Advanced Manufacturing Technologist, Irish Manufacturing Research
Buzzwords like AI, digital manufacture and advanced automation are often familiar, off-the-shelf technologies waiting to be picked up and used.
‘Digital manufacturing pipeline’ describes the steps involved in the generation, manipulation and conversion of digital part-data when making a physical part. For every manufacturing step, there is corresponding data from a digital operation (ie: .PRT with Design, .STP/coordinate code for machining and inspection programming, .CSV reporting measurement). Automating these steps offers increased productivity, self-correcting machining and, ultimately, lights-out manufacture.
You can create your own digital pipeline
Most design software now allows some automation. Examples include automatic CAD and drawing generation by parametric design or generative design of a new shape from user requirements and manufacturing constraints. Some extend into the manufacturing process by allowing for rules-based automation of tool pathing and inspection programming.
Alternatively, various, free tools are available to create rules-based scripts that run and operate different software packages, spread across multiple stages of a digital pipeline.
Expect AI solutions for process management
to become increasingly common
in the next 12–24 months.
Starting small and having fun builds skills
Writing scripts to automate end-to-end manufacturing is a big idea; but it breaks down into manageable chunks: (1) Draw a block diagram of the pipeline. (2) ID inputs/outputs of each stage. (3) Automate processes within each software. (4) Trial different methods to run between stages/software packages.
By breaking the problem down into chunks, starting with a (very) simple proof of concept and engaging multiple skill sets, it might be surprising how far a team can get in automating a digital manufacturing pipeline by using existing software, hardware and staff.
AI allows a robust automated process
Most automation in manufacturing is currently rules-based and user scripted. However, Artificial Intelligence (AI) has been overtaking rules-based programming in other engineering activities such as webpage generation. This is because AI programmes are much more effective at self-correcting when faced with ‘outside the box’ challenges. Expect AI solutions for process management to become increasingly common in the next 12–24 months.
Automating design and manufacture offers significant opportunities for freeing up personnel and equipment resources, increasing productivity and decreasing scrap. Building the skills in-house to apply these methods at will using the resources to hand increases this competitive edge. Having these systems in place to incorporate AI and Machine Based Reasoning (MBR) allows automation to be much more widely applicable. All of this can be possible by challenging a few interested engineers with a low-cost ‘for fun’ project.