection alone — insufficient for catching subsurface or fine-grained defects that visual inspection cannot detect.
Undetected tool wear during CNC machining leads to dimensional inaccuracy, surface finish defects, and occasionally sudden tool failure that can damage the workpiece or machine, but most MSME-scale CNC machines lack the built-in sensor instrumentation that industrial-grade tool-condition-monitoring systems assume, and workshops currently rely mainly on operator experience and periodic manual inspection to judge when tools need replacement — an approach that is inconsistent and either wastes usable tool life or risks running tools to failure.
Using agricultural residue (e.g., rice husk, bagasse) or construction/demolition waste as feedstock for alternative building materials is attractive for both waste management and sustainability reasons, but the composition and quality of such waste feedstock varies significantly by source, season, and region, making it genuinely difficult to produce a construction material with consistent, predictable structural properties — a critical requirement for any material actually used in building construction where safety margins matter.
A large share of India's manufacturing base runs on legacy machinery with no built-in condition monitoring, meaning predictive maintenance — a well-established concept for modern, sensor-equipped industrial equipment — is largely inaccessible to MSMEs, who instead experience costly unplanned downtime from failures that better monitoring could have anticipated. The retrofit problem is harder than it appears: externally-added sensors must extract meaningful condition signals from machinery never designed for it, across a genuinely diverse range of legacy machine types and ages found across different workshops.
Metal 3D-printed parts can develop internal defects (porosity, incomplete fusion, voids) that are invisible externally but critical for structural applications, and the gold-standard detection method — industrial CT scanning — is expensive and slow enough that it is often reserved only for high-value aerospace-grade parts, leaving a large space of lower-cost but still safety-relevant metal-printed components without any practical internal defect verification method.
Have your own innovation in Smart Manufacturing? Design an AI predictive maintenance model for factory machinery, computer vision quality-inspection pipeline, digital twin simulator for assembly lines, or additive manufacturing optimization tool.