Drilling down-force

Autonomy of blasthole drills is evolving while the level of take up is accelerating ; it is no longer a fringe or just emerging solution , reports Paul Moore

Autonomy of blasthole drills continues to be a major topic of interest , but the autonomy technology is not standing still . At the SME MINEXCHANGE conference and expo in February 2024 in Phoenix , Arizona , IM caught up with Curtis Stacy , Senior Product Manager – Autonomous Drills at Hexagon Mining . He gave a much-anticipated presentation updating attendees on Hexagon Drill Assist .

Stacy told IM : “ We ’ ve approached drill autonomy from a digital operator perspective . Instead of changing the core of the machine , we are using AI to effectively take the operators ' place at the joystick controls . Anything the operator can do in the cab , we can do autonomously .” So what ’ s the commercial upside ? “ It ’ s about performance . The difference is in the way we apply the AI at a control algorithm level which makes it more intuitive and acts more like an expert human operator would rather than being based on set parameters . It ’ s looking at all of the feedbacks at one time . The AI operates in a separate controller with a separate automation processor . The AI generates the control signals which are then sent out to the operator levers which are then digitally operated by mimicking the OEM signals whether those be analogue or CANbus or another signal type .” For now Drill Assist is focused on single pass drilling , but it can go on any make or model of drill . “ I would say it is about as unintrusive and as OEM agnostic in drill automation as you can get . As we don ’ t change the drill there are no knock on effects in terms of parts or maintenance for the mines .” He added that new operator training is significantly reduced , empowering trainees to perform at expert production levels when they first start in operations .
Standard drill automation solutions rely on
Boolean logic for control response . In other words , a parameter is set by an operator or supervisor for each condition that must be monitored by the drill automation system . As an example , algorithms used in existing systems to protect the drill bit from becoming plugged or stuck down the hole require human input to tell the automation system when each condition occurs .
Such technology requires one of two approaches . First , performance biased – an aggressive parameter ( such as plugged air pressure ) is set , allowing the drill to continue to penetrate the ground until a higher setpoint is reached . This approach results in higher instantaneous productivity but puts the machine at risk of getting stuck or plugged , conditions which cost precious operational time to correct .
Second , protection biased – exactly opposite of performance tuning , a conservative parameter is set , resulting in false positives that cost production time spent trying to clear a hole or drill bit that may not actually be plugging up . These solutions can be very complicated and require subject matter expertise to properly tune and optimise in addition to a significant investment in operator training . The lack of expertise typically results in the operator setting the drilling parameters based on a menu , which may or may not be close to the actual conditions . The Hexagon solution derives these parameters autonomously , ensuring optimised drilling regardless of ever-changing conditions encountered .
Hexagon set out to improve the limitations of existing drill automation solutions by incorporating AI directly into the control algorithm . By eliminating the requirement for the operator to ‘ estimate ’ and enter drill site
With its Drill Assist , Hexagon set out to improve the limitations of existing drill automation solutions by incorporating AI directly into the control algorithm
parameters prior to commencing drilling on each hole , new operator training for the drilling process was reduced to just 15 minutes for each new operator .
The Hexagon AI solution performs three key functions - genetic algorithm , decision engine , and data purity / ground hardness . Drilling parameters – rotary RPM and force on the bit are now autonomously controlled via the Genetic AI Algorithm that constantly seeks to improve performance of the drill rig within machine and drill bit limitations . The Genetic AI Algorithm automatically determines when ground conditions are stable enough to allow parameter testing ; once stable ground is detected , the algorithm automatically changes one of the drilling parameters and measures the overall performance .
If the performance is improved , the new parameter becomes the baseline and another test is initiated . If performance is adversely affected , the new parameter is made extinct and another test is initiated . Following the principles of Darwin ’ s theory of evolution , parameter changes that result in loss of performance are superseded by changes that significantly improve performance ; this results in always using the best possible drilling parameters for any given ground condition . Baseline parameters are controlled based on rate of penetration , and all
parameters must operate within machine and drill bit manufacturer limitations . Stacy says that this combination ensures highest possible production whilst providing the optimal machine and consumable life .
International Mining | APRIL 2024