By the time an error is caught, bench time is already spent
車の製品開発を
短期間で実現
車の製品開発を
短期間で実現
車の製品開発を
短期間で実現
車の製品開発を
短期間で実現
最先端の機械学習を応用し複雑な技術課題を理解しやすくし、
開発スピードを向上させます。
最先端の機械学習を応用し複雑な技術課題を理解しやすくし、
開発スピードを向上させます。
Secondmindアクティブラーニングを使用することで、データに基づいて自動で成立する設計仕様を見つけることが可能になります。多次元の設計空間を可視化し、探索して、設計の早い段階で革新的な設計を特定することができたり、後工程からの手戻りを削減し、時間を節約します。
メリット
開発時間を半分にし、システム設計のためのSecondmindを使用して、シミュレーションを最大80%削減します。
Exhaustive testing, regardless of value
Without the ability to adapt to what's already been learned, testing continues across the full space - even where the outcome is already clear.
Bench time lost that can't be recovered
Test cells cost $100s an hour to run. Every hour spent on a test that doesn't add value is money the program can't get back.
Costly rework, at the worst possible time
Poorly targeted measurements can produce a model that doesn't hold up when tested - forcing engineers back to the bench when the cost of rework is highest.
Exhaustive testing, regardless of value
Without the ability to adapt to what's already been learned, testing continues across the full space — even where the outcome is already clear.
Bench time lost that can't be recovered
Test cells cost $100s an hour to run. Every hour spent on a test that doesn't add value is money the program can't get back.
Costly rework, at the worst possible time
Poorly targeted measurements can produce a model that doesn't hold up when tested — forcing engineers back to the bench when the cost of rework is highest.
メリット
開発時間を半分にし、システム設計のためのSecondmindを使用して、シミュレーションを最大80%削減します。
Every test earns its place
Secondmind identifies the most informative measurement to take at each step - no upfront planning required
High-precision surrogate models
True system behaviour is separated from measurement noise, with quantified confidence at every point.
Better calibration decisions, consistently
Engineers can understand, explore, and act on the result directly, regardless of who is running the program.
Every test earns
its place
Secondmind identifies the most informative measurement to take at each step - no upfront planning required.
High-precision surrogate models
True system behaviour is separated from measurement noise, with quantified confidence at every point.
Better calibration decisions, consistently
Engineers can understand, explore, and act on the result directly, regardless of who is running the program.
主要な機能
主要な機能
Fewer tests, a map you can trust - here are some of the features that make it possible.
Fewer tests, a map you can trust -
here are some of the features that
make it possible.
Explore
Explore
主要な機能
Secondmind gives engineers different features to map the design space, compare candidates,
and commit with confidence.
成立設計仕様
成立設計仕様
要件を満たすことが検証された設計仕様を開発初期段階に選ぶことで、コストのかかり開発手戻りを防ぎます。高額なやり直しのリスクを排除します。
要件を満たすことが検証された設計仕様を開発初期段階に選ぶことで、コストのかかり開発手戻りを防ぎます。高額なやり直しのリスクを排除します。
成立する設計仕様を指導的に見つけ出す。
要件が成立する設計仕様を開発検討チームに渡してください。
設計の修正・手戻りを最小限に抑え、時間とコストを抑えます
Convergence Indicator
Know when to stop testing.
Real-time signal of model quality and its rate of improvement, updated after every test.
Flags the point where the model is accurate enough to trust - avoiding both premature stopping and over-testing.
Gives engineers a clear data-backed signal for when a result is ready to act on.
最先端の可視化
直感的な可視化により、データが持つ重要な意味をを理解してください。
複雑で多次元の設計空間だとしても、安心して探索してください。
設計値でのスライス、設計空間の2D投影、パラレルコーディネート図など、さまざまな手法でデータを可視化します。
効率的にトレードオフを分析して、最適な解決策をより早く選択します。
最先端の可視化
直感的な可視化により、データが持つ重要な意味をを理解してください。
複雑で多次元の設計空間だとしても、安心して探索してください。
設計値でのスライス、設計空間の2D投影、パラレルコーディネート図など、さまざまな手法でデータを可視化します。
効率的にトレードオフを分析して、最適な解決策をより早く選択します。
Map Smoothing
Meet real-world driveability requirements.
Applies automatic or manual smoothing to balance peak performance against smoothness.e.
Makes the performance/smoothness trade-off explicit, rather than leaving it for downstream teams to resolve.
Produces a map ready for ECU implementation.
Proven results
Delivers faster time-to-market, lower development costs, and better-performing results.
