In machining terms, the manufacturing industry’s continual quest to produce correctly finished workpieces at a certain cost in a certain time has reached the end of the line. Unless a breakthrough tooling solution appears, traditional approaches focused solely on boosting metal removal rates will at best squeeze out a few percentage points of increased output. By Patrick de Vos, Corporate Technical Education Manager at Seco Tools.

Significant future improvements in metal-cutting productivity, quality and reliability will come from a data-driven fourth revolution in manufacturing technology – the latest stage of a lengthy evolution. The first manufacturing revolution involved the move from home-based crafting activities to production in factories with centralised energy sources powering manufacturing machinery. Mechanical shafts and belts distributed power from water wheels or steam. The more convenient, efficient use of electrical energy followed.

The first factories turned out products one by one. In the second revolution, output expanded to mass production. The development of integrated systems such as assembly and transfer lines and automation expedited high-volume production of identical parts. The third revolution in manufacturing technology came with the introduction of numerical machine control, and later computer-based control and automation, increasing accuracy and flexibility, and facilitating lower-volume, higher-part-variety manufacturing.

Now manufacturing is in the midst of a fourth revolution, described as Industry 4.0, which integrates present-day data acquisition, storage and sharing technologies. Networked cyber-physical systems analyse ongoing operations, gather and compare data, and route the information to a central server or cloud to compare it with established machining models, using the results to direct parameter adjustments that optimise machining processes.

Early monitor and control systems

The concept of data-directed manufacturing has been around for some time. In the 1980s, metalworking researchers worked to create adaptive tool monitoring and control systems intended to measure cutting conditions, compare the data to set process standards, then adjust machining parameters to stabilise operations and minimise occurrence of unforeseen machining events.

The systems employed sensors and probes to measure factors such as cutting forces, power, torque, temperatures, surface roughness and acoustic emissions. Unfortunately, sensor technology at that time lacked the speed and accuracy to be fully effective, and computers lacked the processing speed and memory needed to handle large amounts of data in real time. Additionally, advanced data acquisition and management technology was extremely expensive.

Those shortcomings made in-process parameter adjustment nearly impossible. The result was a binary, black-and-white situation. If collected data exceeded set maximums, the machining process simply is stopped. Maximums were set with insufficient knowledge and insight into cutting processes. As well as lacking adequate processing technology, a key concept was missing: that most of the physical phenomena in machining processes – temperature, forces, loads – are not static conditions but dynamic ones that constantly change.

For example, cutting forces in a certain operation may average 1,000Nm, but about 50% of the time, those forces are above 1,000Nm, and below that level during the remaining time. If the system’s cut-off level is set at 1,000Nm, the process stops because the forces appear too high. Now, nearly 40 years later, sensor and computer technology is far more accurate, faster and less expensive. Manufacturing process research itself is four decades richer in experience and provides greater insights to the key elements of the process.

Collect and connect the elements

It is important to understand the roles of different process elements. There are, in fact, more than 80 measurable elements that influence machining operations. It is crucial that all the elements be collected, connected and interactive. If an element is unaccounted for, the effects can be unexpected and uncontrollable.

After collection and analysis, data must be prioritised regarding each element’s impact on the process. It is clear that tooling has very significant effects. A collection of production tools works together in metal-cutting: machine tool, CAM system, cutting tool, fixturing and clamping, and coolant, and in Industry 4.0, sensors and data retrieval and transmission systems.

At the core of metal cutting is the interaction of the cutting tool with the workpiece. However, in the traditional approach to developing machining processes, the cutting tool is often the last consideration. When planning to produce a workpiece, users typically first choose the machine tool, then fixturing, the cooling system and other equipment, and finally the cutting tool. This results in a situation where a cutting tool must make up for less optimum choices of other elements.

For example, if the selected machine tool is unstable, a cutting tool that generates lower cutting forces will be needed to compensate for the lack of stability. However, that tool may fall short when it comes to maximising productivity in the particular workpiece material being machined. In that case, the end effect of choosing the cutting tool last is a subpar system that operates below its full potential.

Fortunately, many individuals in the manufacturing industry now realise it is more appropriate to work in reverse. Shops should first select the cutting tool after considering the final product’s shape and features, its workpiece material and the required quality. The cutting tool – specific material and geometry – should provide the most productivity and meet the specific requirements of the process. Then the choices of the other process elements can focus on creating an environment in which that cutting tool will function at its full capacity.

Balanced operations

After a shop chooses the elements of the machining process, interaction of the elements must be balanced to achieve maximum productivity and minimum costs, and there are several persistent manufacturing issues involved in machining output and expense. Obvious process factors include tool performance along with tool and machining costs. Costs that are not so obvious include those resulting from unreliable machining processes that produce poor quality or rejected parts, or contributing to unforeseen downtime.

Although planned activities such as programming and maintenance are part of non-machining time, other factors, such as operator errors, broken tools, damaged workpieces and system problems needlessly increase process times and expense. Cutting tools represent a minor percentage of lost time, as do workpiece material and process anomalies. The effects of time expenditures generated by personnel and systems are far greater.

Industry 4.0 highlights digital data capture, the internet and cloud-based storage, but those components are only part of the solution. In the end, the collected data must be analysed and a physical model or map constructed that defines the process in question. In cyber-physical systems, collected data is compared to the map, and the system generates feedback to execute process modifications that will produce the desired results. Process control is accomplished not by a human but by the computer analysing and comparing the data against the model instantaneously.

Accordingly, the model stored in the cloud must accurately describe the elements of the process. Constructing such a model requires full understanding of the operations. Unfortunately, machining presents a reality that is difficult to exactly describe. For example, a model must recognise the dynamic properties of the workpiece material because changes in hardness result in varying cutting forces. But it is impossible to measure the hardness of every workpiece. And in some cases, workpiece hardness might be 10% higher than the material’s nominal hardness, leading to cutting forces that are 10% higher as well.

Maintain human control

A model that learns during operations and modifies itself to provide an increasingly accurate description of the process would be a partial solution to this process control dilemma, but the technology has yet to advance to that point. As a result, manufacturing engineers must know how a model was conceived and built to determine if its basis for management of the cutting process is valid. Then, if the parameters chosen via the model’s interaction with cutting data are questionable, the engineer will know the basis on which choices are made and can decide if they should be overruled. The cyber-physical system may control the metal-cutting process, but it is the manufacturing engineer who maintains control over that system.

By consulting decades of field and research experience, Seco builds and provides extremely accurate process models. These models are not closed box in form, but provide both in and out capability for process direction, because human thought, experience and perspective are essential to the final success of the new Industry 4.0 manufacturing revolution.