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A Perspective of the Manufacturing Future: Cutting Tools

In the first post of a GE series, read how one analyst sees how the industrial Internet will impact manufacturing. First, see how cutting tools will be connected and monitored in an IIoT world.

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Some time ago, I made a vision document and a roadmap for transforming a machining line in a 50+-year-old complex-discrete manufacturing plant. When I started, I approached multiple topics including part quality, cutting tool management, manufacturing asset performance, associate/operator performance, and material/production tracking. I approached the vision from the standpoint of what is technically possible and how does it work in a utopian state.

In this post, I want to walk through the portion of that vision pertaining to managing cutting tools. This vision includes automation, lots of data, and artificial intelligence. Manufacturers who conduct a lot of machining know that the cost of productivity of a machining operation is inextricably connected to the selection of the cutting tool and the process for managing them. In the utopian future, the manufacturing intelligence system will automatically control how cutting tools are used and automatically optimize the cost of machining operations.

Current State

The plant itself may be 50+ years old, but the equipment inside has been upgraded and replaced over the years. There may be some machining centers that are less than one year old and some that are 20+ years old. The production associates may also have been with the company for less than one year or over 20 years. It’s common practice in these types of processes for a production associate to operate one to four Computer Numerical Control (CNC) machining centers. In a discrete manufacturing plant, one operation is often conducted on a single part in a single machining center. The part is then passed to the next operation. In a complex-discrete manufacturing plant, it would be possible that the next part in the schedule is not the same as the part which just finished and the process flow may not be the same for each subsequent part. The associates tend the parts in the machining centers, tend the cutting tools, perform quality checks, deburr the parts, and most likely tend to housekeeping and some preventative maintenance of the manufacturing equipment.


In the ideal future, there will be a significant amount of automation to augment the manual tasks of the associate. The automation will allow them to work more safely and productively.

Regarding the cutting tools, there will be robotic arms removing and replacing cutting tool assemblies in the machining centers. If tool setting is centralized, then there will be Automated Guided Vehicles (AGV) moving carts of new and worn cutting tools between the loading area for the machining center and the centralized setting station. 

The cutting tool setting station will have another robotic arm which is removing the worn end-mills, inserts (admittedly, inserts will be exceptionally tricky), drill bits, etc. from the cutter bodies. In the case of an end-mill, the robotic arm will pass the worn cutting tool in front of a vision system for the cutting edge to be inspected for wear-type and amount of wear. The analysis data from the vision system will be saved to a centralized database for that cutting tool. The cutting tool will be discarded and a “fresh” (new or reground) cutting tool will be selected and inserted into the cutting body. The AGV will return the fresh cutting tool assemblies to the machining center for those tools to go through the operation.

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Copious amounts of data are already being produced during manufacturing processes but most of it exists in disparate systems. Some exist in the record of manufacturing execution, some on the control of the machining center, some in the quality database (or notebook), cutting tool inventory databases, etc. The crux of this issue is linking all of the data sources together and creating a contextual relationship between each of the data items.

In the future, the actual cutting life of each tool will be captured automatically by utilizing automatic-identification technology to identify each unique cutting tool. Complete cutting tool genealogy will be pieced together from the cutting tool inventory database and the product database. This actual tool-life and tool genealogy will provide unprecedented visibility to the cost of machining individual features on any given part and allow optimization of those machining costs on nearly a part-to-part basis. Sensors on the machining center will provide data to assess variations in the cutting process and the tool life at any point during the machining operation. The quality data for a particular feature will be linked to individual cutting tools and the wear amount measured by the vision system at the automated setting station. This will provide correlations needed to determine where appropriate wear limits need to be set.

Artificial Intelligence

Artificial Intelligence (AI) has revolutionized the way we interact with electronics and our ability to leverage technology to be more productive. AI helped the US Postal Service automatically read and process handwritten addresses on packages as far back as the late 1990’s. Google uses AI in nearly everything it does. Google search is so powerful because it learns and improves every time anyone conducts a search. Image and speech recognition has become surprisingly accurate thanks to AI and the abundance of digital content on the internet. 

Soon, the real tool life data from the machining operation will be used as feedback to the AI system and it will automatically adjust machining process parameters to optimize the machining costs of each individual part produced while maintaining part quality. If a cutting tool vendor develops a new cutting tool, it will be introduced into the system and will be monitored throughout the process. If the machining costs, that are automatically calculated, are calculated to be higher than the current process, then the new tool will be rejected by system. If the machining costs are lower, the plant’s system will automatically order the new tool from the supplier’s system. If the plant’s demand changes, the manufacturing intelligence system will automatically adjust to drive the lowest total landed cost while meeting customer delivery dates and quality standards.

This is a glimpse into one manufacturing geek’s view of how cutting tool management in a machining operation will be impacted by technology. It is important to note that everything described is technically feasible.  In fact, most aspects have been demonstrated or are being leveraged by existing manufacturing operations. I’m waiting with bated breath to see the culmination of all of these concepts into one cohesive system. 

The IoT Zone is brought to you in partnership with GE Digital.  Discover how IoT developers are using Predix to disrupt traditional industrial development models.


Published at DZone with permission of Andy Henderson, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

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