## The Challenge

Pre-existing inline measurement systems utilize uncertain methods of data analysis that include best fitting, machine learning and AI. Unfortunately, while these descriptive marketing terms sound promising, the results that they deliver are numerically uncertain.

One reason that the previously mentioned analysis methods are uncertain is that they all rely on estimations, assumptions, alignments (physical and/or mathematical) and approximations. In many cases, to try and further provide a warm and fuzzy feeling about measurement results to the user, a confidence level may be assigned to a result. Attempting to improve uncertainty by assigning a confidence level is logically flawed, since the confidence level is uncertain and does nothing to change the fact that the result itself is still uncertain and therefore unreliable as it could be incorrect.

Uncertainty also stems from the incapability to discern “expected” from “unexpected” raw data. Environmental influences /circumstances that may influence raw data can lead to variance, distortion and/or anomalies. Since these influences do not change the certainty of the collected numbers/values themselves, a 100% accurate discrimination of what is representative and not representative is required to achieve measurement certainty.

In traditional methods, because the foundation is uncertain, trying to make any corrections or decisions based on the results is ultimately a “guessing game” and typically involves many man-hours, arguments, and ultimately trial and error to resolve issues or deal with false positives / false negatives. Always remember, there is only uncertainty or certainty, nothing in between.

Through the center point and ideal structure (Inner Reference) provided by OI Core Technology, the estimations, assumptions, alignments (physical and/or mathematical) and approximations used by traditional analysis methods become obsolete. Now, any comparisons between nominal and measured or two different measurements can be made via the Inner Reference and center point of the point clouds with numerical certainty.

Certain results allow you to understand the true reality of any process and instantly see root causes for any aspects that are outside of an acceptable range. This saves time, material and money that is currently wasted by using uncertain analysis. There is no longer a need to hide behind phrases like “good enough” or “too accurate”, not understanding the consequences that the guessing game and uncertainty creates.

OI Core Technology applied to digital manufacturing processes empowers any existing inline (and supporting offline) measurement system to upgrade from uncertainty to certainty. ASYS is a customized software product that provides these benefits leading to improved process efficiency/uptime and ultimately saving time and money for users.

## Applications Of Measurement Control

From offline to in-line inspection, ASYS can be used to control point-cloud comparisons and monitor the capability of any 3-dimensional system inspection system.

#### Automated Inline Inspection

Compatible With Any Brand Of Scanner/Sensor

Step By Step Control Of Process Results From Raw Data To Results

#### Point Cloud Comparison

Compare Actual Parts To Actual Parts

Compare Actual Parts To Nominal

Understand Process Stability/Capability With Certainty

#### Offline Inspection

Can Be Used With Offline Measurement Systems (e.g., Laser Tracker)

External/Independent Way To Troubleshoot Issues Stemming From Other Systems

## InoraASYS Benefits

ASYS Software Is Powered By OI Core Technology And Supported By InoraSRS

The sensor is what is used to collect measurement raw data, so it is important to understand it’s real-world capability at any given time. If the sensor capability is below the application requirements, this can be rectified before worrying about any other resulting calculations.

##### Get An Accuracy Statement For The Comparison Of Nominal And Measured

Instead of comparing data in an uncertain way, data is compared holistically using its center point and geometric structure. An overall accuracy statement can then be given to the comparison for further understanding / root cause diagnosis.

##### Numerically Certain, Actionable Results

All the areas that are completely controlled by OI provide simple but reliable result parameters such as Balance Range (Absolute Accuracy), automatic differentiation between Balance Range and unexpected Deviations (noise, outliers, anomalies), and further relevant analysis. This step-by-step and certain method makes any process easy to understand, improve/correct to achieve an ideal state sustainably.

##### Understand Feature Measurement Accuracy

When raw data can be pulled from the inline inspection system, Inora can provide a value for the consistent accuracy (balance range) that the feature was measured with. If the features are poorly determined, focus can be directed to improving that measurement instead of worrying about subsequent results.

## Customer Success Stories

##### Implementation Of ASYS To Supersede Existing Numerically Uncertain Inline Inspection Analysis

InoraASYS Was Implemented By A Global Manufacturer That Was Not Satisfied With The Uncertain Analysis Results Their Previous System Provided And Now Utilize The Same Hardware But Using ASYS Numerically Certain Results For Absolute Corrective Action Indications, Saving Time And Guessing