10 | Plant & Works Engineering www.pwemag.co.uk August/September 2026 Insight There is a sentence I hear more and more often in additive manufacturing: “We want to use AI.” It is usually said with good intent. The company wants to reduce scrap, shorten qualification loops, automate analysis, predict quality, optimise parameters, or make production more autonomous. These are valid goals. In fact, they are exactly the kind of ambitions AM needs if it is going to mature into a truly scalable production technology. But there is an uncomfortable question that must be asked before any serious AI discussion begins. What exactly is the AI supposed to learn from? Because if the answer is “machine logs in one folder, powder records in Excel, inspection PDFs in another folder, and a few tribal assumptions held by two experienced engineers,” then the AI project is not ambitious. It is premature. This may sound provocative, but the AM industry does not have an AI problem. It has a data credibility problem. Too many organisations are trying to talk about artificial intelligence before they have created the conditions in which intelligence (artificial or human) can operate reliably. AI does not create truth. It amplifies the quality of the data environment it is given. If that environment is fragmented, inconsistent, poorly labelled, or disconnected from process context, AI will not solve the problem. It will industrialise the confusion. AM generates data, not usable evidence One of the myths in additive manufacturing is that because the process is digital, the data foundation is automatically strong. This is not true. AM certainly generates data. Machines produce logs. Powder is tracked. Parameter sets are stored. Sensors record signals. CT scans, CMMs, and mechanical tests generate reports. Postprocessing steps create additional evidence. Operators sign off work instructions. Maintenance activities are documented somewhere. Building the data foundation for credible AI in additive manufacturing As artificial intelligence gains traction in additive manufacturing, Tim Wischeropp, CEO of amsight, explains why connected, contextualised quality data is essential for turning AI ambitions into credible production tools. But generating data is not the same as building a usable evidence chain. The real challenge is not whether data exists. The challenge is whether powder history, machine state, build parameters, post-processing, inspection outcomes, and partlevel conformity can be linked in a way that is consistent, queryable and trusted over time. That distinction matters enormously for AI. An algorithm does not simply need “more data.” It needs data with context. It needs to know which powder lot was used, how many reuse cycles were involved, which machine produced the build, whether maintenance had occurred, which parameter revision was active, which post-processing route followed, and which inspection result belongs to which part or coupon. It needs to know what changed, when it changed, and whether that change mattered. Without that connected context, AI becomes pattern recognition without accountability. The danger of “dashboard theatre” The next years in AM will produce a lot of AI theatre. We will see attractive dashboards that claim to detect risk, predict defects, or recommend actions. Some will be valuable. Many will be impressive demonstrations built on weak foundations. The danger is that organisations mistake visual sophistication for process understanding. A coloured warning icon is not quality management. A predicted anomaly is not rootcause analysis. A machine learning model that cannot explain the evidence chain behind its recommendation will struggle to survive the realities of regulated production. This is especially important in sectors such as space, aerospace, defence, energy, semiconductor, and medtech. In these environments, quality decisions are not just technical decisions, they are accountable decisions. If an AI system recommends that a build is acceptable, a customer or auditor may eventually ask why. If the answer cannot be traced back to controlled data, validated assumptions, and documented process evidence, the recommendation is not credible. AI must therefore be built on an AM quality data backbone, not scattered files. What a quality data backbone actually means It is not a generic data lake, a document repository, or an MES with extra attachments.. For AM, a quality data backbone is a structured system that connects the full production evidence chain around the part and process. It captures and normalises data from machines, powders, parameters, post-processing steps, Figure 1: Connected context makes data usable for traceability, SPC, root-cause analysis, and AI.
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