Manufacturing processes are increasingly complex, so important decisions should be based on facts, not just hunches or even expert opinions. Data-driven manufacturing enables and complements predictive analytics, smart automated equipment maintenance, and more effective human-robot collaboration.


How Data-Driven Manufacturing from Connected Machines Can Enable AI to Refine and Enhance Manufacturing Processes

Accelerated deployment of sensors and wireless technology connecting various pieces of equipment empower manufacturers to collect valuable data outputs that can be used to inform operations in real time and looking ahead as well. Local area networking technology and software are allowing digital transformation and decreasing the economic advantage of offshoring production.

Data is the fuel that feeds and refines artificial intelligence (AI) based on plant historical data and current shop floor production. The parameters of machines and specific parts being manufactured are key sources of that data. A company just needs the right technology stack and staff with the skills to manage its integration with legacy information systems. Then AI can improve real time visibility into the functioning and performance of all types of machines and even facilitate coordination among them. Manufacturing throughput can be optimized in this scenario.

The design files of a product part containing instructions for machining are usually stored in Computer Aided Design (CAD) formats. Their formats vary based on the software used to create the drawings. Conversely, a neutral CAD file format is the STEP format.

Kalpakjian, (2014). “Manufacturing Engineering and Technology”) defined specifications of a machine as parameters that describe the machine’s capacity. When we talk about machine or manufacturing capacity parameters, we refer to the characteristics of that specific machine that determine its ability to fabricate a specific part.

A machines capacity and performance vary based on brand, machine type, and size. Understanding this variability will help in selecting and utilizing the right manufacturing facility for making that particular part. Sustainment defines machine capacity as: The specifications of a machine, which are inherently relevant to the manufacturing processes that define the ability for performing machining to manufacture the part according to given specifications.”

The type of machine has an influence on its capacity parameters. Turning and milling operations are the most basic subtractive processes that can be used to shape raw material into a useful part. (By contrast 3-D printing uses additive processes.) These two subtractive processes are often performed using two different machines: lathe machines and milling machines.

The third type of machine is the machining center, a modern machine that performs operations of both lathes and mills, but within a single machine. Machining centers work with multiple axes and orientations of the part, depending on the specific needs of the manufacturing process. Machining centers are important when there is a demand for making complex parts with minimal setup change.

Data-Driven Manufacturing Boosts Manufacturing Processes | Sustainment


Lathes are used primarily for turning operations, which are commonly performed on a bar material that rotates on the same axis while being held by a chuck. Turning operations on the lathe expand into a series of other operations that shape the raw material into a final product. These operations include: facing, cutting, boring, drilling, parting, threading, knurling, straight turning, taper turning, profiling, etc. All lathes should be able to perform all these operations on a part.

There are three main capacity parameters of a lathe:

1. The maximum radii of the workpiece that can be accommodated

2. The maximum distance between the headstock and tailstock centers

3. The length of the bed

These three parameters dictate the dimensional space available in the machine to carry out the turning process. The dimensional data collected from a CAD file can be matched with the specifications of the lathe machines.



The milling machine, first built in 1820, has evolved to become the CNC mills that we know today. Milling machines have diverse capabilities that overlap with lathes. Even though a milling machine can achieve results similar to those of a lathe, milling is mostly used to fabricate parts that are not cylindrical in nature.

As with turning, milling has many derivatives that vary according to the orientation and travel of the tool during the cut. Some of these operations are peripheral milling, slab milling, face milling, end milling, etc. Milling machines can work with the spindle in the vertical or horizontal direction. Moreover, the tool can move on multiple axes with respect to the part and, depending on the model, some machines can perform 5-axis machining to fabricate more geometrically challenging parts.

In terms of capacity parameters, the axis travel for the mill is like the maximum distance between tail stock and headstock in a lathe. Power and maximum speed are important as well. Milling machines also have capacity parameters such as a cutting speed of 30 to 3,000 m/min, depth of cut between 1 to 8 mm, and a feed rate of 0.1 to 0.5 mm/tooth. Akin to lathes, larger capacity mills are used for special applications like high-speed end milling.


Machining Centers

Machine centers are advanced computer-controlled machine tools capable of performing a variety of machining operations on different surfaces as well as in different orientations of a workpiece. This can all be achieved by removing the feed stock from its work-holding device or fixture. In other words, the machining operation is brought to the workpiece. This type of machine is the result of advances in automation and CNC machining.

Machining centers have capacity parameters similar to those of milling machines and lathes. Typical power goes up to 75 kW, spindle speed ranges between 4,000 and 75,000 rpm, and pallets can hold up to 7,000 kg (15,000 lb). Pallets are unique to these machines; they can work on several parts at a time or on larger sized parts. As with some milling machines, machining centers can operate on different axes. Their capacity parameters include travel dimensions and others related to the specific axis.

All three types of machines are sources of data that can be matched with design data instructions for a part in a CAD file. At the same time, if the machines are equipped with low-cost sensors and connectivity, they can generate valuable data streams during the fabrication process that make development of AI possible. AI can then be applied to refine and enhance the process, optimizing throughput and productivity.

Implementation of AI is perfectly suited to the precision and accuracy required in today’s advanced manufacturing processes. It can enable predictive analytics, smart automated maintenance of equipment, and more effective human-robot collaboration.

If good shop floor data is being collected for analysis, AI can provide actionable insights on it much faster than human investigation and calculation could. It can also expose timing bottlenecks and other unknowns, which can reveal hidden opportunities to increase production yields. All of this fine-tuning makes the company’s local American manufacturing operation more competitive.