Technical authority

Use this page to connect a real person, Exafuse's LMD monitoring work and the public articles that explain it.

People

Manish Sharma — AI for Laser Metal Deposition and Metal Additive Manufacturing

Manish Sharma leads AI and R&D work at Exafuse, focusing on process monitoring, machine vision, robotic LMD/DED workflows, data systems, and inspection-aware engineering decision support.

At a glance

AI, machine vision and data systems for industrial LMD/DED workflows.

This page makes clear that Manish Sharma leads AI and R&D work at Exafuse in Bochum for process monitoring, machine vision, robotic LMD/DED workflows, data systems and inspection-aware decision support.

NameManish Sharma
RoleAI and R&D lead for process monitoring and LMD/DED systems
LocationBochum, Germany
OrganizationExafuse / ThinkIng - Additive Technology GmbH
Core statementManish Sharma leads AI and R&D work at Exafuse, focusing on process monitoring, machine vision, robotic LMD/DED workflows, data systems, and inspection-aware engineering decision support.

Bio

Research and technical communication tied to real applications.

The role is not abstract. It connects industrial LMD/DED practice with process monitoring, machine vision, data systems and clear technical review logic.

Manish Sharma, AI and R&D lead for process monitoring and LMD/DED systems at Exafuse
Contact

Manish Sharma

AI and R&D lead for process monitoring and LMD/DED systems

Bochum, Germany
Person

Manish Sharma

AI and R&D lead for process monitoring and LMD/DED systems

Profile

Manish Sharma leads AI and R&D work at Exafuse in Bochum, Germany, with a practical focus on process monitoring, machine vision, robotic LMD/DED workflows, data systems, and inspection-aware engineering decision support.

His work focuses on turning process images, monitoring signals and workflow data into usable industrial judgment: what should be reviewed next, which signals matter, how robotic LMD/DED routes should be interpreted, and where evidence is still too weak for an overconfident claim.

That scope connects research-facing development with public technical communication. It includes article review, machine-vision context, process-data interpretation and the translation of internal R&D topics into customer-facing material that stays grounded in inspection and engineering reality.

Focus areas

Where the work matters in practice.

Manish Sharma connects process monitoring, machine vision, data systems and robotic LMD/DED workflows with industrial Laser Metal Deposition.

Process monitoringInterpret melt-pool images, sensor signals and review triggers in a way that supports process understanding and realistic technical escalation.
Machine visionUse image processing and neural-network methods to turn noisy LMD process imagery into more useful engineering views.
Robotic LMD / DED workflowsConnect robotic deposition paths, machine context and workflow interpretation to public technical guidance that industrial buyers can actually use.
Data systemsStructure process data, article relationships and digital context so monitoring insights remain searchable, reviewable and useful across the Exafuse site.
Inspection-aware engineering decision supportKeep AI, monitoring and workflow claims tied to inspection, metallography, documentation and release criteria instead of dashboard-only narratives.

Public work

Public Exafuse articles closely tied to monitoring, AI and process data.

These pages are the clearest current public reference for Manish Sharma's work on AI, machine learning, image processing and process-data interpretation in the LMD context.

Contact sheet for reviewing LMD monitoring examples and edge cases
A12

Article

Monitoring and Control in DED/LMD: What In-Process Signals Mean for Quality

In-process monitoring in DED and LMD can help track whether the build is behaving consistently, but it does not replace final inspection or qualification.

Qualification
Image-processing path for melt-pool width measurement in LMD
A35

Article

Melt-Pool Monitoring in Laser Metal Deposition: What Process Images Can and Cannot Prove

Melt-pool monitoring in LMD is useful when it helps engineers see process behavior, compare signals with physical evidence and decide what needs closer review. It should not be sold as stand-alone proof of final part quality.

QualificationMetal AM routeDfAM and OEM
LMD process monitoring and melt-pool signal view
A36

Article

AI in Laser Metal Deposition Process Control: From Decision Support to Closed-Loop Claims

AI in Laser Metal Deposition is strongest as decision support, process-image interpretation and structured technical review. It becomes risky when model outputs are treated as autonomous quality release or closed-loop control...

QualificationMetal AM routeDfAM and OEM
AI-assisted image cleaning workflow for melt-pool monitoring
A37

Article

Image Processing with Neural Networks in LMD: Where Pix2Pix-Style Models Fit

Pix2Pix-style neural networks can support LMD image-processing research by translating process images into more useful visual representations, but generated outputs must be validated against real inspection and process context.

QualificationDfAM and OEMMetal AM route
Coaxial melt-pool analysis showing blue-linked glare channel evidence
A40

Article

Computer Vision for Melt-Pool Monitoring in LMD: Dataset Drift, False Positives and Practical Limits

Computer vision can improve melt-pool monitoring in LMD, but only when teams control dataset drift, false positives, false negatives and the gap between image patterns and physical part quality.

QualificationMetal AM routeDfAM and OEM
Image-processing path for melt-pool width measurement in LMD
A41

Article

LMD Monitoring Data Pipeline: From Camera Signal to Decision Support and Validation

A useful LMD monitoring pipeline does more than collect images. It links camera signals, process metadata, engineering context, review logic and physical inspection so the data supports a real decision.