Faster time -to-market
59.7%
reduction in engineering hours on a production ICE calibration program at Mazda.
Lower development costs
65%
reduction in test cell occupancy time - from 4 weeks to 1.5 weeks — in a benchmark against an incumbent calibration tool at a Tier 1 supplier.
Better quality and performance
2.3x
more of the calibration space explored in that same program, without adding a single day to the schedule.
Proven results
Delivers faster time-to-market, lower development costs, and better-performing results.
Faster time -to-market
59.7%
reduction in engineering hours on a production ICE calibration program at Mazda.
Lower development costs
65%
reduction in test cell occupancy time - from 4 weeks to 1.5 weeks — in a benchmark against an incumbent calibration tool at a Tier 1 supplier.
Better quality and performance
2.3x
more of the calibration space explored in that same program, without adding a single day to the schedule.
The Secondmind advantage
What makes this possible - working in one system.
No bench time wasted in the wrong place
Secondmind Active Learning runs as an automated DoE loop - acquiring data, analyzing it, updating the model, and directing the next test to where uncertainty is highest - rather than following a fixed plan.
Signal, not noise.
Measurement noise is automatically separated from true system behaviour, so the model reflects what the system is actually doing - not measurement error.
A result any engineer can verify
The complexity is handled, so any engineer can understand a result, explore it, and act on it directly.
The Secondmind advantage
What makes this possible - working in one system.
No bench time wasted in the wrong place
Secondmind Active Learning runs as an automated DoE loop - acquiring data, analyzing it, updating the model, and directing the next test to where uncertainty is highest - rather than following a fixed plan.
Signal, not noise.
Measurement noise is automatically separated from true system behaviour, so the model reflects what the system is actually doing - not measurement error.
A result any engineer can verify
The complexity is handled, so any engineer can understand a result, explore it, and act on it directly.
メリット
開発時間を半分にし、システム設計のためのSecondmindを使用して、シミュレーションを最大80%削減します。
Faster time -to-market
59.7%
reduction in engineering hours on a production ICE calibration program at Mazda.
Lower development costs
65%
reduction in test cell occupancy time — from 4 weeks to 1.5 weeks — in a benchmark against an incumbent calibration tool at a Tier 1 supplier.
Better quality and performance
2.3x
more of the calibration space explored in that same program, without adding a single day to the schedule.
メリット
開発時間を半分にし、システム設計のためのSecondmindを使用して、シミュレーションを最大80%削減します。
Faster time -to-market
59.7%
reduction in engineering hours on a production ICE calibration program at Mazda.
Lower development costs
65%
reduction in test cell occupancy time — from 4 weeks to 1.5 weeks — in a benchmark against an incumbent calibration tool at a Tier 1 supplier.
Better quality and performance
2.3x
more of the calibration space explored in that same program, without adding a single day to the schedule.
メリット
開発時間を半分にし、システム設計のためのSecondmindを使用して、シミュレーションを最大80%削減します。
Faster time -to-market
59.7%
reduction in engineering hours on a production ICE calibration program at Mazda.
Lower development costs
65%
reduction in test cell occupancy time — from 4 weeks to 1.5 weeks — in a benchmark against an incumbent calibration tool at a Tier 1 supplier.
Better quality and performance
2.3x
more of the calibration space explored in that same program, without adding a single day to the schedule.
The Secondmind advantage
What makes this possible - working in one system.
No bench time wasted in the wrong place
Secondmind Active Learning runs an automated DoE loop — acquiring data, analyzing it, updating the model, and directing the next test to where uncertainty is highest — rather than following a fixed plan.
Signal,
not noise.
Measurement noise is automatically separated from true system behaviour, so the model reflects what the system is actually doing — not measurement error.
A result any engineer can verify
The complexity is handled, so any engineer can understand a result, explore it, and act on it directly.
サステナビリティの追求
私たちは、CO2排出量を減らし、環境に優しい影響を与えることを目指しています。そのために、効率的な車両開発を通じて、自動車のネットゼロに向けた未来へのシフトを加速させたいと考えています。
サステナビリティの追求
私たちは、CO2排出量を減らし、環境に優しい影響を与えることを目指しています。そのために、効率的な車両開発を通じて、自動車のネットゼロに向けた未来へのシフトを加速させたいと考えています。
Example use cases
Complex products, dozens of interacting variables, measurable impact.
E-motor calibration
Cut calibration time by 85% and test bench usage to a fifth of previous methods, with 80% fewer motor measurements.
ICE calibration
Mazda achieved a 59.7% reduction in engineering-hours to create high-precison calibration maps.
