Systems and method relating to machining parts include a CNC system including CNC machining tools, and a computer including a processor and a computer-readable medium, wherein the computer-readable medium encodes instructions of a single NC program that, when run on the processor, causes the computer to control a selected CNC machining tool to perform operations including alternating between (i) moving the selected CNC machining tool along a semi-finishing toolpath segment using a first set of spindle speed and feed rate values to remove a next portion of rough stock material in a next region of a part being manufactured, and (ii) moving the selected CNC machining tool along a finishing toolpath segment to remove a semi-finishing thickness portion of the part in the next region, wherein the first set of spindle speed and feed rate values are different from the second set of spindle speed and feed rate values.

A system and methods for multivariant learning and optimization repeatedly generate self-organized experimental units (SOEUs) based on the one or more assumptions for a randomized multivariate comparison of process decisions to be provided to users of a system. The SOEUs are injected into the system to generate quantified inferences about the process decisions. Responsive to injecting the SOEUs, at least one confidence interval is identified within the quantified inferences, and the SOEUs are iteratively modified based on the at least one confidence interval to identify at least one causal interaction of the process decisions within the system. The causal interaction can be used for testing, diagnosis, and optimization of the system performance.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes obtaining data specifying baseline probability distributions for each of a plurality of controllable elements; maintaining a causal model; repeatedly performing the following: selecting control settings for the environment based on the causal model and values for a particular internal parameter of the control system that are sampled from a range of possible values; selecting control settings for the environment based on the baseline probability distributions; monitoring environment responses to the control settings selected based on the causal model and the control settings selected based on the baseline probability distributions; determining, for each of the possible values, a measure of a difference between a current system performance and a baseline system performance; and updating how frequently each of the possible values is sampled.

Automation system with computerized numerical control includes at least two data processing levels, particularly a data block preparation level and a data block processing level, and a clock time-registering unit assigned to the particular data processing levels for registering clock times of subsystems in the particular data processing levels, and a unit for comparing the clock timesand relating them to each other in terms of timeregistered for the particular data processing levels.

A manufacturing process for physical components can be optimized using some techniques described herein. For example, a system can access a group of models, where each model corresponds to a respective type of production location. The system can select a model, from the group of models, that corresponds to a particular type of production location selected by a user. The system can select a particular type of production equipment based on the selected model. The system can then execute one or more computing operations to facilitate deployment of the particular type of production equipment at the particular type of production location. This can help ensure that appropriately sized production equipment is installed at the production location.

A method of optimizing a plurality of control signals used in operating a vehicle is described. The operation has a plurality of associated measurable parameters. The method includes: for each control signal, selecting a plurality of potential optimum values from a predetermined set; operating the vehicle in at least a first sequence of operation iterations, where for each pair of sequential first and second operation iterations in the first sequence of operation iterations, the potential optimum value of one control signal in the first operation iteration is replaced in the second operation iteration with a next potential optimum value of the control signal, while the potential optimum values of the remaining control signals are maintained; for each operation iteration, measuring each parameter in the plurality of measurable parameters; and generating confidence intervals for the control signals to determine causal relationships between the control signals and the measurable parameters.

A system and methods for multivariant learning and optimization repeatedly generate self-organized experimental units (SOEUs) based on the one or more assumptions for a randomized multivariate comparison of process decisions to be provided to users of a system. The SOEUs are injected into the system to generate quantified inferences about the process decisions. Responsive to injecting the SOEUs, at least one confidence interval is identified within the quantified inferences, and the SOEUs are iteratively modified based on the at least one confidence interval to identify at least one causal interaction of the process decisions within the system. The causal interaction can be used for testing, diagnosis, and optimization of the system performance.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes obtaining data specifying baseline probability distributions for each of a plurality of controllable elements; maintaining a causal model; repeatedly performing the following: selecting control settings for the environment based on the causal model and values for a particular internal parameter of the control system that are sampled from a range of possible values; selecting control settings for the environment based on the baseline probability distributions; monitoring environment responses to the control settings selected based on the causal model and the control settings selected based on the baseline probability distributions; determining, for each of the possible values, a measure of a difference between a current system performance and a baseline system performance; and updating how frequently each of the possible values is sampled.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing a manufacturing process. In one aspect, the method comprises repeatedly performing the following: i) selecting a configuration of control settings for a manufacturing process, based on a causal model that measures causal relationships between control settings and a measure of a success of the manufacturing process; ii) determining the measure of the success of the manufacturing process using the configuration of control settings; and iii) adjusting, based on the measure of the success of the manufacturing process using the configuration of control settings, the causal model.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes identifying a procedural instance; determining a temporal extent for the procedural instance based on temporal extent parameters for the one or more entities in the procedural instance; selecting control settings for the procedural instance; monitoring environment responses to the control settings that are received for the one or more entities; determining which of the environment responses to attribute to the procedural instance in a causal model; and adjusting, based at least in part on the environment responses that are attributed to the procedural instance, the temporal extent parameters for the one or more entities.