QualificationMetal AM routeRFQ and buying
Segmentation benchmark heatmaps for validating AI image-processing outputs
A42

Article

How to Validate AI Outputs Against Physical Inspection in Laser Metal Deposition

AI outputs in Laser Metal Deposition become credible only when they are tested against physical inspection. The validation route has to connect model outputs to dimensional checks, microscopy, metallography and documented...

QualificationMetal AM routeRFQ and buying

Reviewed articles

Exafuse articles technically reviewed by Manish Sharma.

These pages sit close to industrial AI, process monitoring or LMD/DED systems questions and therefore carry an explicit Manish Sharma review relationship.

Laser Metal Deposition process basics visual for industrial buyer education
A03

Article

What Is Laser Metal Deposition (LMD) and When Should Industrial Buyers Use It?

Laser Metal Deposition is the route to evaluate when a part is large, expensive, repair-driven or best built by adding metal only where it creates value.

Metal AM route
Powder-bed fusion visual for SLM comparison
A04

Article

LMD vs SLM (PBF): A Practical Process-Selection Matrix for Industrial Metal Parts

LMD and SLM solve different industrial problems. LMD usually wins where the value sits in local buildup, repair, cladding or large-scale geometry, while SLM wins where compact fine detail or internal channels carry the part value.

Metal AM routeQualification
CNC-based LMD machine at Exafuse
A06

Article

LMD for Large Metal Parts: Bead Width, Deposition Efficiency, and Productivity

Large-part LMD is not just a scaled-up version of a small coupon build. For large components, productivity and quality depend on the interaction between bead width, overlap strategy, heat management, machining allowance,...

Metal AM route
Finished component after LMD deposition and post-processing
A18

Article

Hybrid Manufacturing: Combining LMD and CNC Machining to Hit Tolerances on Large Parts

Hybrid manufacturing with LMD and CNC is often the practical route for large industrial parts. LMD adds material where it creates value.

Metal AM routeDfAM and OEM
BreitbahnDED research process visual
A21

Article

Research Spotlight: BreitBahnDED and Why Wider Beads Matter for Industrial Productivity

BreitBahnDED is a publicly funded research project for developing wider weld beads in Laser Metal Deposition, also known as DED-LB/M or Laserauftragschweissen. The industrial question is simple:

DfAM and OEMMetal AM route
130 mm drill during Laser Metal Deposition build and coating workflow
A26

Article

From Metal Powder to Functional Drill in 24 Hours: What Rapid LMD Prototyping Can Prove

Exafuse has publicly shown a rapid LMD proof story: a functional drill, described publicly as a "Bombenbohrer," produced from metal powder with an antimagnetic coating in under 24 hours. The useful takeaway is not that every...

Wear and corrosionMetal AM routeQualification
Robotic LMD setup for a 750 mm multi-material nozzle demonstrator
A27

Article

750 mm Water-Cooled Nozzle by Multi-Material LMD: What Thin-Wall Nickel-Alloy Builds Prove

Exafuse has publicly shown a complex 750 mm water-cooled nozzle design manufactured by Laser Metal Deposition with two Ni-based alloys: Inconel 625 for the inner structure and Inconel 718 for the outer structure and cooling ribs.

Wear and corrosionMetal AM routeQualification
130 mm drill cover image for LMD build-and-coat workflow explanation
A30

Article

How to Evaluate LMD Build-and-Coat Workflows: Geometry, Surface Function, and Validation

An LMD build-and-coat workflow is worth evaluating when a metal part needs both geometry creation and a functional surface layer. The route should be reviewed as one manufacturing chain:

Wear and corrosionMetal AM route
Large LMD-manufactured bridge node component
A32

Article

Large Structural LMD for Bridge Components: Lessons from the Duisburg Bridge Project

Large structural Laser Metal Deposition (LMD) is not only a larger print job. It is a full engineering route that connects CAD redesign, deposition strategy, parameter development, monitoring, production planning, finishing and...

Metal AM routeQualificationDfAM and OEM
Valve seat ring after dye test with no visible cracks or pores in the photographed condition
A38

Article

Crack-Risk Control for Hard Co-Based LMD Coatings on Ring Geometries

Hard Co-based LMD coatings on ring geometries need more than a powder selection. Geometry, preheating, layer strategy, powder-feed direction, travel-speed review and dye inspection decide whether a wear coating is technically...

Wear and corrosionQualificationRFQ and buying
Laser Metal Deposition process adding metal to a component
A39

Article

Laser Metal Deposition / DED-LB/M: Industrial Guide for Large Metal Parts, Repair, Cladding, and AI Process Monitoring

This is the central Exafuse hub for industrial buyers comparing Laser Metal Deposition, DED, DED-LB/M, laser cladding, repair, large-part manufacturing, validation and AI-assisted process monitoring.

Metal AM routeRepair vs replaceWear and corrosion

Related topics

Additional public pages that reinforce this direction.

Alongside the core articles, these pages help show how research, monitoring and process understanding connect to industrial application.

Talks and events

Public event profiles and speaker references.

Public event references on this page stay limited to material that is already released for publication, including the linked speaker profile and public event post below.

Case-study involvement

Public where monitoring, route and validation can be explained safely.

Public case-study involvement is framed conservatively. The visible contribution is in monitoring context, process interpretation, route explanation, validation logic and technical storytelling where publication is already approved.

Links

Current public contact and profile paths.

The reliable public paths now run through Exafuse, direct contact and the linked external identity profile.