Diesel calibration
Reached minimum fuel consumption within Euro 6 limits in under 400 test points, vs. 2,500+ conventionally required.
E-powertrain calibration
Removed lengthy soaking periods needed to stabilize rotor temperature, cutting testbed occupancy by 80% - without compromising torque accuracy.
Example use cases
Complex products, dozens of interacting variables, measurable impact.
E-motor calibration
Cut calibration time by 85% and test bench usage to a fifth of previous methods, with 80% fewer motor measurements.
ICE calibration
Mazda achieved a 59.7% reduction in engineering-hours to create high-precison calibration maps.
Diesel calibration
Reached minimum fuel consumption within Euro 6 limits in under 400 test points, vs. 2,500+ conventionally required.
E-powertrain calibration
Removed lengthy soaking periods needed to stabilize rotor temperature, cutting testbed occupancy by 80% - without compromising torque accuracy.
Example use cases
Complex products, dozens of interacting variables, measurable impact.
E-motor calibration
Cut calibration time by 85% and test bench usage to a fifth of previous methods, with 80% fewer motor measurements.
ICE calibration
Mazda achieved a 59.7% reduction in engineering-hours to create high-precison calibration maps.
Diesel calibration
Reached minimum fuel consumption within Euro 6 limits in under 400 test points, vs. 2,500+ conventionally required.
E-powertrain calibration
Removed lengthy soaking periods needed to stabilize rotor temperature, cutting testbed occupancy by 80% - without compromising torque accuracy.
E-motor calibration
Cut calibration time by 85% and test bench usage to a fifth of previous methods, with 80% fewer motor measurements.
ICE calibration
Mazda achieved a 59.7% reduction in engineering-hours to create high-precison calibration maps.
Diesel calibration
Reached minimum fuel consumption within Euro 6 limits in under 400 test points, vs. 2,500+ conventionally required.
E-powertrain calibration
Removed lengthy soaking periods needed to stabilize rotor temperature, cutting testbed occupancy by 80% - without compromising torque accuracy.
Example use cases
Complex products, dozens of interacting variables, measurable impact.
E-motor calibration
Cut calibration time by 85% and test bench usage to a fifth of previous methods, with 80% fewer motor measurements.
ICE calibration
Mazda achieved a 59.7% reduction in engineering-hours to create high-precison calibration maps.
Diesel calibration
Reached minimum fuel consumption within Euro 6 limits in under 400 test points, vs. 2,500+ conventionally required.
E-powertrain calibration
Removed lengthy soaking periods needed to stabilize rotor temperature, cutting testbed occupancy by 80% - without compromising torque accuracy.
E-motor calibration
Cut calibration time by 85% and test bench usage to a fifth of previous methods, with 80% fewer motor measurements.
ICE calibration
Mazda achieved a 59.7% reduction in engineering-hours to create high-precison calibration maps.
Diesel calibration
Reached minimum fuel consumption within Euro 6 limits in under 400 test points, vs. 2,500+ conventionally required.
E-powertrain calibration
Removed lengthy soaking periods needed to stabilize rotor temperature, cutting testbed occupancy by 80% - without compromising torque accuracy.
サステナビリティの追求
私たちは、CO2排出量を減らし、環境に優しい影響を与えることを目指しています。そのために、効率的な車両開発を通じて、自動車のネットゼロに向けた未来へのシフトを加速させたいと考えています。
サステナビリティの追求
私たちは、CO2排出量を減らし、環境に優しい影響を与えることを目指しています。そのために、効率的な車両開発を通じて、自動車のネットゼロに向けた未来へのシフトを加速させたいと考えています。
サステナビリティの追求
私たちは、CO2排出量を減らし、環境に優しい影響を与えることを目指しています。そのために、効率的な車両開発を通じて、自動車のネットゼロに向けた未来へのシフトを加速させたいと考えています。
資料
急速に進化する業界で常に一歩先を行きませんか。あなたの競争力を維持し、必要な知識と革新的知識を保つために、当社の最新の資料、インサイト、ツールをぜひご活用ください。
資料
急速に進化する業界で常に一歩先を行きませんか。あなたの競争力を維持し、必要な知識と革新的知識を保つために、当社の最新の資料、インサイト、ツールをぜひご活用ください。
資料
急速に進化する業界で常に一歩先を行きませんか。あなたの競争力を維持し、必要な知識と革新的知識を保つために、当社の最新の資料、インサイト、ツールをぜひご活用ください。
資料
急速に進化する業界で常に一歩先を行きませんか。あなたの競争力を維持し、必要な知識と革新的知識を保つために、当社の最新の資料、インサイト、ツールをぜひご活用ください。
