The 21st-century control systems used in precision manufacturing are becoming highly automated, autonomous, and intuitive to address the rapidly changing requirements of modern factories and businesses. However, the majority of the industry still relies on conventional control systems that are beset by limitations. These control systems cannot correctly execute control over changing production conditions as they are instructed to run on predetermined instructions under predictable conditions. They cannot make independent judgments and decisions about manufacturing processes when machines face problems. They lack the ability to autonomously monitor and analyze information related to production procedures and conditions.
Moreover, such control systems cannot handle more than a single process optimization objective at a time. They cannot function properly at a multi-objective optimization level where objectives like high production efficiency, energy efficiency, and low consumable spend need to be considered. Any deviation in the predetermined instructions would make them unresponsive. Further, they still rely on human programmers’ and operators' expertise to perform at the optimum level. So any change in guard with a novice programmer and/or operator results in sub-optimal output in terms of quality and productivity.
Industry 4.0 is radically changing the operational parameters of precision manufacturers as it unlocks the potential to upgrade CNC precision machines to an intelligent level. These machines can learn skills autonomously and adapt to changing instructions and uncertain operational conditions in real-time. They deliver high processing accuracy and efficiency and can self-diagnose and rectify faults. Their intelligent control system (ICS) can use information from process sensors to autonomously adjust process functions, such as cutting speed, feed, and depth. They enable self-calibration, vibration and thermal compensation, voice indication, and remote maintenance activities. Machines with ICS behave and act more and more like humans and provide feedback to machine programmers and operators through self-learning and evolutionary procedures to prompt the need for recommended changes.
Lambda Function is building such an ICS that analyzes and processes CNC controller data which otherwise is lost and is not utilized to its maximum potential. The platform can scrutinize process parameter data, shop floor data, and engineering design data to facilitate real-time decision-making for improving production process efficiency, and support early fault detection and resolution processes. Additionally, the platform can learn and imbibe business processes over time, which helps generate relevant insights into machine effectiveness and staff productivity, thereby helping precision manufacturers garner an ever-increasing degree of control intelligence on their machining shop floor.
Lambda Function's ICS software integrates machining physics and mathematics with advanced analytics and machine learning to identify key insights based on underlying patterns and prescribe appropriate actions to achieve the desired result on the CNC machining shop floor. Vast amounts of unstructured and structured data is carefully pre-processed and cleansed in real-time before getting transformed into insights through a series of advanced analytics to provide actionable recommendations targeting overall equipment effectiveness (OEE) improvement and consumable spend reduction. The combination of real-time advanced analytics and insights effectively pinpoints hidden inefficiencies in NC programming, uncovers key shop risks, and enables efficient cutting tool utilization, ultimately driving up OEE and shop profitability.
While the eventual goal for Lambda Function ICS is to help achieve Level 5 autonomous machining on the shop floor, significant value is being delivered and realized through a step-by-step approach of incrementally increasing the degree of autonomy on the shop floor. The primary source of value creation stems from the ICS’ ability to reduce the variability and unpredictability that drives non-recurring and recurring costs in precision manufacturing production processes today. The integration of machine learning allows Lambda Function's ICS to adapt to the specific machining processes of each customer, continuously learn and self-improve, and scale to support the varying needs of our customers' machine shops and their respective industries. Lambda Function ICS is helping machine shops increase cutting time, reduce risks of quality issues/unplanned downtime, increase throughput, optimize annual spend on cutting tools, and improve staff productivity.